Please welcome Elastic's VP of Investor Relations, Nikolay Beliov.
Good afternoon, everybody. Welcome to the 2022 Elastic Investor Day. The Elastic team would like to welcome all of you who are here in person. It's great to see so many familiar faces, and all of you who are on the webcast. Before we begin, I will share a brief legal disclosure. Today's session is being webcast and is available for viewing on the investor relations section of Elastic's website. A recording of today's webcast will also be made available on the IR website, along with the slides from today's session. Statements made during today's session include forward-looking statements, and you should not rely upon forward-looking statements as predictions of future events. These statements represent our management's beliefs and assumptions only as of the date such statements are made. We do not intend to update any forward-looking statements unless required by law.
Such forward-looking statements are subject to numerous risks and uncertainties, such as those more fully described in our filings with the Securities and Exchange Commission. We will also be referring to non-GAAP financial measures. Disclosures regarding these non-GAAP measures, including reconciliations with the most comparable GAAP measures, can be found in the appendix of the slides of the investor relations website. With that out of the way, today you'll have the opportunity to hear from and meet a lot of our leaders. We will start the day with Ash going through the business strategy and business overview, followed by our product team, headed by Shay, and followed with Steve, Sajai, Santosh, and Matt. With product demos from our very talented product managers, [Bakar], and a couple of others, you'll get a complete overview of our product portfolio and solutions.
Following the break, you'll hear from our Chief Sales Officer, Michael Cremen. After that, Rick Laner, our Chief Customer Officer, will lead a customer panel with three great customers. One of them flew all the way from Europe, so thanks to all of our customers who've been here. Janesh will wrap up the presentation portion of the day with our financial projections and the bridge to $2 billion and beyond. We're gonna have a Q&A with the management team. This is the agenda in detail. A couple of logistical things. Please keep your questions till the very end during the Q&A session, which is gonna start at 4:45 P.M. Lastly, with the reception, we're going to have demo booths, so there you have the opportunity to dig deeper into the products.
With that out of the way, I would like to introduce our Chief Executive Officer, Ash Kulkarni.
Thank you. Hey, good afternoon, everybody. It's great to actually be here in person. It's been a couple of years now since we've had a chance to do this with all of you, and it's fantastic to see so many people here. I know there are a lot of people that are following along on the webcast, but I wanted to kick things off by first welcoming you and maybe talking a little bit about Elastic, who we are, what we focus on, what's our core reason to exist, our mission, our overall business strategy. Many of you I know have followed Elastic for a very long time, but for those of you who might be new to the story, let me just talk a little bit about what it is that we do at the core of things.
We all know that data is the lifeblood of businesses. I mean, we've talked about this ad nauseam. I don't think I need to prove that to anybody else. The reality is that there is one type of data that is growing incredibly fast that is very challenging to work with and get value out of, and that's unstructured data. Unstructured data is large, it's voluminous, and it's expanding, and I'd say exploding at an unprecedented scale. Think about logs. Machine systems, whether it's applications, networks that are constantly spewing logs that have a lot of information and value in them. Think about the descriptions of items that are sold online. All of those descriptions are typically in unstructured form. That data has a tremendous amount of value, and dealing with that data is very hard.
Dealing with that unstructured data, but then when you need to marry that unstructured data with structured and semi-structured, that's another level of complexity. For businesses to be able to do that well, the first thing they need to be able to do is bring in all of that unstructured data at scale very, very quickly into a system where you can actually derive some value from it. That unstructured data is in various forms, like I said. When you're bringing it in, you need to make it usable. You need to be able to index it. You need to be able to index as much of it as necessary to truly figure out what might be the questions, what might be the insights that you can get from it. Once you've brought it in, now you need to make it searchable. Why search?
Because when you think about unstructured data, you take a log file as an example, where you are not even sure exactly which of the elements in that log file are truly gonna be of value to you. If you aren't able to ask free-form questions, if you aren't able to iterate through it, you can't really get to the actual information that you're looking for. Traditional database techniques just do not work very well when it comes to unstructured data. Search is the lingua franca. It is the language that is best suited when it comes to unstructured data. It's not just that search, but then the ability to analyze all of that data, applying machine learning algorithms on that data in place. Then on top of it, the ability to visualize and explore.
Do that iteratively, so you can continually refine the information that you're getting, apply relevance, get to the next level of search and results, and get real value from it. That is what is needed when it comes to unstructured data, and this is really what Elastic is very well known for. This is our core strength. Based on that, there are the three solution areas, the three markets that we play in today. I want to connect the dots to truly make the point on why we focus on these three areas and what is our core differentiation. The team's gonna get into it in more detail along the day. Let's talk about observability for a second.
When you look at observability, the entire problem in observability is how do I quickly understand the root cause why some application might not be performing as well as you expect it to? Why some transactions might not be completing the way they're supposed to. To be able to do that, you need to be able to bring in all of those application infrastructure logs and so on. But you also need to then be able to correlate them with application traces, with metrics, with OpenTelemetry information, with profiles. You need to then be able to apply machine learning, what people talk about as AIOps, quickly surfacing the indicators that really matter so you can get to the root cause.
Doing it with workflows that are custom-built for site reliability engineers, those individuals whose job it is to often sift through the hundreds of gigabytes of data being spewed per hour when they need to deal with any one incident. Being able to do that fast, at scale, in a way that's suited for that particular use case is hard. It's all about dealing with that unstructured data, searching across that unstructured data. Observability at the end of the day is a search problem. Let's talk about security. When you look at security, being able to detect an indicator of compromise, an indicator of attack, it comes back to that same kind of a challenge.
You are trying to pull in network logs, application logs, identity and access management logs, pull them all in together, search across them, correlate across them to see whether you're seeing any patterns, hashes, malware hashes, IP addresses that might be problematic. To do that at scale and to do that quickly, and then apply machine learning algorithms, behavioral analysis, what people refer to as user entity behavioral analysis. To be able to do that with workflows that are suited for the security operations center, the SOC, the analysts whose job it is to be able to deal with often the tens of terabytes of security event data per year that Fortune 500 organizations end up having to deal with. This is inherently a search problem, and that's why we do security so well. Lastly, enterprise search.
Again, enterprise search, the kinds of use cases we play in, whether it's app search to allow you to have a search experience within your application, workplace search to be able to pull in all the relevant data that can help you deliver a better customer support experience. All of those use cases tend to focus a lot on being able to pull in unstructured data, being able to apply relevance based on capabilities like vector search, based on machine learning, and then surface that information within the context of that application. Search problems, a lot of unstructured data, the same core themes. This is why we play in these three areas. This is why we build upon our core strength and compete very effectively in these three markets. We do that all with one single platform, and that gives us some tremendous strengths.
The solutions themselves are built directly into the platforms. They are not add-ons. All the data sits in one data store, Elasticsearch. All the visualization happens in one layer, Kibana. The ingestion techniques are all well uniform, which means that one single platform gives me the ability to leverage common strengths across all of those three solution areas. Let me talk about these core differentiators that we get from this platform. First is the fact that it's powered by search, and I'll spend a few more minutes talking about it. Then also this notion of a singular platform and what it lets us do, the leverage that it gives us. Lastly, the fact that this platform is built on an open architecture, and that has some unique advantages and strengths.
When I look at this notion of being powered by search, if you think about the three use cases that we play in, whether it's enterprise search, whether it's observability or security, time matters. Being able to search across petabytes of data in milliseconds matters. When you are on a portal, on an e-commerce application, trying to search for some particular item, the speed with which the right relevant results are surfaced to you gives you that confidence that you're gonna keep transacting with that business. When you're dealing with a ransomware attempt, you need to be able to detect that ransomware attempt quickly and block it immediately. Speed matters. The same with observability. Every minute that your application is not performing the way it should, that's a problem. This is one of the big strengths of Elastic. By the way, we can do it at scale.
The fact that all of this data ends up in Elasticsearch is very well known for its ability to scale horizontally. Terabyte scale searches done very, very quickly. That's always been a core strength of ours. Relevance. With all of these search kinds of use cases, whether it's security, observability, enterprise search, relevance matters. In security as an example, we often see that the threat hunting routines that somebody goes through involves multiple iterations where somebody's truly trying to figure out exactly where did this threat start from. Our ability to show the right relevant results makes a big difference. The ability to visualize everything in that one single platform just makes it that much easier. Now, as we've built all these solutions in this single platform, we've been able to focus our efforts in some very meaningful ways to improve the customer experience.
First and foremost, in improving the overall onboarding experience, especially in Elastic Cloud. The simple, easy-to-use getting started experiences that you'll see today if you have a chance to hang out and take a look at some of the demos later. Once somebody gets started with that easy experience and comes onto the Elastic platform, these unified workflows that are tailor-made in these three solutions just means that the customer gets to value that much faster. They are not trying to build their own solution. That solution is ready-made. It's there for them. Once they adopt that solution, the single resource-based consumption pricing model that we have makes it possible for a user to go from one solution to another. Again, it all comes back to that same core principle that data stays in one single data store, Elasticsearch.
Because once you bring in your data, you might have started for the reasons of enterprise search, but once you have that data, you can see that you can do more with it. You can use that same data for observability. Once you've brought in data for observability, you realize that why just observe when you can also protect. These benefits play off of each other. The platform has been designed from the very beginning with this notion of an open architecture. It's a community that has been growing for the last 12 years. Partnerships, especially with all three major cloud hyperscalers, ecosystems of partners. You know, when we talk about open, another really, really important thing about Elastic is we've always taken this approach that we don't wanna be a black box. We want you to be able to see what is under the covers.
Because when you take that approach, the community innovates on top of everything that you have built. All our threat detection rules, everything is out there, so customers can add to it, expand upon it. That is a huge differentiator when we go and compete, because when we displace incumbents, one of the things that we always hear is, you know, we are the fresh approach. We are not the black box, don't look under the covers, but we are the ones who can come in and show you not just how it works out of the box, but if you wanna open the lid and look inside, you can. You can improve upon it if you want to. That's the power of the community.
The best proof point of that is the fact that when we went IPO, we had about 350 million downloads cumulatively of our software. Today, that number is over 3.6 billion. This demonstrates that when you're talking about search, anything to do with search, anything to do with unstructured data, we are the platform, the natural choice of users and developers. That is just a reflection of the strength of the brand. Whether you look at GitHub stars, pull requests, every indication shows the popularity, the natural bias that users and customers have to our technology, and that is a phenomenal source of strength for us. With Elastic Cloud, we are able to make this available to our customers wherever they are. We have deep integrations with all the three cloud marketplaces, which means you can purchase Elastic directly from those marketplaces.
You can transact in such a way that if you have pre-commits that you've made to these cloud providers, you can use that pre-committed spend to make your Elastic purchases. The experience is that much more seamless, and you get to choose which cloud you wanna run Elastic on, which data center. We are very widely distributed, and we support multi-cloud and hybrid. The team's gonna go into a lot more detail on that when we cover products. These are some of the core differentiators that we have. We make it possible for you, as you're going on your journey towards the cloud, to adopt Elastic in more compelling ways. I wanna spend a few minutes just talking about each of the solution areas. When we look at Elastic Observability, we almost always start with log analytics.
Over the years, we've built a comprehensive set of capabilities, everything from APM to infrastructure monitoring, real user monitoring, more recently, synthetic monitoring, and even continuous profiling. All of these capabilities on one single platform. Companies like Zurich Insurance Group, they started with us for log analytics, but have expanded significantly since then. Let me touch upon security. By the way, the product teams are gonna go into a lot more detail. When we look at security, we almost always lead with SIEM. Again, going to that core principle that I talked about. It's where unstructured data is the most prevalent, it's where we have our greatest strengths, and we lead with those strengths. On top of all of that functionality, we've built capabilities for XDR, leveraging the technology that we got when we joined forces with Endgame.
We have capabilities for cloud security, and that provides a comprehensive security portfolio that customers like Orange Business Services have been able to leverage. In enterprise search, we are incredibly well-known. These are some of the earliest use cases of Elastic, as you all know. It's not just been application search, which is where we started, but workplace search. All the capabilities that we added around machine learning and vector search recently have made us very potent in this area, and customers like BMW are using us across the board. The total addressable market that comes from these three market areas, these three solution areas that we play in, is massive, and it's growing. Now how do we take advantage of that market opportunity? How do we expand and grow to get our rightful share of that market opportunity? Well, it's basically these six vectors.
The first is just adding more customers. I talked about the fact that we've had over 3.6 billion downloads. That just gives you a sense of the popularity of the core technology. With the investments that we've made in Elasticsearch, and specifically in Elastic Cloud as the platform for our new customers to land, that just becomes a wonderful place, Elastic Cloud, for us to sign up new customers. Now, once a customer comes on to our platform, as data volumes grow, their consumption grows, their usage of Elastic grows, and our business with them grows. The third is as customers adopt more use cases within the same solution. I gave some examples of the kinds of customers that started with us on log analytics for observability, but then went on to use us for APM and more. The same thing with security.
The same thing with enterprise search. The fourth vector is about customers adopting us for more solutions. Somebody that starts with one solution, say, enterprise search, and then adopts us for observability. Now, this is not that easy to pull off because these are distinct solution areas. Because of the way the platform is constructed, because of the fact that all of this data is ending up in one place, and because of the fact that the solutions allow you to gracefully move from one to another, today we have in the cohort of customers that spend over $100,000 with us, over 400 customers that are using us for more than two solutions. Two and more. That just reflects our ability to drive that expansion motion even across solutions. The fifth is when customers adopt cloud.
We've seen that the net expansion rate in cloud is higher because it's a more frictionless model. As we lean into cloud, our success in cloud is that fifth vector for us. Lastly, as customers adopt higher subscription tiers. Our enterprise tier is our most premier tier, and that's our fastest-growing tier today. Multiple ways of expansion, and we are leveraging our go-to-market strategy to drive all of those plays. The bottom-up motion, leading with Elastic Cloud, leveraging that deep love that we have in the community for Elasticsearch in our platform, making it easy for customers to onboard, and then marrying that with a top-down enterprise sales motion that Michael's gonna touch upon.
We are using a lot of the instrumentation that we have, the telemetry that we get from Elastic Cloud to inform the decisions on how our sales teams should engage with these customers, especially in cloud. That then helps them drive the expansion motion better. We almost always, when it comes to the land aspect of things, lead with these three anchor use cases. Log analytics in observability, SIEM in security, and app search in enterprise search. Because all of these three use cases are so heavy on unstructured data, where we have an amazing competitive advantage, where all of those trends of being able to deal scale, speed, relevance, really come to the fore. Logs are almost always the most voluminous, by the way, when it comes to all the different types of data that you can deal with for observability or security.
When we lead with these three use cases, not only are we able to land incredibly well, but it becomes a wonderful place for us to expand from. We'll start by expanding within that solution. If you've landed with log analytics for observability, our natural motion will be to talk about APM, infrastructure monitoring, et cetera. If you've landed with SIEM, to talk about XDR, to talk about cloud security. Cloud security is pretty new, as you know, and so on. To go across solutions as well. We have a lot of these examples. I talked about that 400, over 400 customer count in the 100,000-plus cohort that is using us for more than two solutions. Well, I'll give you a few examples. Emirates NBD.
They started using us for observability and specifically with log analytics, but very quickly realized that they could do so much more with us. They started using us for APM. Once they realized how much value they could get by using us as a comprehensive solution for observability, they started using us for security, leading with SIEM. Again, the data often tends to be very similar, and they are very quickly able to realize that a lot of the same technology skills that they are honing in one area are very applicable in another, allowing them to do more with less. Booking.com, very large customer, very well-known customer in Europe. Now, Booking.com started with us on enterprise search. You know, when you go to Booking.com and you're purchasing anything related to travel, you're hitting Elastic under the covers. It was a classic enterprise search use case.
From there, they quickly realized that that same application could be monitored for observability reasons using Elastic. They started using us for log analytics, and over time have started using us for security and SIEM. This is just some of the proof points of our consistent ability to be able to do this and do this well. Before I turn it to the product teams, I just wanna bring it home a little bit and talk about the overall mission that we are on, our business strategy. Given the market opportunity, our overall total addressable market, our strength when it comes to dealing with unstructured data, probably the type of data that's growing the fastest out there, we have confidence that we are building a generational company, and it's on these three pillars.
Durable growth, leading with our strength, log analytics for observability, SIEM for security and enterprise search. It's about cementing our leadership in these three areas. It's then expanding from there and leaning into cloud. Our competitive moat. Our innovations when it comes to all things Elasticsearch and the platform that we're building continues. We have a significant differentiated moat here. The ability for customers to use this one single platform for multiple use cases and multiple solutions just means that we become an even more critical part of their infrastructure over time. The open architecture is key to everything that we do. All of these benefits also help us in terms of building a profitable company, driving towards profitable growth.
The fact that we have these marketplace partnerships, the fact that we have this efficient bottom-up motion, really means that we can continue to drive profitable growth going into the future. Even from an engineering standpoint, it gives us the ability to be more efficient because it's one single platform. A lot here, and the team's gonna go into a lot more detail. Let me bring on to stage my colleague, our founder, our CTO, and the person who convinced me to join Elastic, and the person I still enjoy having a lot of fun working with, Shay Banon. Shay.
Thanks, Ash. Hi, everybody. It's great to see many faces that I recognize here. I'll just do the opening and give a chance to all of our product team members to talk about the wonderful things that we do on the product. I just wanted to share a few things, at least on my end. As you know, I moved back to a CTO position from a CEO. First of all, I can just say I'm having fun. Not that I didn't enjoy the CEO position, but my heart was always on the technical side of the house, and I'm just enjoying immersing myself with the team members and our customers.
The last few months, I focused a lot on both our customers and our community and also getting back to understanding our products at a level that I didn't get a chance to do over the last few months or for the last few years. A few things I realized. The first one, we have a really strong distributed engineering team. Those are people that develop amazing capabilities that you get a chance to see that are just, you know. I talk to a lot of startups out there and other companies, they're just a level of quality there that is just very humbling to see. The other part is that the products that we develop are very mature. In enterprise search, I'm talking to customers, I'm seeing the competition that we have. I get a chance to experience the product.
In enterprise search, we have been leading the pack, and I don't see in the next few years how others are gonna be able to come over, and be able to lead the enterprise search space. We have probably the most popular search engine in the world today in Elasticsearch, and through that, we enable enterprise search capabilities to our customers that are far and above beyond what anybody else can provide. We're not slowing down, and you get a chance to see it with vector search capabilities, machine learning. This is like significant advancements that we have, and a market position. The other one is observability. You know, when you lead a product, you always look at the things that are still missing. We still need to do Synthetics better or other areas.
When you take a step back and see how the products affect the customers, our story in observability is very mature. When you look at the observability capabilities that we have, and the maturity of our observability solution compared to other vendors in the space and compared to what our users are asking us, it's there. We're still getting questions about maybe you can do something else a bit better or something along those lines, but there's nothing significant. Like, we don't have to go to the drawing board and re-architect something or innovate something that is not obvious. It's very mature. In the security space, and remember, like this, we're not born as a security company, so we had to learn how to do it.
We got a chance to join forces with Endgame to really inject our company with security DNA. Our SIEM solution, again, a SIEM part within security is very mature as well. We're growing and improving in an extremely fast pace when it comes to our security solution to a level where we satisfy endpoint security, SOAR and other key cloud security and other capabilities. Really the level of innovation that we have in the products and the maturity that we have in our solution is something that I feel very confident about. Second part is that all of it is built on top of a single platform. In Elasticsearch and Kibana, both of them are leading the pack.
You know, sometimes you have to, again, like take a step back and look at the statistics and the community around it. Those are two of the most popular products in the world today. It's just impressive to see. You don't get a chance to talk to a developer, to speak to a developer here in the street. There's not a lot left, I think, in San Francisco, right? The rumors are everybody moves to Miami or something. But if you talk to them, there's no chance that they won't know about Elasticsearch and Kibana. That's impressive. Like, that's a level of mind share that we don't take for granted as a team, as a company, as developers that develop these products. This is something that we got to in the last 10 years.
There's also the next steps that we can take when it comes to our platform. That's where I personally have been focused over the last few months. For example, when I became a CTO, the area that I got drawn to immediately, you know, partially because of the fact that I feel very close to it, is Elasticsearch. I want to make sure that Elasticsearch is the best search engine in the world, not only in the next couple of years, but in the next 10, 15 years. It's the best place to push data to get all the capabilities that Ash talked about. Unstructured data, search, speed, and relevance, not only in the next couple of years or something like that, in the next 10 - 15 years.
That means that we need to evolve the architecture and the capabilities of Elasticsearch to address the needs of tomorrow. I'll give you one interesting example. Maybe you're familiar with the capability that we have called Searchable Snapshots. It has been extremely impactful on our business. You can see the effect of it in the enterprise subscription tier, for example, that Janesh will show, but also very impactful on our customers. They can put so much more data into Elastic and being able to search it at a much cheaper price and obviously a better financial outcome for us. We've been spending the last few months trying to figure out how can we make everything in Elasticsearch Searchable Snapshots. Every piece of data, every single d oesn't matter whether it's old data, new data, something along those lines.
Decouple compute from storage completely, while still maintaining the speed, all the things that Elasticsearch is known for. Speed, scale, relevance. Make it completely stateless. Serverless. The last few months we've been proof of concepting it, and it's really exciting. The results are very, very promising, and to me, our ability to go and deliver it. We don't have an exact date, obviously, for that delivery. We're working extremely hard on it. This is not something that is gonna take years to come, right? It's like it's something that our engineering team are working on now, and the ability to do that is gonna make sure that Elasticsearch is the best search engine in the world, not only in the next couple of years, but in the next 10 years.
It's gonna be a competitive advantage for us because not only Elasticsearch will be the best engine in the world, it will make our solutions significantly better. That's thanks to the fact that we're a single platform. To give you a bit more glimpse into it, I'd like to invite Steve Kearns, one of the early Elastic employees, who leads our platform team today to talk a bit more about the platform. Steve?
All right. Thanks, Ash. All right. It's been, Ash, eight years already, and it feels like we're still just getting started. Hi, folks. I'm Steve Kearns, Vice President of Product. I lead our platform group. One of the most unique things about Elastic is the way that we build our products, right? Building around one core platform is not a natural way for people to design some of these technologies and solutions. Ash touched on the core components of the platform, right? It starts by getting data in, right? The ability to have hundreds of pre-built data integrations, a single centrally managed agent to pull data in makes it easy to get the data in. When you get the data in, you land it in Elasticsearch. Highly scalable search engine, right?
When you think of speed, scale, and relevance, that's what Elasticsearch does for us. Then Kibana, the front ends to the entire platform, right? The analytics hub, the place that you manage and monitor your deployments, and the place you experience all of the solutions. When you go forward today and see the demos in the booth or hear some of the rest of the discussions, all of the UIs that you're going to see are Kibana, and all of the data that you're going to see is stored only in Elasticsearch. This technical simplicity has a lot of ramifications for us, right? Ash talked a lot about what the platform does. I wanna focus a little bit more on why we build the way that we do, why we focus on one platform.
There are benefits to our customers and benefits to us as a company as well. When we think about our customers, one of the big benefits that we have with the platform is flexibility. When you start to think about observability or security, all companies have their own data. Take a company like Jaguar Land Rover. They're using us for observability, and they're looking at, you know, monitoring their servers and their applications and their infrastructure, but they're also generating tremendous amounts of data. They also have a fleet of connected cars. Pulling that data into the same system for them, that's just an extension of observability. Thanks to the power of the platform, it's very easy for them to continue analyzing that data.
Start by looking at the applications that run on your servers, continue by looking at the software that runs on your cars. This ability to build custom views onto your data to use platform-level capabilities like machine learning, to detect when one of those connected cars is performing differently than the others, that's really powerful to them. It's a differentiator when it comes to succeeding with observability for them. For us, you can think of this as almost like there's no edge of the map for our solutions because the platform is there to continue extending your ability to analyze and make use of that data. At the same time, as people start to use this for more than one solution, right, more than one area, the benefits of tool consolidation start to become very visible. Take a company like InfoTrack.
They started to use us for building their search application. In fact, they actually started with the Amazon OpenSearch Service, and they moved to Elastic. One of the reasons they moved to Elastic, there were a few, but one of the big ones is they wanted to use the same technology, the same tool chain for building their search application. It's a legal search, a document search application. They wanted that same technology that their developers are familiar with to monitor that application as well, right? By consolidating that application, going with Elastic Cloud as a managed offering, it let them put a lot more of their time and energy onto their business, the things only they can do. What did they do at that time? They invested more in Elastic.
In fact, their whole data lake now is based on Elastic, and they're using us for a number of different applications internally. We also see a tremendous number of operational efficiencies. This covers a couple of areas, right? The kinda things you might think of as what does it take to stand up and run and operate a set of services. When you've got five or six or seven different products for observability and security, you need five or six different plans for how am I gonna store that data? How am I gonna protect it? What's my data retention and data governance policies look like? It's a huge amount of overhead. And so as folks simplify a lot of their environments, they recognize a lot of these operational simplicities. Also hits in other ways.
One of the things we see a lot as folks adopt us for observability and security, we see a lot less data duplication. One of the reasons is the data that you collect for observability is very similar to the data that you need for security purposes, and the fact that you can say, "I'm gonna collect this data just one time, search it where it lives," can really simplify the environment. It's to the point where one of our financial services customers actually referenced Elastic as part of their sustainability efforts, saying they've reduced their total cost of ownership, reduced their spend, put more of their spend on Elastic, and also did something good for the planet. It's really nice to be able to say that. Now, it's not just the products themselves, it also goes down to our pricing model.
We have a resource-based pricing model that's really targeted to help our customers solve their whole problem, right? Whatever they're trying to accomplish, we don't want our licensing model to get in the way. So by pricing based on the resources it takes to store, search, and analyze the data, it means that we don't have to have awkward conversations with them about, "Well, wait, are you running in containers? Because that's a different pricing model, and if you're monitoring another application, that's a different cost," right? We can really focus on helping them solve their underlying challenge. One of the things that's interesting, you can see a lot of other companies these days starting to adopt these kind of resource-based or consumption-based prices, right? It's very natural in a cloud-first world.
At Elastic, we've had this pricing model, resource-based pricing from the very beginning of the company, and we do that because we want our customers to be successful, right? We wanna make sure that we're aligned with them solving their whole need. Now, the benefits of investing in a platform, right? Building the way that we do, it's not just for our customers, it's also for us, right? We see big efficiencies in terms of how we build our products. When you start to look at, there's a number of different ways that we can sort of show this, right? Think about on the one hand, speed. So much of what we do, so much of what we're known for is around speed. As data volumes continue to increase, that speed becomes that much more important. We hear this over and over again, right?
If I can get an answer to my question in a second, I'm gonna ask another question. It changes the relationship that I have with my data. It changes the value I can get from it. This is something we hear over and over again from our customers. We continue to invest to make Elasticsearch faster, and that means that all of our solutions benefit, right? Some of our use cases are 4x faster, some of them for enterprise search, more than 100x faster, even as data volumes are expanding. As we make that improvement at the lowest level of Elasticsearch, all of the solutions benefit from that investment. The same thing on the scale side of things, right? Companies like WePay, they store more than 0.5 PB of observability data in Elastic today.
As a highly regulated industry, they have to have that data available when the regulators call, right? It has to be reliable, has to be accessible, and available to them, but they don't need it to be super high performance. They can use platform-level capabilities like Searchable Snapshots that Shay talked about earlier to dramatically reduce their cost of storing and searching that data without giving up on availability or accessibility. That's really the beauty of this model, right? As we invest in the core of Elasticsearch, the core of Kibana, these benefits accrue to all of the solution areas, right? The solution users and our solution development teams alike, right? You can almost think of it as a force multiplier for our engineering team. This applies, right, in so many different ways. If you think about it, we have...
You know, we're gonna hear from Sajai and Santosh and Matt Riley, the leaders of our solution teams. Each of them have a large engineering team, right? Working on building out these really excellent applications. For those developers, for those teams, they have a lot of efficiencies, right? They have one data store to plan for. Just one way to bring data in. They have one powerful and performant query language that they have to learn. When they're building charts and graphs, there's one platform to integrate it into. This means that they can build much more efficiently and bring these products to market more efficiently as well. It benefits our customers too. When our customers adopt us for multiple use cases, we see pretty quickly that they start to gravitate towards creating centers of excellence around our products.
They can make one set of decisions around, well, how do I secure data in Elastic? How do I wanna protect that? What are my data governance requirements for my business and from a regulatory perspective? Answer those questions just one time consistently across these different use cases. All of this investment that we make in the platform, it is really about one thing. It's about helping our solutions innovate faster. Observability, security, and enterprise search. We want the work that we do in the platform to benefit us in many different ways. Maybe a simple mental model for this is, a dollar that we invest in our platform team should provide more than a dollar of forward motion in our solutions. I've given a couple examples of this already, but maybe one more.
We needed a rich alerting capability to power our SIEM, right? This detection engine that's at the heart of our security product. When we built it, we built it as a platform-level capability so that we could bring these same rich alerting and case management capabilities to observability customers as well. This, again, building it once, motivating it for security, making it available to observability, allows us to move faster as a business. It also benefits us as we introduce these new use cases on top of our existing solutions. The reason we're able to bring cloud security to market so quickly or continuous profiling to market so quickly is in part because it's building on a rich and capable platform. Now, I've focused so far on the benefits to our customers and our engineering team.
There are also benefits to our go-to-market team of this model. When you heard Ash talk about this earlier, right? We have no barriers or boundaries between the solutions, right? Nothing in the product holds you back. Nothing in our pricing model holds you back. Our field teams, when they're in front of a customer, they're not worried about, "Oh, but if I ask them to use this one extra capability, continuous profiling or add APM," that's not a new price point. That's not a new pricing model we have to educate on or have a conversation about. Instead, we can say, "Hey, what problem are you trying to solve for your business? What opportunity do you see, and how can we help?" That allows us to really focus on solving the whole problem for our customers.
Now, to build a company as dynamic as Elastic around a single platform, it is hard, right? I think, both Ash and Shay alluded to this a bit, 'cause when we build a new capability or join forces with another company, we don't just bolt on additional capabilities onto the side, right? We integrate those technologies deeply into the core of the platform. We do this because we need to ensure that our platform stays efficient, both from a sort of cost perspective, total cost of ownership, from a performance perspective, that's what we're known for, but also, consistent as we continue to scale the business and continue to scale the data that comes in. We believe that building this way provides us an enduring technical advantage, and so we put a significant fraction of our engineering effort into the platform teams, right?
As our company has grown, I think you can see the pace of innovation coming out of the platform continues to accelerate. It's really exciting to see. Now, one of the most important aspects for us is Elastic Cloud, and we talk about it a lot. The reason is, this is the best way to use our products, full stop. It's available in more than 50 regions across the big three cloud providers, and it offers a level of operational simplicity that you just can't get by downloading and using our products. One click to upgraded deployment, one click to upgrade to the latest, hardware from any one of the cloud providers. It's really simple, and it allows our customers to put more of their energy back into their own businesses. Thanks to our close partnerships with the, hyperscalers, right?
Microsoft Azure, Google Cloud, and AWS, Elastic Cloud lets you purchase through those marketplaces. That allows you to say, "Well, when I'm setting it up, directly integrate into a number of the data sources in this cloud provider and burn down my pre-committed spend with that cloud provider," right? It's a really great way for our customers to engage with us. It works for everybody, us, the cloud providers, and the customers. While cloud is where most of our new customers and new workloads begin, we also have a large installed base of existing customers, right? You saw those download stats. There's a lot of people downloading and running our software themselves.
While many of those customers are on their journey to cloud, some of them and some of the workloads may take a while to move to cloud or may never move, right? Some regulated industries may not move all of their workloads to cloud, and so they're gonna be living in a hybrid environment, having some of their data in an on-premise data center, some in a public cloud, maybe multiple public clouds. We have capabilities in our platform like cross-cluster search. Hybrid cross-cluster search allows you to bring a single pane of glass across your self-managed deployments and cloud together, right? Really powerful for customers that are in this hybrid world. Now, I wanna switch gears for just a moment and talk about a couple of the things that we're working on in the roadmap, looking forward here.
Elastic has always been great at storing and searching metrics data, right? This is the core and central to our value proposition, right? Bringing together structured and unstructured data, and we've been very good at this for a long time. As we look at the scale and the volume of metrics data continue to explode, we saw an opportunity to improve the efficiency of the way that we store and search this data. We've made big investments in how we store that data. In some of our early indications, we're seeing more than 70% reduction in the storage cost without making any changes to the kinds of queries that you can run against it. Still can combine your structured and your unstructured data together.
This value is huge for observability use cases, whether it's infrastructure metrics or even other use cases like APM, where traces have a lot of metrics within them, or security network traffic data, right? Tremendous amounts of metrics here. These are huge area. This is one of these areas where our focus on efficiency in the platform, it really benefits us in multiple ways. We started by saying, "Let's make time series metrics, right? The metrics data more efficient." With these foundational improvements we made to Elasticsearch, we're actually seeing a 40% reduction in the total cost of ownership of logs data as well, right? We're working ourselves and benefiting from developing into the platform these same capabilities. Another big area of investment for us is in our query languages, right?
Elastic is known for the speed of our queries and the power, that we have in the sort of existing query languages of Elastic. One of the things that our security users and advanced observability users come to us and say is they say, "Hey, we love these capabilities. We love the power of the query language. But what I need to do, or what I want to do, is I wanna take this query, and I wanna take the output of that query, use it as an input to another query. Then I wanna filter it like this, and then I wanna pipe it into another request." What we decided to do was to build that directly into Elasticsearch itself so that we can support this natively.
We're building right now a new query execution engine that supports these multi-stage queries, allowing you to do things like pipe results from one query into the input of another, do joins and sub-queries, and all of these additional kinds of processing right inside of Elasticsearch itself. We've paired this with a query language that'll be familiar to security analysts and DevOps engineers alike. You'll hear a lot more about ES|QL later this calendar year and into next, but it's an area that we're really excited about. Finally, Shay alluded to this, just before I came on stage, right? One of the big areas that we've been investing in is taking advantage of what the hyperscalers provide, right?
These major cloud providers have all of these services like low-cost object stores, and that's allowed us to build these compelling features like Searchable Snapshots that offer dramatic reduction in total cost of ownership for storing data for extended periods of time. One of the ways they do that is by decoupling the compute, the amount of compute resource that's associated with a given amount of storage. By decoupling compute and storage, it gives us tremendous control over what does it cost, what is the total cost of ownership for a given use case, and how can we tune that for different scenarios, different cloud providers, and different customers. This is a foundational set of work, as Shay mentioned, that will be coming out and benefiting us for years to come, but stay tuned, a lot more to come on this.
As I hope you've seen, we believe pretty strongly that developing as a platform is an advantage to our customers and our company, and we're investing and innovating accordingly. With that, let me go and hand it over to Sajai, who's gonna take us through Elastic Observability. Dive in there.
Thanks, buddy.
All right.
Good afternoon, folks. I'm Sajai Krishnan. I'm the GM for Observability. Thanks everyone for spending the afternoon with us. Must have been an adventure flying out yesterday, so thanks for being here. I'm relatively new to Elastic, so a couple of weeks shy of five months. As I get ready to talk to you about our observability solution, I just wanna say, like, I'm super excited to be digging in to Elastic's tech stack. You know, as Shay was mentioning, the breadth and depth and maturity of our observability solution really gets me charged up. The other thing is every week, you know, we meet customers, it's really great to meet Elastic's customers.
There's a spirit of partnership, Marcel, for example, and almost fondness, and that, for a new person walking into these accounts, is truly special. With that said, many of you know this, clouds, containers, microservices, mobility, apps being accessed from anywhere, a lot of complexity and dynamism in today's software stack. Really that requires us to take a new look at how observability tooling works, right? Let me start with a relatively new services company, Accolade. As Ash was talking about the marketplace motion with our hyperscalers, Accolade is an example of that. Now, Accolade, in the patient services business, observes roughly 400 services with Elastic Observability. They started with siloed tooling, you know, different tools for traces, metrics, and logs, and then chose to consolidate everything on Elastic.
They also love the fact that they can access the data and it's not hidden behind any API. They're very much into business analytics and finding patient care metrics and so on, based on this data. Another positive for them. The developers seem to be super excited, and we are gratified by that. Stepping back up a level, talking to that, the maturity of our overall suite and APM in particular, log analytics is our strength. As you look at Elastic APM, a third of our cloud clusters today are instrumented for APM, and they're using APM. A large mutual fund company uses Elastic Observability for observing 1,800 services. This is really at scale, right? A U.S. healthcare services company only has APM, has no log analytics.
They're now looking to consolidate into log analytics, but to date, they're all APM. Typically for our customers who are already log customers, turning on APM is very easy. It's 10 minutes for a Java app in production to install the agent and light up the traces and spans, right? All of that, what it means is, as we look into this market in somewhat uncertain macro climate, the observability business continues to be robust as far as I can see, as far as we can see. That's because it's really essential, so it's not optional. The tool consolidation conversations are going on every week across multiple verticals. As customers consider a consolidation story around observability, the fact that we are so widely deployed, adopted is a good thing.
As Ash mentioned, 3.6 billion+ downloads. That picture there is from a survey done by CNCF, the standards body. What it shows at the bottom in that blue section there is Elastic in the company of some really well-deployed and popular technologies. In the 2022 Gartner Magic Quadrant, we were called out as a visionary. Even more interestingly, in the companion technical report called the Critical Competency Report for APM and Observability. By the way, we've been featured in these APM Magic Quadrants. This is the second year we're doing it, right? It's fairly new for us. In five of six critical use cases, we've been listed amongst the top three. This is a little bit of a eye chart, but it is intentional.
The takeaway here is, again, case in point to Ash's and Shay's comments about the maturity of our tech. There's a lot of work done over a decade to get to this point. I'll start with 2016 and Prelert. That was when we joined forces with this machine learning company, came in with at least six years of tech. More than a decade of machine learning technology behind the scenes, which we need to continue to bring to market as AIOps. 2018 with, you know, when we joined forces with Opbeat was when we got into the APM market. 2019 and 2020, you can see a lot of activity in the open source, you know, open standards, OpenTelemetry in 2020. Also, our version one of our Synthetics product.
By the way, Synthetics is a emulation technology to come in outside in and test the user experience. So catch it before a user is tweeting about poor experience. You can use Synthetics to kinda do that kind of outside-in testing. We joined forces with Optimyze with some really cool profiling technology, and I'll come and say something about that in a little bit. 2021, 2022, a lot of the techs that Steve was talking about, platform technologies and advantages, we brought that to market through the Observability suite. 10 years of work, a fairly complete Observability suite across the top line. You can see all those yellow boxes there. It is a complete solution ready for customers who are looking for a more holistic, consolidated kinda approach.
All of this consolidation, again, back to that single platform, single store, topic, we keep coming back as Elastic. Why does it matter from an observability perspective? Metrics, logs, and traces, three different signal types, all on Elasticsearch, single store, at scale, with integrated context across all of that, which really facilitates our machine learning. When you get anomaly detection in metrics, anomaly detection in logs, marrying it all together is super important if you have to see patterns in the data. So our machine learning tech, you can see on the left side there, lot of capability. Clearly, this market is mature. There are lots of competitors, right? Logs, log analytics is a solid foundation. As Ash was mentioning, you know, we are leaders in that segment. It's a great platform to launch this consolidation story from.
When you compare us to other leaders in the log analytics space, we are really, really capable on a TCO and a price value dimension. Depending upon customer configurations, we are 2- 20x more performant. Yeah, you know, you will see these slides on the investor side, so you can look at the speedometer. When you compare us to really great metrics vendors, what kind of stands out obviously is the sort of constraints they place in terms of how long they retain their data. Automatic roll-ups of metrics, traces, health for 7 - 30 days. Elastic offers its customers the ability to keep everything in uncompressed native format. It's a customer choice, depending upon what their needs are. When you look at the mature APM space, there clearly are interesting challenges in that space as well.
What that graphic is referencing is, you know, OpenTelemetry and these open standards. These are hard for the APM segment to adopt. They're the models are more proprietary. It's not unusual to discover that there is a fee to use open methods of telemetry ingestion. Roughly 15% penetration in the APM space to date with APM suggests, you know, there's good opportunity for us to actually take good share there. Lastly, when you look at petabyte scale observability, there's work that's ongoing amongst the vendors in that space. Overall, you know, I feel pretty good and we feel pretty good about Elastic's opportunity in the observability solution space. Why we win? In summary, there are platform reasons and there are solution reasons. On the solution side, number one in log analytics, scale, performance, and TCO really behoove us well.
The fact that we bring this integrated approach to observability across all signals, signal types, app to infra, on-prem, and multi-cloud. On-prem and multi-cloud observability of a single set of bits at the back end is unique, right? Best-in-class telemetry support. If you're going to get trace information from OpenTelemetry and agents, we do a really very good job of aligning all of that and not getting your ML algorithms messed up, right? Platform capabilities, we talked about ML. Talking about consumption billing, Ash described it in some good detail. Michael will talk about it as well. Point is that when you're an SRE and you're looking to deploy different observability capabilities to different line of businesses or app teams, it's very easy to work in the framework of Elastic. You know, you wanna deploy Synthetics? Fine.
You wanna go ahead with APM? No problem. You wanna try security? You can do that as well. Okay? Lastly, enduring economics over multiple years. The three-year horizon, five years. As you get down that journey and you're going towards petabyte scale, lots of levers you can throw. Tiered storage, on-prem versus cloud, federated search across clusters, all really great capabilities. I wanna leave you with our story for fiscal 2023. These are the three areas we're gonna focus on. Very excited about bringing the optimized product to market. Continuous profiling, super exciting because unlike. You know, profiling is a fairly mature tech, has been around for 20 years or so. All of that requires instrumentation in the code. What that means is that there is an overhead, there's a tax, so you don't run it in production.
You run it with specialist performance people doing tuning. 1% overhead, zero instrumentation, we think it'll be a game changer. Very exciting. Gen two of our Synthetics, a shift left approach to Synthetics that we feel excited about. 270+ betas, that's looking really good. Lastly, continue to kinda deliver on AIOps. That's observability, and I wanna just invite my colleague, Baha, to come up on stage and bring our tech to life.
All right. Thank you, Sajai. I'm Baha Azarmi, I'm gonna present the observability demo. The demo features a company called ACME Financial. This is a company that provides to their user a solution to manage their financial assets. There are three priorities in mind. The first one is a reliable and available platform. Second is real-time for everything, any clicks, scroll, anything ultimately as a search action. The third aspect is to protect themselves and their users from cyberattacks since they are a regular target for hacking groups. We're gonna look at the process of adopting Elastic Observability at ACME. We're starting with the onboarding of their operational and business data. We will look at the insight they provide to their operations team to look at the health of their system and also track the root cause in case of an incident.
Lastly, how did they grew their observability footprint to new business services and deployments. ACME has a lot of microservices. They have a highly distributed application, and to collect this data, they will, as we see on the screen, deploy the agent, the Elastic Agent, from a centralized console called Elastic Fleet. It's not only to collect the data, but we will see that with Santosh, it's also to protect the host. One of the thing with Fleet is not only being able to manage the policies, manage the lifecycle of the agent, but it's also in a single click, enable integration out of the 200+ we have. Sajai mentioned that we have a native integration with OpenTelemetry.
For ACME, it's really important to be able to combine OpenTelemetry with the agent, so they don't fall into the same pitfalls than with other vendors in the past where they had duplicated data presented to the operations team, which was completely misleading in terms of the investigation. Now, the root cause analysis workflow starts here with an underperforming service where the operations team can drill down and look at the anomaly spotted into the latency distribution here. Then drill down into the anomaly detection job to get more details and switch to the traces correlation and separate the inconsistent traces and immediately see the root cause. As we can appreciate here is the fact that machine learning is a core component of the root cause analysis workflow with four aspects. The first one is metric anomaly detection, as we see, in the demo.
The second aspect is the traces correlation, so the separation of traces is done by machine learning. The third aspect we'll see in the next section is log categorization. Surfacing the abnormal logs and making it easy for the operations team to just look at which one are abnormal. The fourth aspect is for the control team to be able to have full visibility on what the developers are logging. They wanna make sure they stay compliant, so they will be able to load NLP models, so natural language processing model, to surface, for example, personal data and make sure they're compliant. Sajai mentioned how uniquely we combine traces, logs, and metrics. It's not only if we double-click on logs, it's not only structured logs, it's also unstructured logs.
Three challenges ACME was trying to solve here is, first, onboard those logs seamlessly without having to format anything. The second is to be able to provide this guided experience where an ops team can get into the stream here and then jump into the anomaly section and surface the abnormal logs at the top, and then drill down into the category to get an understanding of what the root cause is. The third aspect they're trying to solve is, it's not only great to onboard unstructured logs, it's even better when they can use it at scale and with speed. While with Elastic, they're able to do 20% of the investigation in their process, they can only do one with the previous solution.
Now, ACME suffered from penalizing pricing, and Sajai mentioned that some of them are restricted in terms of how much data you can retain. In their case, it was even worse. They couldn't have as much data as they wanted, ingest as much data as they wanted, so it reduces dramatically their observability coverage. With our flexible resource-based pricing, they can stretch it out, close to user, down to the kernel. In this example, we see a case of endpoint availability monitoring combined with synthetic monitoring, which allows them to understand what the user behavior is and the performance perceived at the application level. Now, ACME has, like a lot of customers, a hybrid approach to their deployment. They have deployments on-premises, they have deployment in the cloud, in multiple clouds spread across regions.
For them, it's really important to have the Elastic observability footprint next, sitting next to those workloads. With Elastic cross-cluster search, they're able to provide a federated approach to these decentralized deployments, so that for all the UIs I've showed, a user can connect and not even have to think about where the Elastic workload is. They can have a first-hand experience and go across all the observability data. Now, just to finish, what truly differentiates Elastic for ACME is to be able to keep the TCO under control while playing out with all the dimensions we're seeing on the screen. They were able to deploy a petabyte scale observability with a flexible deployment model without compromising on the strong root cause analysis. On top of that, they're breaking the barriers between observability and security using the data from one side for the other side.
We're gonna see the Elastic offering now with Santosh. Thank you.
Thanks, Baha. I'm Santosh Krishnan. I'm the General Manager for Elastic Security. One year into Elastic, enjoying every minute of it. Security today is all about collecting data from a wide variety of sources, typically unstructured events and logs, applying behavioral rules and machine learning models on top of them to drive insights and threat detection, and then storing that data away efficiently so that you can do future investigations. Without a solid foundation in data management, security teams often have to cut corners in terms of the data that they collect, which reduces their threat surface visibility.
In some cases, they also have to invest in very cumbersome tools, like for example, rehydrating data from archival storage and such, which increases their time to response. As an example, the security team at OLX, which is a Dutch online marketplace, they engaged with us a little over a year ago to increase their visibility into the threat surface. Their existing tools had reached their limits in terms of the data volumes that they could capture, and also their operational complexity was building up due to siloing of all the data which they were managing. With the Elastic platform, we were able to increase their security-related log collection by 20x, so from about 500 GB per month to 10 TB per month, all the while reducing their response time by 30%.
Now, this pattern of need is not unique to this customer. It is something that we see across our entire customer base and the market at large. Accordingly, our vision in Elastic Security is to completely modernize security operations with the power of the Elastic platform and with its inherent data management and analytics capabilities. Specifically, our goal is to expand visibility into the threat surface by ingesting everything and storing it in an efficient fashion, applying our analytics capabilities to detect and prevent threats in real time, as well as provide insightful investigative workflows in order to reduce time to response, all in a consolidated offering so as not to contribute to further analyst sprawl. I mean, you have heard about security teams, you know, having to manage hundreds of tools and beyond.
Also ready for on-premises to hybrid cloud to multi-cloud environments. We began this journey in 2019, that's when we introduced our first security use case to market. It was a SIEM. In the initial days, we were largely deployed as an augmentation to an existing SIEM, side by side with another SIEM system in a SOC. Over the subsequent couple years, we have added a wide variety of operational features, out-of-box integrations, out-of-box detections, and matured this SIEM product. SIEM went GA in 2020, and as of last year, most of our deployments are either replacing an existing SIEM, not augmentation anymore, or winning new greenfield opportunities. Along the way, we have also added first-party protections on top of the SIEM.
As Ash mentioned, in 2019, we joined forces with a company by the name of Endgame, which formed the basis of our endpoint security offering. We did not stop there. We actually combined that endpoint security offering with the wider context of the SIEM to introduce XDR to market. This went GA in 2021. Many companies talk about XDR. We are one of the few that's actually delivered on true XDR by combining the benefits of SIEM and endpoint security. Last year, we acquired two companies, build.security and Cmd, which formed the basis of the cloud security offering that we launched at RSA this year in June. By cloud security specifically, I mean cloud workload protection as well as cloud security posture management. The SIEM product that we have today is mature, as Shay mentioned.
It is one of the fastest-growing SIEMs in the market. The Elastic Security offering today is an integrated suite that is rooted in our data management and analytics capabilities. The core of our offering continues to be SIEM, and endpoint and cloud security are attachments on top of it. The key point is that these are not point products. We don't sell these separately. We land with SIEM in our go-to-market motion, and then we attach endpoint and cloud security as expansion opportunities on top.
Tying back to our vision and the platform capabilities that Steve mentioned, at the core of it, we practically ingest everything in a scalable fashion, all the way from cloud logs, firewall logs, endpoints, OT logs, and so on, store it in an efficient fashion, keep it searchable, index it and keep it searchable, and use that search and analytics in order to drive both insights and threat detections. Of course, use Kibana on top to drive the investigative workflows. We have had some favorable market tailwinds over the last couple years when it comes to the SIEM market. Today, a huge portion of the SIEM market, which is about $5 billion, is still composed of legacy SIEM systems. By legacy SIEM, what I mean is SIEMs that are more focused on operational features, case management, alert management, and such.
We are in the middle of a migration cycle from such legacy SIEMs to next-generation analytics-based SIEMs, and we are big beneficiaries of that. Most of the names which are mentioned over here, in all of those cases, we actually ended up replacing an existing SIEM. When it comes to our newer use cases, endpoint security and cloud security, we typically win when customers see the value in that consolidated offering, as well as when they want to future-proof their systems with things like extended detection and response. On the backs of those favorable tailwinds, we have grown revenue and security very rapidly. After introducing the product in 2019, we are now close to 25% in terms of total ACV. This was at the end of FY 2022.
IDC estimates put us as a top five SIEM vendor already, and a top three when it comes to growth rate. In order to understand that quantitatively, the value that we offer to our customers, we actually conduct an annual value study. We did one earlier this year with 300 customers, and we found a 90% customer satisfaction rate and an average threat impact reduction of 70%. A separate Forrester study on total economic impact in enterprise customers have found us reducing mean time to response by 10x at lower cost. At the end of the day, our differentiators, much like in the case of observability, are rooted both in the platform differentiators as well as the innovations which we have added on top.
Goes without saying that our data management, our analytics at scale, these are our biggest differentiators, very tailor-made for security. We offer one platform across security and observability, as multiple folks have already spoken about. These are not sold as separate products. We do not charge by use case. Our pricing model is resource-based so that customers can actually start small, they can increase consumption within security, and they can increase consumption by actually moving across use cases. On the security layer, again, we offer a consolidated offering. It's a single product. Don't think of it as three-point products. Together with hundreds of integrations with the third-party ecosystem tools, all of which are bolstered by our open source community as well. We offer unparalleled native protections today.
At last count, more than 700 detections, guided by 60+ machine learning models. Our investment in security continues in FY 2023. On the feature functionality side, we are investing in broadening out our response actions all the way from endpoint security to SIEM. We are rounding out our next-gen SIEM offering by investing in threat intelligence management as well as UEBA, which Ash mentioned. Last but not the least, the cloud security product use case, which we had introduced to market in June, we are continuing our investment in that in order to mature that. Long story short, with these innovations and the platform differentiators, we're well on our way to execute on that vision which I'd shared a few minutes ago. Not to mention cement our leadership in the next-gen SIEM market.
I'll now invite James Spiteri from the security team to show a little bit of the product.
Thank you, Santosh. Hi, everyone. My name is James. I'm here today to run you through our demo for Elastic Security. We'll mainly be covering three topics: talking about how the team at ACME, which Baha was mentioning, are able to search, detect, and investigate threats thanks to our ability to search over unstructured data. Further expand into other use cases such as automation and prevention. Finally, we talk about how our open and transparent approach to security is allowing them to reduce risk and tackle the cyber skill shortage we're all very well aware of. As Baha was mentioning earlier, ACME Financial are covering and monitoring all their distributed systems, thanks to Elastic. They've struggled in the past to find a platform which can both observe those systems, secure, and protect them as well. Well, now with Elastic, they're able to do that.
Even better, they're able to do it with one agent. Whether they're doing APM, whether they're doing container monitoring, whether they're doing malware prevention, we've been able to consolidate all those solutions under one platform. What you're seeing here is actually the notorious Log4Shell vulnerability which hit last December, impacted thousands of organizations, ACME included. This made this really evident. It was one of those attacks which really showed the value of having something like observability and security in one platform. Whether we're looking at the APM traces as the exploit was happening, exactly what happened within our application, and then the outcome of that exploit inside of Elastic Security, what was that exploit able to do and what were we able to detect? It's really valuable that the team had this at their disposal.
In reality, the only way we're able to do that is thanks to our ability to search over unstructured data. Security events are the epitome of unstructured data. Elasticsearch is the ultimate platform to be able to do that. What you're seeing here is that same search that would have been needed for ACME to spot that Log4Shell activity. The search string itself is pretty complex. Over other systems, it would have taken hours to return results back. Within Elasticsearch, we're able to get results back in seconds. Also in the screenshot, you can see the almost non-human readable data it was searching over, but we were able to spot it with ease either way.
Now, dealing with all this distributed data over multiple cloud providers, as we saw earlier, from Baha's presentation as well, how do we allow teams to do that efficiently and cost-effectively? This is where our cross-cluster search feature comes in. Traditionally, to be able to do this, especially between working with multiple cloud providers, you'd have to bring the data in from one provider to the other to the next, which costs thousands in cloud egress costs. Well, that goes away with cross-cluster search. Whether the team at ACME is looking at one cluster or 10, it doesn't matter. The data stays where it is, but we centralize that view under one Kibana instance. Couple that with our auto-scaling capability, not only do they not have to worry about where their data is, but they also don't need to worry about how much of it they have.
Now, to the end analysts at ACME investigating all these events, we've provided them a way to do that in a very streamlined fashion, something we call our timeline. This is the ultimate way to do an investigation within Elastic Security. It has tailor-made views to present the data to the analyst, depending on what type of incident they're investigating. It's also very collaborative. They can leave notes, interact with other colleagues, and again, the best part about it is whether they're looking at one cluster or 10, the timeline doesn't really care. It does all of that work for them. Most of the time, they don't even have to type in a query. There's all pre-built views ready for them to use from day one click away. Now, that's the SIEM use case, right? That's the security analytics over unstructured data.
What about expanding into other use cases that ACME needed? What about instead of just being able to detect something like Log4Shell, what if we can help them just automatically prevent it? This is where our automation capabilities come in. Those detections that were mentioned previously, the 700+ detections which we provide, we can allow teams to have an automated action assigned to them. If they want to interact with any system that has any programmable interface to do that, they can do it with ease. Perhaps craft very refined tailor-made messages to interact with something like Slack. That's not all. The team at ACME also has thousands of users that they need to protect. Phishing is still one of the most prevalent ways for an attacker to get into a system.
Well, because ACME is using Elastic, they have our endpoint security capabilities natively as part of their existing subscription. All they need to do is turn that on as part of their agent policies. Now, instead of just being able to detect something like a ransomware attack, they can prevent it from even running in the first place with a unified agent. What you're seeing here is just that. This is a malicious Excel sheet being prevented from running after being received via a phishing email. Now, what about when the team has to collaborate with other members? We have case management for that. It's a very intuitive experience, very interactive as well, but it also allows team members to interact with other members across the organization because we allow them to synchronize with tools like Jira and ServiceNow.
For the non-technical folks at ACME, we can present really rich reports thanks to our dashboarding capability. Again, powered by that search over unstructured data, just a click away. Now, with all this data, if the team at ACME isn't careful, it can get very expensive from an infrastructure perspective. We've built in really easy ways to manage the data life cycle and use our data tiers. Within a few clicks, they can adapt and say how long they'd like to have that data live within their clusters and how quickly they want it archived. Within archives, in the past, the team at ACME have found it very hard to actually use them and operationalize those archives. It was a very time-consuming and costly exercise to bring in data, let's say, from two, three years ago to search it actively during an incident.
With Searchable Snapshots, this goes away because that data that's living in very inexpensive storage, usually cloud-native block storage, is all searchable. What you're seeing here is a screenshot of hundreds of billions of security events taking up zero bytes of disk space, but are all available, represented by that green status. Now, all these features are great, but if we can't find the right staff due to the cyber skills shortage, that's not gonna help us. At Elastic, not only do we provide those detections built by industry experts, we also provide investigation guides. If one of them does trigger, here's exactly what you need to do to investigate it, and we can tailor-make it for Elastic. Click here, do this. We don't stop there. That same team also publishes regular research articles.
If a new malware variant or a new attacker group comes out, here's what the Elastic team has found. Not only does this help the technical users, but anyone looking to buy Elastic can reference these articles to see, you know what? Elastic has really rich expertise in this area. There's no question about it. All our detections and preventions are built in the open, so you no longer have to question what the vendor is giving you. It's all right there, as Ash mentioned at the very beginning. 100% transparent, no black box approaches. Here's exactly how we're helping you get protected. Hopefully you can see how the team was able to reduce risk, complexity, and cost with Elastic. This slide is very similar to Baha because of the same unique platform differentiators.
As Steve mentioned at the beginning, we build one feature for the platform, it can be used across all three solutions. Things like integrated machine learning, limitless retention with Searchable Snapshots, but then you couple it in native endpoint protection and our open, transparent approach to security. I'd like to hand it over to Matt now to talk all about Enterprise Search.
All right. Hi, everyone. I'm Matt Riley, General Manager of the Enterprise Search Group here at Elastic, and very happy. Can you hear me okay? Very happy to be here to walk you through this portion of our business today. So the enterprise search use case is the original use case of Elasticsearch. Going all the way back to the first recipe application that Shay built that spurred the development of the product in the first place. But even back then, it was clear that this is a very diverse use case, and the need for these search-powered applications was really growing very rapidly. For the past few years, when we've talked about this space, we've typically described it in two broad categories.
There's the App Search use case, which is kind of referring to consumer-facing search applications like the product search on an e-commerce store. Then there's the Workplace Search use case for employee-facing applications over internal company data. The reality is a little bit more nuanced than that. If you look at any successful SaaS application out there, App Search powers the internal search capabilities of those products. Customer support applications typically span across both sides. There's a public-facing component for, you know, public-facing knowledge bases and consumer-facing help centers, and there's also the tooling on the back end that helps the internal customer support teams be more efficient when they're answering phone calls or answering chats.
Beyond that are examples like Uber and Marketplace Search, where they built entirely new consumer application and experiences, but in this case, based almost entirely on Elasticsearch and some of the geo search capabilities that we make possible at such production scale. Altogether, we refer to these as search-powered applications, and the goal of the enterprise search solution is really to put the tools in the hands of our customers so that they can build whatever one of these applications fits their business and use cases that they have in mind. I'm gonna take you through a couple of examples of what this ends up looking like in some of our successful customer engagements. The first example is a leading home supply retailer.
When you think about their business in relation to Elastic, you probably first think about the product catalog search, the e-commerce search that's the front and center of their website and mobile application, and really drives a lot of the business that they have there. This is a very critical application for them, and it's a use case where we've had a lot of success in the past. It's something we're very well known for. One of the reasons that we've been so successful here is because not only do we let you index some of the content of the product catalog, we let you index every piece of content in that catalog.
With the part IDs and the names and the descriptions and all the different components that you need to have indexed in order to build a relevance model that can return relevant results for human-type searches. For the example shown in the screenshot here, knowing that DEWALT chop saw in that query, DEWALT is actually a filter on the brand category, and chop saw is actually a name, a nickname for what's typically called a miter saw, and it's probably called a miter saw in the actual product description itself. Making sure that we can help our customers build relevance models that can take a query like that from an end user and actually return the right relevant results is one of the reasons that we've had quite a lot of success in this space for some time now.
That's not the only application that they have with us, and if you look a little bit deeper, they look a little bit less like what you think of as traditional search applications. Their quote center internal to them is powered almost entirely by Elasticsearch. Their order management system, where they can search through and manage the orders from both brick-and-mortar retail locations and the e-commerce store, all powered by Elasticsearch. That system is actually larger by data volume than what you see in the product catalog itself. Stepping a little further from traditional search application would be something like a leading pharmaceutical corporation where we power internal applications that help their scientists do drug discovery more efficiently. They have a ton of research data stored in a wide variety of different repositories internally at the company, all of different types, right?
Different types of unstructured data in different formats, all pulled together into Elasticsearch. On the ingestion side, they're using our machine learning models to enrich that data, that text that comes in as unstructured format to identify certain aspects of structure that we can then leverage on the outside to build more and more relevant research results and more precise searching applications. These are just a couple of examples of the kinds of varied applications that people are building with the Enterprise Search solution. I wanna highlight that really it's the diversity of the Enterprise Search use case that it makes this part of our business so strong. The fact that Elastic is so well-positioned to capture all of these applications across these different customers is the reason that it's something that really sets us apart against the other vendors in these categories.
Rather than approaching just the app search use case or just the workplace search use case, we've built our tool in a way where we can capture both of those use cases, as well as all the others that are in between in that space that we think is very dense with a lot of opportunity. These use cases may look a little bit different on the surface, but there are common fundamental attributes, which is why we are able to serve all of them. First, as we've talked about a lot today, they all deal with unstructured data at scale. And in the case of search, it requires real-time search and discovery. Consumer-facing applications built on top of search need search results very, very quickly, and they expect that, and they go to massive scale in these production applications.
Finally, the last point here is that all these applications are ultimately built by software developers. We need to build the right tooling and the right kinds of components that make those developers successful in inventing the applications that are right for their business. We think about all three of those things when we think about how we can maintain success in the search space. We have had a lot of success in this space, in the past, like I said, as the original use case of Elasticsearch, but we haven't stopped making investments. The Prelert acquisition formed the foundation of a lot of our machine learning capabilities. The next acquisition was a company called Swiftype, where we really stepped into providing a layer of simplicity over all of the power and flexibility of the Elasticsearch platform.
Developers today are looking for simple experiences. They want things that are easy to get up and running with and that are powerful from the start, but don't want to have to, they don't wanna compromise in the power and flexibility. More recently, you see some of our investments in machine learning capabilities with machine learning model management and with vector search. The model management portion here is actually pretty interesting. It's a good example of one of the very fast investments that we made to make sure that we're staying on the cutting edge of what people are coming to expect of search experiences and the search relevance models that drive them. With machine learning model management, you can now bring a transformer model directly into Elasticsearch and perform inference on that model in your Elasticsearch cluster.
Transformer models didn't even exist until about 2018, right? We very quickly saw that a lot of the research that you see from transformer models, like large language models and a lot of the things that you see even in the news today, these things are rapidly progressing in building entirely new capabilities in natural language processing, and they're heightening the expectations that consumers have of their ability to search. Previously, a lot of these kinds of technologies were siloed into only the largest search companies in the world. With machine learning model management, we're bringing a lot of those capabilities to our entire user base, our entire customer base. Vector search is really our most recent investment here, which is how you take all of that, ML model management and operate it at scale.
This continued investment does take a lot of work, but we are very proud to see that it has continued to be validated by the rapid commercial adoption of customers across industries, across company sizes, and across all the application use cases that I described. A few of the key product goals, which we've talked about a bit already, that we keep in mind as we go through and make these investments. Obviously, ease of use is probably one of the most critical aspects of it. In something like search that is inherently kind of complicated, we wanna make sure it's as easy as possible to get started and get you up and successful very quickly. But the fact is, most search use cases ultimately need some level of customizability. They need flexibility and power.
Finding the right balance of making it easy to get started but not restricting you, once you're there or as your use case matures, to let you continue to dive down and get deeper and deeper into the technology to build exactly the application you need, it's an interesting line to have to balance, but it's something I think we're doing very well today. The last product goal is to maintain our technology leadership. A lot of the investments that we make, like vector search, for example, there's a lot to be built there. We're building the foundational implementation of this top to bottom, and we do the hard work of making sure that when we do build these things, we build them in the natural way that they should exist inside of the Elastic ecosystem.
By that, I mean rather than taking something and bolting it on to the side of the product to be used or to check a box for a particular use case, we think of things like vector search as fundamental capabilities that have to work with all the other parts of the ecosystem. Making sure that vector search works immediately out of the box with cross-cluster search or with our Searchable Snapshots implementation. If we take the time to do these implementations the right way, our customers benefit from the power of the entire platform instantly. Why we win. Our established leadership in search has created an enormous amount of adoption, the 3.6 billion downloads that we've talked about a bit today.
That has also created a very valuable and vibrant ecosystem of passionate community of developers, people who build integrations and complementary tooling, and people who are working at companies today who already understand the product and know how to utilize it in various ways. All of this is important right alongside making sure that we continue to deliver world-class relevance models, which today features full-text search, vector search, and hybrid search, which is actually the best way to get the top performing search results, at least as of today. All of this is buil on top of that same platform that everyone else has touched on a bit as well, but I wanna touch on a couple of the things that are unique to us here. The flexible pricing and licensing model.
When we're working with developers often, it's great for us to be able to let them adopt the product as easily as possible, whether they're downloading it or trying it for free on cloud, they can get going in just a couple of minutes. We also have a very sophisticated cloud platform. It's not just in a single cloud service provider. You can get Elasticsearch on all three of the major cloud providers, and many of our customers are working into multi-cloud environments and have multi-cloud strategies right now, and we have to match them where they're moving. Finally, a look into what we're investing in in FY 2023. It could probably be best summed up by just continuing to anticipate the needs of the developers who have been building on Elasticsearch for so long now.
That starts with making sure that Elastic Cloud is the best platform for building search-powered applications online. It means continuing to invest in the more sophisticated use cases like machine learning model management and vector search that people are coming to expect from platforms like this and that they need access to in order to satisfy their own customers and their own customers' expectations. Finally, the last pillar here is kind of an emerging opportunity. When you think about how ML models work and what makes them better and what makes them get more and more precise over time, it's all based on the user behavioral data that's collected around those applications, and we're making sure that we're capturing that and making it really easy to leverage inside of these relevance models that we're helping our customers build.
I think that's a great, jumping-off point for our demo, which we'll go over that and a few other things.
All right, let's land this plane for this product section. Jonas Lavoie, Director of Product Lead for Enterprise Search. Well, let's just explore the power of search here together. Perhaps the best way to do that is to look at the two key angles that people will evaluate search from. First of all, we'll have the end user experience. You'll have applications where search is consumed for either discovery, analysis of content, and we'll have the behind-the-scenes experience, the management interfaces for the business decision-makers who pull the strings. All right. By now, ACME Financial should feel pretty familiar. This is the kind of application that an end user would use on a daily basis to proceed with trades.
This is obviously an environment in which search can be dramatically useful, but also not only that, pretty much expected, right? Now, if I were to go down in Union Square and ask people to identify the search components on this page, I'm pretty sure the answer I would get is something like this. That's not a bad answer. I think that's how we've been trained to think about search. The way we think about search at Elastic is a little more something like this. We think that companies can actually rely on the power of search to surface information and power discovery experiences using the power of search. Going from application search to search-powered applications. That makes sense, right? Search is inherently about flexibility and personalization.
It's about taking large amounts of unstructured data, structured data, and bringing it in front of users in milliseconds, making sure that they can better understand the information at hand, making sure that we can digest, structure, and synthesize all that information. Then on top of that, we can take into account their own personal preferences, their characteristics, their behavior to really surface the most relevant experience in the most personalized of ways. In many ways, search, in fact, has become the backbone of the modern application experience. Let's go over a few of those search components that we highlighted a little earlier, and let's see what's so specific about them and so special about them. Well, I don't think it's a surprise to anyone here, but search is uniquely powerful at making sense of data that comes from a variety of different places.
In this case here, we have a database of stock, which considered structured set of data. But we also have a lot of unstructured data coming from a variety of places. For example, a rapidly evolving feed of news. That's unstructured. Help center, unstructured data, lots of content, a lot of text, a lot of evolving context over time. We even have a document repository in the form of resources. To be able to make sense of all that information that comes from all these places and to provide a relevant search experience is unique to Elastic's pre-tuned search engine relevance, right? Right out of the box, you get some relevance. You don't have to tune anything. But if you choose to, we'll give you the tools to do that.
Just on top of that pre-tuned relevance, though, there's a number of quality-of-life improvements that we can provide. For example, my favorite one, typo tolerance. I have a tendency of typing pretty fast on my phone. That comes out of the box. So you can immediately account for people making typos as they're typing through. Same goes for things like query suggestions. How many times have you tried to look for something and would really have loved to have that little help to get you to that answer that you're looking for? Again, all of this comes out of the box with Elastic.
It's not only just about the data that we make searchable here, it's also about the data that we track about that user, whether it's a search, whether it's a click or their behavior across the platform, all of that natively captured along the way. That allows the technology to do a couple of very interesting things. The simple ones, well, we can store your search history. If you issue a lot of the same queries over time, they'll appear at the top. We can also identify global and local trends so that we can help you understand what's happening in your field or in your general geography, for example. That same data is actually much more powerful than just for these small quality-of-life improvements.
When you think about the data that we capture for those users, it will help inform a lot of the content discovery experiences that we can create. This is also where Elastic's core commercial investments in machine learning and vector search are gonna come into play. Now, you've heard vector search a number of times. If you're not familiar with it means going beyond character and word matching and using fancy math powered by machine learning to provide even more flexibility and even more relevance to the queries that are being issued. This is where Elastic's approach, by the way, is most differentiated. We really wanna put the search experts, the data scientists, the ML experts in the driver's seat here to make sure that they can bring their knowledge, that they can bring even their own pre-trained machine learning models for ultimate accuracy.
Again, you heard the theme of no black box. This is yet another area where we make sure that there is no black box type of scenario when using the technology. These exact same building blocks, well, we can use them other places. For example, customer support. This is a pretty typical use case. We wanna make sure that we're able to quickly answer questions as they're provided by our users on ACME without having to involve necessarily a customer support agent if we don't have to. Question answering powered by machine learning is something that we've been talking about for a very long time, but is now very practically capable, thanks to search.
With all of this talk of leading-edge technology, to call it that, it's also important to remember that Elastic's claim to fame is to be able to handle large amounts of data, sensitive data in just a few milliseconds. A key example of that would be something like transactions and orders that are generally stored in databases that are very critical to the business. They're usually in the dark corners of an IT infrastructure. Same goes for mainframe computers, for example. These systems are not designed to be consumed publicly. This is where Elastic and Elastic Cloud specifically shines, the ability to provide a highly architected and a highly available infrastructure for public consumption so that people can have access to their own wealth of data in a very safe, secure way.
Again, in finance and financial data, this is extremely important, obviously, and all of that at a petabyte scale. So if we were to describe the purpose of Elastic in this case would be the perfect interactivity and the perfect speed layer to access this data. Now, we spent a lot of time thinking about the end user and how they experience that search, but I want us to spend a little more time thinking about how the business decision makers, the search experts, are able to pull the strings from behind the scenes. I don't think it will surprise anyone here to know that it starts with data.
With Elastic Cloud, teams can go from zero to searchable data in just a few minutes, thanks to the hundreds of integrations that you've seen before in several slides, but also thanks to the Elastic Web Crawler and a number of APIs and clients that allow you to ingest that data in just a few minutes. Now these integrations, what they do is they synchronize the content to make sure that it's always up to date. They'll make sure to structure that content, so that it's very useful for content discovery. All of that without complex configuration or even code-level intervention.
If we use the example of the Elastic web crawler here, we can siphon in all of the content from our education center at ACME and immediately make that content available within that search bar that we looked at just a second ago. It's extremely powerful. It all happens behind the scenes, and no need to go and re-implement or redeploy any application. All of that happens at ingest time and at query time. We also talked about the fact that we are ingesting a lot of content about those users. That's extremely helpful, obviously, for automated systems such as machine learning, but it's also very helpful for humans in the loop, the experts who understand their markets very well and wanna be able to make decisions not only about their search experiences, but also about their businesses. Understanding trends, for example, and taking action on these trends.
If you wanna understand, for example, a high volume of queries that don't return any results, or you wanna understand why in certain geographies, for example, this query term is very popular. There might be something going on there. Is that a business opportunity for us? We can take action based on this data that we capture automatically. Now, one of the ways we can take action here is by tweaking the search relevance. If we see, for example, that there are a number of query terms that are not returning results, or we feel like people are not necessarily engaging with the content that we want, we offer all of the tools of the trade to allow search experts to go and modify these things, either one query at a time or very globally. This is an area we call relevance management.
We also get help from the system, where we can use the same data and let the system suggest some of those changes without having to necessarily go through the full analysis flow manually. What I can do here as an analyst is I can look through the suggestions, choose to accept some of them, and I can even choose to let the relevance algorithms go on autopilot, which is very specific. This is what we call adaptive relevance. Last but not least, as search continues to be one of those mission-critical experiences, it only makes sense that we can use the power of things, the tools that are available, thanks to Elastic's observability, to really monitor the health and monitor the performance of all of the search experiences that are interfacing directly with our end users, right?
We wanna make sure that we understand if in specific geos, there might be a little bit of performance issues. Can we go and fix that? 'Cause we know that will have a real impact on our users. Perhaps specific devices. All of that, thanks to APM, Real User Monitoring, as well as Uptime. That, to me, that's the real power of the Elasticsearch platform. Putting it all together, just make sure that we're all seeing the same things. It all starts with that one data platform where we can ingest very quickly all the information, make it discoverable for our end users. All of that happens at a scale that it's at a petabyte scale, enterprise-ready scale, speed, scale, relevance.
Really that last one, relevance, being the key where we're making a lot of the core investments right now around vector search and machine learning, and we know that this is the way of the future for us. With that, it's my pleasure to reinvite on stage my friend, Shay Banon.
Thanks, Jonas. I'll wrap up the product session here today. We're a bit ahead of time, but I hope you get a chance to actually see our products in action. I hope you agree at least with my questions on how mature these products are. One of the things that I really like, and we try to convey that story, is how holistic the journey that our users take from observability to security to enterprise search and vice versa. All of that is powered by our single platform. We get a chance to see it every single day with our customers, and that makes me extremely excited. It means that we deliver value to our customers many times in an unexpected way.
If I had to characterize one of the reasons why people love Elastic and love Elasticsearch, it is that we keep surprising our users with things that they didn't expect. They adopted us for one thing. Anyhow, we're gonna have a break now for 20 minutes. We'll get back at 3:15 P.M. Pacific here. Thank you very much, and see you soon.
Please welcome Elastic's Chief Sales Officer, Michael Cremen.
All right, folks. I'll wait a little bit so everyone could get their seats, please. Not a bad view out there, huh? All right. Well, look, good afternoon, everybody. It is great to be with all of you. Again, I'm Michael Cremen, I'm the Chief Sales Officer here at Elastic, and I'm joining you just after a tour of Asia Pacific. Over the past two weeks, I was in Japan, I was in Thailand, I was in Singapore. Before those two weeks, I was in Washington D.C. with our U.S. Fed team. The week before, I was in Chicago with our U.S. central enterprise team. I had the opportunity, as I always do, to meet with a lot of customers, many partners and of course, a lot of time with our teams who are local, who are really serving those markets.
I bring that up to you just because there's so much insight I get from those trips and the time I spend in the field, and I've peppered a lot of it in this presentation. You'll hear from me for sure on a lot of it. Look, what I wanna do is I'd love to talk to you a little bit about who I am, where I come from, what my mission and focus is here at Elastic. I also want to talk to you about my perspective on the opportunity in front of us and how we're gonna go capture that. When I wrap up, I'm going to turn things over to Rick Laner, our Chief Customer Officer. He's gonna host a customer panel I think you'll really get a lot out of.
Thank you so much for the customers who have joined us. Let's get on with it. Look, who am I? I've been in the industry 25 years now. I've had a lot of quota over those years. You can tell by the gray hair. I started out selling mainframes to Wall Street firms out of New York City, obviously. I moved into management progressively, first line management, second, third, then I ran theaters and then global businesses for some of the companies right there. I have lived and operated all over the world, New York City, the Bay Area, Hong Kong, Singapore, Sydney. I spent a lot of time in the Middle East. I like to say I have international experience and local knowledge.
I've been in data pretty much from the start or close to it. I led teams and ran businesses for Hitachi, for IBM, for Veritas, with a very aggressive growth motion. Most recently, I was Chief Revenue Officer for Cohesity, a private data management company, where I established an enterprise-grade go-to-market, something that I'm doing here at Elastic as well. I've been here for a year now, and I'm really proud to say that a lot of what we've been doing over the last year is really demonstrating impact and big results, and you'll hear more from that for sure from me on that. Look, first thing I wanna do, though, is let you know how excited I am about our opportunity moving forward.
I think and everything you've heard so far hopefully convinces you. I think we have the greatest opportunity in the industry as Elastic. From the first day I arrived at Elastic, really understanding our strengths, three really jumped out at me. Our market fit with speed and scale is unsurpassed in my view. I look at the relevance and applicability of Elastic with our customers. I think it's tremendous. The adoption and the consumption of our cloud has been amazing, and that ramp continues, and really excited about that. These three strengths, to me, have really helped us establish some traction in the marketplace. Let me talk about that a bit. Let's look at our customer base. We've grown our customer base year-on-year, 22%. Over the last two years, 64%.
You can see there, Q1 has not slowed down, which is great. We're excited. We're so thankful to have such an incredible set of customers around the world across industries. We've made it clear to the market and all of you that we're pursuing the enterprise space even more heavily, and there's a big focus on the enterprise segment around the world. You can see there, on the charts how we're doing with customers that spend greater than 100K with us from an ACV standpoint. I think that's a testament to how we're doing. That's both commercial and enterprise. But then if you look, and you can see that we've grown, our customer base that are doing more than $1 million with us each year, 50% year-over-year. Really happy and pleased with that.
Again, I think that really is a good testament to the traction that we're getting in this space. I'm gonna talk a lot more about how we're continuing to invest and pursue in that area. Let's talk cloud for a minute. I talked about all my travels and at lunch, some of the folks were asking me how much time I spend on the road in front of customers, and it's at least 50% of my time. Some of the heavier months, it's more than that. Get a lot of insight, really talking to customers. There's three major areas that I thought I'd just share with you that I hear on a regular basis. The first one is the disruption to labor and the talent war that's going on out there.
I have so many customers say to me, "Look, I'm really struggling to keep and retain my top talent. They're being wooed away." It's very hard to acquire new talent. There's a lot of mobility right now with regard to the labor market, around the world, across industries, certainly in the IT space of any industry. Another big one is, look, infrastructure is pretty hard to come by. You look at the lead times that are in place and just continuing to grow around hardware, around compute, storage, network. They're intense. You know, this idea of business as usual is kind of gone, right? Over the last couple of years.
There's a huge focus right now to make sure that there's an established set of capabilities for remote working, again, in any industry, and all the initiatives that come along with that. One aspect that's really important is making sure that there's no security gaps with those setups. All of these reasons I hear constantly, and to me, it's certainly important to know that that's what's really driving a lot of our customers to our cloud. They're getting a world-class experience, of course. They are really looking for agility in terms of deployment, simplicity, scalability, and ease of operation. What customers tell me is, "I love the fact that I get to think about what my expectations are and what I wanna get from the Elastic Cloud.
Instead of spending a lot of time with my staff, focusing on ordering, procurement, of hardware and infrastructure, looking at these big costs, having to rely so much on the labor, having to find data center space, configurations, implementation, et cetera. Elastic Cloud makes so much sense for these customers. You can see here how it's growing in terms of percentage of our overall revenue, and that trend continues. I think Ash showed it, but basically in Q1, it was 39% of our overall business. The other thing I wanted to mention was our new logos. More than half are moving directly into the cloud, which is awesome. Again, another testament of how we're doing from a cloud perspective. I thought I would talk a little bit about themes around why we're winning out there. It's competitive, it's dynamic.
There's three areas in particular that are just bolstering up our win column for sure. I know you are somewhat familiar with them, but differentiated value first, cloud second, and then just our open heritage. This differentiated value. Look, you've heard a lot this afternoon around we have one platform. That ability to have a single platform and not an amalgamation of products bolted together with all different pricing schemes, I think Steve hit it pretty well. That is a huge benefit to our customers when they're working in such a dynamic environment, and they're able to leverage world-class technology for search, for observability, for security, and use them in a way that there's simplicity when they're looking at leveraging this platform, so for so many different things.
Being able to do so in a way that instead of focusing so much time on what are the pricing schemes, tell me how you do this, is that product different, we can talk with them about really propelling their business and getting them faster time to value. That's a huge differentiation for us with our customers in the marketplace, certainly versus our competitors. Our cloud, I just talked about the benefits of it, but the fact that we have the big three hyperscalers, quite frankly, driving our business, making it available in terms of Elastic Cloud in the marketplace. By the way, in addition to the big three, we have Tencent and Alibaba from a China and APJ standpoint, presenting and offering Elastic Cloud. Really, really big advantage for us.
Our customers can buy directly from us, they can buy from our partners, or they can buy right off of the marketplace of these hyperscalers, and they can draw down the commitment that they've made buying Elastic. It's fantastic and it's really conducive to, again, what customers are looking for. They want speed, they want simplicity, they want scalability. They wanna know that they can streamline their operations and security right out of the box. That rapid deployment is exactly what Elastic Cloud is. Then our open heritage. The fact that we're an open source company, we're able to build out this world-class community of fan base that have done so much innovation with our technology over the years in so many industries around the world.
What they're able to do, especially in this mobile world right now from a labor standpoint, is bring Elastic to their next company and their next company. It's a big advantage for us because we're able to, in a very, very efficient way, move and land into so many different customers in so many different industries and so many markets around the world. Look, this is what we do at Elastic, and this is why we're winning. This is maybe my favorite topic, our go-to-market, and I just think about the opportunity we have to truly scale. I've talked to many of you already about our ambition to really scale out in the marketplace, and it's certainly part of my key remit, why I was brought into Elastic.
What I tell you is there's so many areas, but the three I really wanna focus on are customer base, the sales force, and then as I just talked about a little bit, the ecosystem. You heard Ash talk about the 400+ customers that spend over $100K with us that are utilizing us for more than one use case. That's great, and that continues to grow, and that's important. You think about the 19,300 customers that we have and how many of them are actually utilizing us for a single use case. That's a tremendous opportunity for us to bring more value to our customers, and we're absolutely focused on doing just that.
We're working with our teams to help them understand more about the way the customers are using our technology, what their business needs are, giving them the tools and the data and capability to really help expand. I'm gonna talk a bit more about that in a little bit. The other one is the sales force. Look, we have a great sales force out there. It's world-class in so many different ways. What we're doing is we're continuing to evolve the sales force to meet the demands of today's market and also to capture the opportunity of today. It's a really, really important aspect for us and one that I'm very focused on in terms of ex-U.S. expanding the sales force. It's happening all over the world.
Also bringing in talent that is extremely experienced in cloud and observability and security, as well as the enablement and training that we're doing with our existing sales force to really take us to the next level. The investment we've made year on year in overall go-to-market is considerable, and that investment will certainly continue, and the results are showing that we should continue that. The ecosystem. Talk about a lever for scale. These partners are begging to do more with us. I would tell you that, you know, I mentioned I was just in Asia-Pacific. One of my first meetings was with the leader of AWS for APJ. My last trip, I met with the same position for Microsoft for Azure. Previously to that, at a corporate level, I was with Ash at Google.
They wanna meet with us. They wanna talk to us. They love what we represent, and their teams do as well in terms of driving consumption in the marketplace. In addition to the hyperscalers, which is going very, very well, taking a look at MSPs and MSSPs, they love the simplicity and scale of our platform, and they're building it in their offerings in the marketplace. We have GSIs that are wanting to build practices around Elastic. I'm also spending a lot of time right now with our geo leaders. As we look to where we're gonna expand, specifically in emerging markets, that's a channel play. We're spending a lot of time continuing to build out our overall channel partnerships in these areas so that we can improve coverage, drive efficiency, and scale our business. Look, where does that take us?
We've talked about this publicly, driving to $2 billion and beyond in revenue in fiscal year 2025. Look, the growth is there, but we need to, from my standpoint in the field, we need to focus on three distinct areas that are gonna really help propel us to that $2 billion and beyond. First one is we have to continue to grow in the cloud. That's absolutely the focus and the priority. The second one is, look, we have to make sure that we expand our footprint in the enterprise markets around the world. It's so important for our business. The third one is, with this platform, we absolutely have to help our customers consume well, consume more, bring a new level of value to their business, and help expand inside and span across different use cases and aspects of our platform.
We do this right, and we're in such a tremendous place, and it really is right there for us. Let me start with cloud. Let me start with how we continue to accelerate our cloud business. Look, we've redefined our go-to-market based on cloud, and I can tell you right now, our new customers are using our cloud. They love it. Again, it's all about speed. It's about simplicity. It's about the experience. Really, really important for us. Our existing customers, in many cases, have taken a hybrid approach, so they're leveraging us in a self-managed on-prem scenario, but also bursting or scaling into our cloud for a variety of reasons. They may wanna be focused on new workloads. They may be looking at new areas of their business.
They may look to be storing data in a more cost-effective scenario in our cloud so that they can really drive more business decisions with the insights they could derive from that data. Really, really important for us. I would tell you that another aspect is the onboarding approach, and you're gonna hear again from Rick Laner a little bit later with our customer panel, but Rick Laner and our customer success team are incredibly focused on onboarding our customers, really building a world-class experience for them, and it's all about a warm welcome. It's about helping to assist in adoption and ramping inside our cloud to really bring fastest time to value possible to our customers. Look, the sales motion for us. We lead with cloud. Let me be really clear. We're out there in the marketplace leading with cloud.
We have acquired talent coming in from an AE and an SA and a CSM standpoint that are heavily experienced in cloud. We also have done a big lift and a lot of investment around the education, around the development, around the certification of our teams from a cloud perspective, so they can get out there and really have robust conversations with our customers, working with our partners to bring more value with our cloud. The ecosystem, again, talk about a leverage point. The relationships that we've built with the ecosystem, with the hyperscalers in particular, I'm really proud of. They're deep-seated from an executive standpoint. The commercial agreements we have in place and the arrangement in terms of how we all pay our sales forces and the joint motions we have in the marketplace are going incredibly well, incredibly well.
These hyperscalers are viewing this partnership with us as extremely strategic. We've won Accolade. They put us in their special programs. They view us as one of their absolute top vendors, and they've said that and validated that specifically. Once again, I can go to pretty much anywhere in the world, and I can meet with the top person in these hyperscalers, talk about marketplace, what we're doing, make sure we have good alignment in the field, and it's rolling. We're able to drive significant consumption, which their teams love and are paid on and are really focused on. They love it because Elastic grows. When Elastic goes into those accounts based on a marketplace purchase, the organic growth is substantial, and they see it, and it's very conducive to their goals and what they're doing.
Now, the other great thing is our customers get to draw down by buying Elastic through the marketplace, those big commitments that they made to them. We see that every day of the week, where we'll be working with a customer, driving the value of Elastic, and they'll be talking to us about, "Hey, can I procure this through Marketplace with Azure or with Google?" Absolutely, you can. We have these great partnerships. There's very little friction, there's speed, and we're able to actually reduce sales cycles, which is conducive to us, and we can scale pretty significantly with these partnerships. The other thing is, I talked about the MSPs and the MSSPs. We're doing really well with them. They love the technology. They're really building it in their offerings in the marketplace.
The other thing we're doing is we're spending time with GSIs, as I said, working with them to build practices around Elastic. There's so much in it for them when it comes to the services they can deploy around Elastic and really drive an even broader value proposition to their customers. Finally, it's so imperative that we continue to build out this channel that we have, where we provide an optimal level of coverage. We have many more feet on the street that are driving Elastic out there for us in different markets. That's truly how we scale. All of this is conducive into what we're doing around cloud and driving this cloud acceleration. I thought I'd give you an example of a customer who started out small in our cloud. This is a large enterprise financial company.
They initially wanted to do some log analytics with a legacy mortgage application. You know, it went really well, and they asked us to actually continue with this expansion on the platform and replace their SIEM vendor. We did just that, and you can see there how we grew just a matter of a year. After FY 2021 or within FY 2021, they did two acquisitions, and they brought those environments into Elastic. The experience was so great. They've asked us now to take a look at replacing their APM vendor, and then subsequently, they want us to look at replacing their endpoint vendor. This is just a great example to me of a customer that starts out small in our cloud in the enterprise space and grows dramatically. There's so much opportunity.
Again, back to the one platform, it really helps customers to understand all the value and the different offerings that we can provide from this platform. They look at this consolidation, they look at competitive displacements, and we're driving an efficiency that's conducive to their business goals. Let's talk enterprise focus. This is a big one for us. It's a really important one for us. What I wanna do first is just explain from a field standpoint how we're clearing the way, if you will, to really establish this level of focus. One thing I wanna say, by the way, just about the overall go-to-market is, when I arrived at Elastic, we have a great foundation and fundamentals. I'm standing on the shoulders of the leaders before me, from a CSO standpoint.
What we're doing is we're taking it to the next level. We're accelerating and we're evolving it. What's important is my job is to make sure not only are we accelerating for today, but also really evolving, rapidly evolving in some cases, how we're going to market and how we're establishing ourselves for the future. That's the key here, and that's what we're doing when we look at how we went even deeper from a segmentation standpoint. We're truly running our business now from SMB all the way through to not only enterprise, but large enterprise and public sector and commercial, and establishing teams and partner ecosystems that are conducive to all of those segments. We're also. We've stood up, actually, multiple large enterprise regions strategically, and they also have a vertical spin to them. In the East, obviously it's banking and finance.
In the West, here in the U.S., it's high tech. We have telco across Western Europe, and that'll continue in terms of bringing more vertical expertise and focus to the large enterprise. That helps us drive deeply into the enterprise segments. Look, the other thing, as I said, establishing everything to create focus. We call on SMB, and it's a very important market for us. But the way that we do it is through inside sales reps, lower overall cost, more efficiency. We've leveraged technology to create automation. That allows me to then free up assets, resources, budget to double down, as I said, in this large enterprise space where we go much deeper into the enterprise segment. We built teams out that are purpose-built for the enterprise.
We may have dedication to one or two or maybe three large accounts from an AE standpoint, even from an SE point, SA standpoint in some cases, and then also from a customer success manager. We're really building out that next generation team and go-to-market that helps us penetrate and take our enterprise business to the next level. What's the strategy as we pursue the enterprise? The first thing is really establishing an overall enterprise-grade operation. How do we run the business? What we've introduced was the next evolution of how we've run the business in the past, and that's a true field operating system. There's multiple components, and this is for our leaders and how they drive the business.
It starts with an operational model or cadence, weekly forecasting, bi-weekly pipeline management, deal reviews, all the things that are motherhood and apple pie, if you will, and fundamental, but really creating a new level of rigor and vigor inside those disciplines is part of that field leadership system. Another part of it is instrumentation. us really establishing the metrics that we're gonna measure our progress every quarter, every month, every week around the different elements of our business that truly matter. Third component in the field operating system is around enablement. The day I arrived at Elastic, I was so impressed with the sales enablement team and capabilities that we have. It's so important for us as we continue to grow and scale and really double down on the areas of our business that are so important. Enablement helps that.
The last component of this field leadership system or operating system is leadership. That's truly having the right leaders in the right roles with the right focus and making sure again that we're, you know, leading our teams, but we're managing the business with data. That's something that has really come a long way for us, and we've truly evolved that approach. Speaking of evolving an approach, really taking an even deeper look at the sales methodology. Look, we have to make sure everyone's singing from the same hymn book, speaking the same language, and making sure that we take our sales methodology to the next level. It's MEDDPICC, for example, and it's all about how we can harden our approach even more than in the past around the way that we run the business.
Taking MEDDPICC which is a sales methodology. It's a deal qualifying motion that's really fundamental to driving our business and increasing conversion rates. Really indoctrinating that into our selling motion and everything we do, for example, deal reviews, large deal reviews, that's been key for us. Another one I talked about a little bit earlier is around just our people, around our talent. It's bringing in the right talent matched with the right opportunities today and tomorrow. For example, having AEs that can truly run a very complex sales campaign in a very, very complex bank or telco or high-tech company spanning across different groups. I can't tell you how many banks that I meet with say to me, "We love Elastic, love the technology. Huge value for us. We have a lot of instances all around the bank.
We want our account teams to really pull it all together and bring it up. This is that whole top-down motion of meeting with executives on a very strategic level and looking at all the different ways and use cases that a bank, for example, or whatever customer, is utilizing our technology and able to really bring things to a whole new level. That's where this is going, and that's what the enterprise wants and needs, and the opportunity for us is tremendous. That's that selling motion, that top-down selling motion I'm talking about. Look, the other thing is we have to build and win the next generation multimillion-dollar enterprise deals and accounts. We have a big focus right now on going after white space enterprise logos. I talked about how we stood up those large enterprise, strategic regions in different parts of the world.
Three-quarters of those are actually built on white space accounts, new logos that we're pursuing quite aggressively. One of the big muscles that we continue to develop is our SDR team. I gotta tell you, when I came into Elastic a year ago, I was really impressed with the capabilities that we have. What I'd say is we've taken it to the next level. We've further evolved that organization where we still have them handling our inbound leads and that whole flow and doing a phenomenal job, but also, we've pivoted to a much deeper outbound motion, where we're leveraging this team, we're leveraging new technology to really start pursuing more aggressively these new logos from an enterprise standpoint, working with AEs on existing, but also much more focused on new logos.
I'm really pleased with the results I'm seeing so far and all the different metrics we use to see how we're doing in this approach. Let me get into the last aspect here of what's really important for us to pursue, and that is this expansion. What I thought I'd do is just show you all the possibilities, right? It's pretty straightforward, but it's important for everybody to know going after new workloads, helping customers understand all the different things we could be doing for them beyond what they're currently doing, and continuously educating them and understand their business and helping them understand how it's going, how they're utilizing things, what's available to them, the new roadmaps, the type of solutions we could bring to them. There's the competitive aspect of it.
Half the meetings I'm in with customers, they ask me about looking at different competitors and asking, "Can you get deeper into this? Can we get some proposals? Can we understand what you could be doing for us?" They're asking us. Look, the reality is they love the technology. I say this very humbly. Customers like doing business with us. They absolutely do, and they look at where we can take them, and this is a really key area here. Again, it's been said a few times, the single pricing model is so conducive to us truly being able to help customers span our platform. As a few folks said, expand within the use cases or the areas, and then span across other areas in the platform. That's really, really important for us. Then, of course, cloud. Cloud gives so many options to our customers.
New customers, and then again, also customers that are bursting or scaling into our cloud, really, really important for us. I thought I'd just tell you another aspect of this which is really important, and this is evolving rapidly, and I'm really excited about the capabilities we're giving our teams. We've done so much in terms of investing in being able to help our teams understand more what their customers are doing in terms of consumption levels, in terms of growth, in terms of the applicability of our technology in different areas of their business. As we look to have our teams help our customers not only get to value faster, but create new value for them, it's all around providing our teams that data set, and then also the tools associated with how they can drive that motion into these customers.
It's really, really important for us. By the way, it's going extremely well. Helping them understand where, hey, you know what? The customer may have signed up for a specific subscription level, but their needs have changed, and they need to go up to the next level, enterprise, for example. It's this intimacy that we wanna create with our teams and with our customers, helping them understand what we could do in terms of expanding this platform. It's such a big opportunity for us, it really is, and for them. Lastly here in this area, this is a big one, an important one for a seller. It's truly operating through the eyes of our customers. This is game-changing, and it takes some time, and we've been evolving, and we've been getting better and better at this.
I'm really pleased with the trajectory, and it's gonna continue. What we've done is, we've done this across the the whole go-to-market. It's with marketing, it's with my organization, it's with Rick Laner's organization in customer success. We've mapped out a model you can see here that, you know, is an infinity loop because it goes on forever, and it's all about helping customers land initially with Elastic, either on-prem or in the cloud, and then it's helping everything from the implementation, the deployment, seeing how they're doing, their speed to value, then it's the expansion and that starts, of course, with leads coming in through marketing. We have people focused on all these different areas. You can see them mapped out there in this model, in this customer buyer's journey, and we incent people across all that as well.
This is really taking hold, and at the end of the day, this is about value selling. It's about really understanding our customer's business and making sure that we can match our technology up to the value that they're trying to drive to their stakeholders. This is one that I'm pleased where we're at. I look where we're going in the future, and it just has endless opportunities for us. Really excited about this. This is all in the theme of the evolution of our selling motion, of our sales organization, of the integration between marketing and customer success, and of course really focused on areas like enterprise and cloud and then expanding that overall platform of ours. I have one final example. It's a large enterprise company again in the CPG industry, and this is an interesting one.
This is an on-prem customer of ours that started out a few years leading up to FY 2019 with log analytics, relatively small, but organically grew, and then this is where we were able to really span across the platform and also bring in search and security. Still on-prem, but really, really important step for us. Built out the account, but we became more and more relevant to this customer. Then they had some organic growth, but the real key here is, as one of the log analytics workloads was a networking workload, and it started expanding pretty significantly. This customer and us started looking at what it would take to manage this expansion, looking at the labor intensity of it, looking at their ability to procure more hardware in a timely fashion to support it.
In the end, it made sense for them to burst into our cloud. Now they're a hybrid customer with big, big expansion in one application in particular, where now they moved it into the cloud and it is growing and growing and growing. This is great progress, and this is our ability to have hundreds and hundreds and hundreds of these customers doing just this. This is where it's all going. We're positioned extremely well. We think our teams are armed, and our partners are as well, to be able to provide this level of service to our customers and continue to expand across our platform, both on-prem and in our cloud. With that, thank you so much. It's a real pleasure to be able to talk to you. I know we're gonna talk more through the Q&A session. I hope that was straightforward and made sense for you.
I wanna introduce Rick Laner. I referenced him a couple times, our Chief Customer Officer. He is someone who I work very closely with. Rick's come on board and quite frankly, doubled and tripled our focus from a customer centricity standpoint. He's brought a whole new level of really data-driven management, and the integration that we have in the field between sales and customer success is phenomenal. Thank you for all that, Rick. I appreciate it, and please, welcome to stage.
Thanks, Michael. Thanks, man. Cheers. All right, as we get set up here, my name's Rick Laner. I am the Chief Customer Officer at Elastic. Been about a year since I've started, as Michael mentioned. I've got the pleasure today of hosting three of our amazing customers, to talk about their journey with Elastic and what they've been able to achieve with Elastic over their periods of time as well. I'm gonna start to do the introductions as we go. First up, we have Marcel Matus from Concur, and he's come all the way from Prague, so arrived yesterday, I believe. Next we have a close almost friend of mine. I think we live just near each other, so in Dallas, Texas. Biji John from USAA. Thanks, Biji.
Thank you.
All right. Grab a seat. Third, we have Kevin Serafin coming in from Ecolab as well. All right. Awesome. Thanks. Grab a seat, man. All right. I'll take a seat myself. Let's get started. First question, I'd like you to do a little bit of intro of yourselves and also talk about your role in your respective companies, and then move into talking about your overall journey, and then also speak to the challenges you might have had with other solutions and how has Elastic helped you get back to where you are today. Over to you, Marcel.
Thank you. Hello, everyone. My name is Marcel Matus, and I work in the SAP Concur for about 13 years. About my responsibility is to manage a team, and we manage the observability tools for logging, monitoring, mostly time series data. We started with Elasticsearch, not necessarily with the subscription, like nine, 10 years ago. Very small. There was an old logging solution, we called it Error Services, and it simply didn't scale. It was based on Microsoft SQL database. When we first brought in Elasticsearch, our users were like, they loved it. They loved the speed, search and visualization stuff. Since then, we are growing always exponentially. Every year, we go 30%-50% up. Now we are from these, I don't know, few terabytes of data a month.
We are at few tera, few petabytes of data a month. In high peak, we ingest about one million documents per second. Pretty high scale. How Elastic is helping us is really to give much more insight to the data to our users.
Fantastic. All right. Over to you, Biji.
Thanks, Rick. My name is Biji John. I'm located in our Plano campus in Texas. I'm from the security side, so everything that you heard about this morning about security and observability, I am part of the security team that initially came into security and we launched it on-prem. Then recently I took over the cloud security organization, where I was able to use the Elastic SaaS as a launching pad to observe what's happening on the cloud and to secure our cloud workloads. So that's what I'm in charge of. Elastic, you didn't present a challenge. Actually, we ran into you for an opportunity.
Mm-hmm.
Everything that you talked about today has been a lead for us to really launch onto Elastic, provided that strength on-prem as well as on cloud. It's been more of an opportunity. We have about 94 TB of data coming in on-prem, and we have about 3-6 TB of data on the cloud. We are a multi-cloud customer, and we got AWS, GCP, and others. It's been really good to hear some of the strategies here as well. We use full-length extension of what Elastic SaaS provides for us on the cloud.
Thank you, Biji. Kevin?
Hi. Kevin Serafin. I'm the director for our incident response team at Ecolab. Very much a security use case. Our initial, you know, what we went to Elastic for help with was we had a lot of islands of data all around Ecolab. In incident response, I need that data immediately, I need it searchable, I need it consumable to my team so that we can react to whatever is happening. Yeah, no, it, the Elastic Stack was, you know, we got there before the SIEM really came into place. It was really log analytics with a security play, but rapidly.
That was just, you know, the team has been introducing feature after feature that has just kind of really helped us kind of grow our security organization and our capabilities to be able to protect all our data.
Okay, very good. Let's pivot to more broader thinking, right? Outcomes. If you think about your parts of your organization, what does Elastic help you do to drive certain outcomes for your business? Let's start with you again, Marcel.
As I mentioned, we use Elastic mostly for the log analytics today, time series data. With using Elastic Stack, it helps us significantly increase the time when we find the issue, recognize or track back to the problem for root cause analysis.
Reduce mean time to value, root cause faster.
Exactly. Just recently, we moved over to Elastic Cloud with Search Central, which helped us also significantly decrease the price with together extended the retention of the data.
Okay, very good. Biji?
If you know about USAA, we provide financial security for about 13 million members of our military services. We have a couple of objectives. One was securing our operation, because there's lots of attraction towards the data that we store as well as the people that we secure. Security was a really primary focus with why we use Elastic. We started on the on-prem activities. We have multiple private cloud solutions that we use. Security was one, and it shined. As far as ability to ingest data coming in, about 94 TB, like I mentioned, per day. Being able to ingest that, being able to look at threat, potential threat that's hovering over us, has been really more of an eight to nine years of relationship that we did on-prem.
That objective is right there, but security is all about staying ahead of the game. We're able to match that with Elastic and what you guys provide. When I look at the cloud, the cloud objective is to launch before we actually put applications on the cloud. If you notice, the cloud is. We talked about all the. I think we talked about the cloud and where that's going. We actually put Elastic from a PoC to a production within about three months, which we have never done something like that in our history, so a lot of accolades we received there. Second objective was making sure the cloud is protected before we put any workloads on it. That was secured. I think what we branded also up into observability and performance management, you heard about APM.
We actually monitor all our applications, mission-critical applications. Now, Elastic has become a feed. When Elastic goes down, it's more of an outage call and ACC call for us now because it has a mission critical. We talked about SRE. I think Ash talked about SRE. Right now, we connected that into an SRE framework where we are providing more of a threshold and responding before an outage occurs. Several objectives I stated, and pretty much all that was critical through use of the Elastic.
Thank you. Thank you. Kevin?
Yeah. From our side, it's really been around the being able to mix different data types in order to really gain insight to what's actually occurring. You know, from endpoint to cloud activity logs, audit logs on web servers and different things, being able to synthesize that all into a single view has been critical for us to be able to just gain visibility to what's happening out there. That's been the biggest thing for us. On top of that, just SIEM has just been a game changer for us in order to kind of corral all these alerts that are happening all over the enterprise. Just being able to synthesize that into, like, a single feed for my analysts to be able to consume and understand what's happening.
All right. Very good. All right, question number three. Think about workloads. Are you expanding, growing your workloads? Are they on-premise, in the cloud? How do you think about Elastic supporting you through that journey? We'll go to you first.
Okay. We started to explore Elastic Cloud about 18 months ago, and we liked the idea. We started small, created few proof of concept clusters, few small production clusters, and we need to explore, like, what's the performance of the solution, what's the cost of that. Before we start the big workload migration to Elastic Cloud, we were actually cooperating with our Elastic people, where we are bringing up various different strategy and architecture on the migration, on the clusters. We've been working, and just recently, we migrated our biggest cluster over to Elastic Cloud, and it's actually working really, really well. Right now, our strategy is move everything that's possible, legacy reasons, to Elastic Cloud and minimize the workloads on-prem. Right? It helps us significantly save a lot of resources, a lot of time of people solving security issues.
One of the biggest benefits in Elastic Cloud, you instantly delivering bug fixes and security vulnerability fixes. It's just a matter of 10 minutes. You click a button to completely upgrade the whole cluster. We have right now a big motivation to move over our Elastic Cloud.
All right. Cloud, huh?
Yeah.
All right. Biji?
I don't see us moving away from on-prem at all. We're a hundred-year-old company. We started cloud journey in 2015, so it's very, very much a newborn baby at this point. What I would say is we are gonna stay on the on-prem, and we're gonna use Elastic. You mentioned, I mentioned about the data that's coming in. That's gonna continue to grow, and there will be a future where we are gonna go, grow much faster on the cloud. If I talk about the cloud journey, AWS East Side, we're about 3-6 TB a day ingestion. We got the West Side coming up and then the GCP is coming up. A lot of the new application fit for the cloud is going on the cloud.
I would say maybe 99% of our new applications are going on the cloud, so we will be monitoring from the cloud. It's gonna be depending on the machine learning or data or storage or computing, we are also picking that the purposeful driven cloud providers for those activities. I will look on the horizon. I would say in the future sometime we'll do more on the cloud. How Elastic is playing a role is, since Elastic SaaS came on, there's been a lot of rave about how quickly we can scale up. There's more talk internally about consolidating everything on-prem for log management, observability onto cloud, and your account team's been very much of a part in that conversation.
All right. Very much a hybrid strategy, but you're also seeing a nice path to the cloud, but cautious.
Cautious. Very cautious.
Okay.
I'm the exact opposite. I'm trying to get off-premise as quickly as possible. Not only just from like a, you know, Searchable Snapshots were, like, really the decision point for us. It was, you know, the cost to cloud, obviously, like storage is pretty high. From a snapshot perspective, my analysts don't search across all that data all the time. I need to keep it there from an audit or just in case kind of thing, but they're not looking at it day-to-day. For us, it's been critical for us to just from that perspective, but also just moving over to from a disaster recovery perspective. I don't wanna have my analysts need to be Elastic experts. That's what Elastic is for.
I can redeploy resources to work on other things that are more, you know, relevant to us and let Elastic handle all of that for us. So.
Okay. You'll wrap it.
We'll actually be off-prem by the end of the month. So.
Wow.
So.
Go ahead. All right.
Little bit left.
All right. Fourth question. Elastic continues to invest in developing our platform, as you saw this morning, right? Or this afternoon. You've all been long-term customers of Elastic. What's kept you with Elastic, and what has driven you away from looking at other solutions as a start? Then what's your plans for Elastic over the next few years? Let's start with you, Marcel.
There are many solutions for providing logging possibilities, whether it's the tooling or companies. We are trying to be up-to-date with all of these and always do some analysis or even try different solutions. Elastic always wins from several reasons. First of all, our users, those are always internal R&D, SRE teams, users, they love Elastic. They love all the product visualization possibilities and all the features. Second, Elastic is very responsive from the bug-fixing perspective as well, security perspective. As I already mentioned, it's so easy to be up-to-date. You just click the button, and you are on the latest version within the major one. Next reason is also the cost perspective.
Recently, we just implemented the Searchable Snapshots, and we realized it helped us to really maximize the retention of the data, maximize the value of the data from a long-term perspective with practically zero cost.
That's a long list already.
Yeah.
All right. Very good. Biji?
For me and USAA, how I influence is leading the pack, I would say from a technology side. Three things that kept us with Elastic is how are you leading the pack and the technology you heard today about what you guys are innovating in 2023. I'm excited to hear that. Then the second one is cost management, especially when we go into cloud and even on-prem, right? Cost is a very big thing, especially when we are dedicated to making sure we're taking care of our members in a cost-effective way. Cost is a big thing. I'll give you a good example on the SaaS side, account team and support team, your team, and we worked together on initially when we went into SaaS.
Because of the pure sheer data that we were ingesting, the cost was really high. Within about two to three weeks, we were able to significantly reduce to 1/4 of it, mainly working together on the policy as well as changing some of the hardware on the cloud. We were able to quickly adapt it into managing costs within my budget. Last, I would say, is the relationship, right? We can speak about technology and cost, but at the end, when the things don't work like we plan and where is our relationship, who do I call? Great account team, great executive support. That is the third factor, I would say, is wanting us to stay with Elastic.
All right. Thank you, Biji. Kevin?
Yeah. I mean, you can just look at those, charts that they have over time, right? Like, all the features that keep coming out, and it's really, you know. Thinking about when I started with it, just logs. Get logs in there so I can search across it, visualize that, you know, do different aggregations, but it's just thing after thing after thing, and it's just been great for my team. Being able to just get them to be able to sit in that one kind of pane of glass and not have to bounce around 20 different screens has been huge just from a time cost perspective.
What do you think about the future of Elastic? Like, what are your plans for Elastic coming up through the next few years of what you might do more of?
Yeah. Well, today we do mainly the time series data and just logging, analytics, monitoring. I would love to actually extend with all the other features we just saw today, right? That makes complete sense. The company is driven by making the great decisions. You can make a decision if you have all the data together at one place. We actually already started the cooperation with you guys that to onboard more features, to onboard, like, more services.
Okay.
on Elastic
All right.
-platform.
Biji?
Ours is gonna be a little more drastic. I think the SaaS has been a model for us. I think the next one to about 18 months, 12 months to 18 months, we're looking at moving the on-prem Elastic self-managed onto cloud, which will be a huge lift for us. Second thing is we got about 150,000 endpoint devices. That's laptops, servers, 13 million members utilizing our network. That's a lot of data, right? The future is gonna be how much of a terabyte can you handle per day, and how do we scale and adjust to that, right? That's gonna be a challenge for all of us on how do we manage that data. The last thing, how else do we plug in, right? We talked about observability, security, application management, SRE.
One of the things I, you know, I do wanna talk about is what other integration can we do with GitLab? Can we instantiate and scale using our own GitLab repository, and how well we adapt to that? Those are some of the things that we're playing around with.
Wow.
with you and Elastic right now.
Massive scale.
Yeah.
Kevin?
I think the recent cloud acquisitions were, you know, really kind of a good direction for us to move into, right? As you kinda cement yourself into that kinda core SIEM, you know, observability, there's lots of ways to branch off that. You know, endpoint was an easy one for us, but then, you know, the posture management as well as the Kubernetes workload monitoring, that's gonna be. I'm excited to see all that come into play.
All right. Very good. Got one last question for you, Kevin. All right? You've been leveraging endpoint for over 55,000 devices. How has Elastic helped you facilitate that?
Probably the scariest day of my career was definitely when we flipped over to say, "Yep, enterprise, it's not a PoC. It's not 1,000 endpoints. Time to go big." That was. You know, the Elastic team was with us, like, every step of the way. The fact that we had literally no bumps with it, like, it almost, we got a little too cocky with it. We were like, "Oh, well, we started deploying the servers now." It’s been fantastic. You know, it's one of those platform. You know, and just seeing the value of all that data right out of the gate, just streaming right into Elastic with, right next to our, all of our paths logs, all of our audit logs, all of our authentication logs, that it's just been huge for us.
We've been super happy with it.
All right. Fantastic. Well, that wraps up the questions. I would just wanna say a big thank you for all flying in today for our event and being the panelists, and just for your trust overall in Elastic as well. A big thank you.
Mm.
Mm.
Thank you.
Thanks to you too. Thanks, Kevin. All right. I've got the pleasure of inviting our next guest up. I've had the pleasure of seeing Janesh speak several times. He's an amazing speaker and also an amazing business mind. I just one of the reasons why I came to Elastic as well, basically, not only just the technology, but the team, but just Janesh overall as a leader. I wanna just thank him for being such a great leader as well. With that, handing over to Janesh, our CFO and COO.
Thanks, Rick. I'll slip you the $20 bucks later. Thank you for that intro. It's now the setup is like I have to meet the expectations. In any event, thank you all again for coming. It's great to hear from our customers, and I wanna thank them for making the trek all the way and being here with us. It doesn't get more real than when you listen to customers talk about how they are using the technology and why they made the choices they made, and how they are working with us, and how they are growing with us. I wanna thank all of you for coming out here today as well one more time. I know it's been a packed afternoon. We've had a lot that we've covered.
The engineering team obviously talked a lot about why we win. Michael talked about how we win. We made it come to life with customer examples. I'll try and make it come to life with the numbers as well. From the standpoint of our track record, I think everyone's familiar with these numbers. We've had a very strong track record of driving growth in the past. If you look at our overall business, total revenue growth has been very strong, 34% here in the most recent quarter in constant currency. Cloud has been a big momentum driver for us over the years, with 62% growth in constant currency in the most recent quarter, up to 39% of the business. That momentum generally continues.
You know, if I think about the future, then where do we go from here? Everything that you've heard today, how do we then wrap that into the financial model for the future? That's really what I'm gonna talk about around two main themes. One is around the path to $2 billion in terms of how we drive that durable growth, and the second is how do we make sure that that growth is profitable, and how do we continue to deliver operating leverage that we believe is inherent in the model? Let's get into it right away. You think about the path to $2 billion. In the past, what we've said on several occasions is that we have multiple ways of getting there.
When you look at different kinds of trajectories, different kinds of models, we feel very confident in our path to $2 billion. I'll unpack a couple of those models for you here today. One is our customer model around land and expand motions. The second is around the deployment model around cloud and self-managed. Let's talk about the land and expand motion first. We've talked about the strength that we've got in terms of the customer base. We've got a large number of customers, more than 19,300 here at the end of Q1. One of the things that we also talk about is the diversified nature of this customer base.
We've got diversification, which is a big advantage to us in terms of our overall business model, and that diversification is across segments, it's across solutions, it's across geographies, and it's across verticals. When you think about that vertical diversification that we've got across all of these verticals, we are working with some of the largest players in these industries. Michael talked about how we're continuing to build on the enterprise selling motions and continuing to move further up within the enterprise. It's a great starting point for us, given that we're already so highly penetrated in every vertical. When you look at it, we're working with some of the largest companies in those verticals. We've been adding customers at a very strong rate as well. We talked about how most of our customers land on cloud.
In fact, more than 90% of our new customers are landing on cloud, which is a great advantage for us as we think about how we, about the expansion potential with those customers in the future. Monthly cloud is the predominant motion, as we've talked about before, in terms of that new customer acquisition. Where you'll also recall that, in Q4, we had a slight shift in terms of our approach over there, in terms of how we go about acquiring those new customers. A big part of that was also just thinking about the propensity of those customers to spend with us and to grow their spending over time, part of our focus on driving profitable growth. We had that deliberate change in Q4, and that change has been incredibly well executed.
Even if I say so myself, I think we've been very pleased with the results that we've seen around that internally, and that sets us up very nicely for the future. It's not just the strong land motions, but also how these customers then expand with us over time. One of the really encouraging things about our expansion motion is when you think about the customers in terms of the cohorts and how these cohorts have grown with us over time, every cohort continues to expand. That's again, a reflection of the fact that we've got a very rich product portfolio. We've got strong innovation, more capabilities being added to the stack every day, more capabilities being added to the solutions every day, people continuing to move further up in terms of subscription tiers.
There's many levers that drive that expansion, which I'll talk about in just a moment, but that's applicable to customers, whether you started with us in fiscal 2021 or 2020 or 2019 or earlier. When you think about, I'll use a couple of these as data points. If you look at the customers that started with us in fiscal 2020, those customers have increased their spend with us 2.1x in those two years. Customers that started with us in the year before that, in fiscal 2019, also grew 2.1x in three years. In other words, the newer cohort actually got to that same multiple in a shorter period of time. So we're very encouraged by some of these trends in the business.
If you think about what drives these, Ash touched on some of these when he talked about the growth vectors in the business overall. If I select just the ones from those that focus on expansion, obviously customer data volumes. As those continue to grow, there's more data that comes into Elasticsearch, more data that needs to be indexed, more data that needs to be searched, more resources that get applied to it. That drives expansion. We talked about customers extending across more use cases, although we start with our focus in terms of our areas of strength, and that's where we lead in the market, and that's how we go to market initially in areas like logging and SIEM, which really leverage unstructured data.
You saw a number of examples where customers extend from logging or SIEM into additional use cases, and that's another part of how we drive expansion. It's not just across use cases within that one solution. One of the things that differentiates us when we talk about observability, for instance, we think about logging and APM and metrics. That's all part of one single solution for us. We don't double count those and count those as three separate products. To us, those are just the use cases, but the solution of observability is one solution. You also have customers that extend from one solution to another. They go from observability to security or from enterprise search to observability.
We have, if I think about the pool of 100K plus customers that we have, we now have almost 400 customers that use us for two or more solutions. I'll talk a little bit more about the expansion potential that we've got. Cloud is another great driver of expansion for us, not just because the ticket sizes are bigger, because you've got, on average, larger transactions or workloads, but also the nature of the adoption, the nature of the scaling is relatively frictionless. You heard from some of our customers how they're adopting cloud and how they are scaling even faster in cloud. Of course, as customers continue to move further up in terms of subscription tiers, we've talked about how the enterprise subscription tier is the fastest growing tier for us.
This incidentally coincides with the launch of Searchable Snapshots, which we launched late in November 2020. Really started to GA and take it to market in early calendar 2021. You can see that in fiscal 2021, the enterprise subscription tier for us on cloud was only 2% of our mix at the end of fiscal 2021. At the end of fiscal 2022, it had grown to 13% of the overall mix. You can see that the product innovation that we've been driving continues to help drive that subscription mix change over time as well. With Searchable Snapshots came the ability to search across gold and frozen storage for the enterprise tier. I'm personally super excited by the hybrid cluster, cross-cluster search.
I always trip up on cross-cluster, must be the alliteration. There we go again. Cross-cluster search and replication capabilities. We'll need to come up with a new acronym for that just to help me out. The other piece in this is the enterprise subscription tier tends to be about 30%-40% higher in terms of list price versus platinum. Of course, the economics in terms of every customer conversation play into that as well. Let's take a look at the pool of customers that are more than 100K in size. If you think about that pool of customers more than 100K, this is data that we've shared before. We share it every quarter.
If you look at the net adds in any quarter, it's typically been, you know, call it 50-70 customers for the past several quarters. We've been adding to that pool at a very consistent pace. That's a really important metric that we think about, and I'll come back to in just a moment, because when we talk about the net expansion rate, that tends to be, it's a trailing 12-month measure. The customer count more than 100K usually is a much more current measure of expansion because customers, when they first land with us, tend to be less than 100K in size, and then they grow into this. This is a current measure of expansion.
Of course, to the extent somebody just lands with a transaction more than $100K, they do get counted in this number as well. What's really exciting about this pool is that when you think about the cloud opportunity that we have with these customers, and you look at the disaggregation of spending of these customers, 17% of the spend from these customers is cloud only. 31% is self-managed only, but the majority of it, more than 50% of the spend, is for customers who have both self-managed deployments and cloud deployments. As you just heard from the panel now, everybody is moving to the cloud at their own pace and with their own strategies, and some of them are adopting new workloads in the cloud, some are moving existing workloads.
What really matters for us is to be there for customers where they are, and to meet them and to be there on their timeline when they are. We're not trying to drive any forced migrations. We're there working with our customers, and that 52% represents significant opportunity for us. Nobody else can really help customers in that hybrid cloud journey, the way we can, especially when you think about the feature that I just mispronounced earlier, which I'll avoid mentioning again. When you think about the net expansion rate, for customers in that greater than 100K band, that has been more than 135% now for several quarters. Again, keep that metric in mind. We'll come back to that one. It shows you how quickly those customers continue to expand.
When I think about this pool of customers that's more than 100K in size, and I look at how much of our total customer spend do they make up, they make up 69% of our total customer spending. They have been like that for the past couple of years. That's a metric that's actually been pretty stable. The reason you have that consistency in mix is obviously you've got the customers that are continue to grow and expand and move into that 100K plus bucket, and their spending continues to grow. On the other hand, you've got strong new customer additions that are coming in, as well as the customers that are less than 100K continue to expand in size.
That's kept the mix of $100K customers versus the smaller pool relatively stable over time. If I think about what that customer journey then looks like, when you look at the customers, as I said, most customers will typically land less than $100K with us. Our average has been $34K for that, for those customers. As those customers have increased their spend with us over time, today our customers who are in that pool of $100K plus, they spend on average a little bit over $600K per year with us. That's an 18x increase from the time that they first land to where they stand today.
Within that, we also talked about the fact that we have more than 115 customers that spend more than $1 million with us at the end of fiscal 2022. As I look at that pool of customers, they spend on average $3 million a year with us, so that's another 5x increase in terms of that category. It just gives you a sense of the expansion potential that we've got within all of these accounts, all of which tend to be in the enterprise space. Let's tie this all together. As I think about the revenue build and think about how this all ties to the path to $2 billion, let's first take a look at fiscal 2022.
We had, at the end of fiscal 2022, over 960 customers that were in that greater than 100K pool. They were spending on average a little bit more than 600K per year with us, and that made up 69% of the total spend of the total customer base. If you think, do the math there, that works out to total annual customer spend of a little bit, call it just shy of a billion dollars, roughly $900 million. That's subscription spend only. That excludes professional services. You then apply the normal revenue recognition and so forth to that. We recognized roughly $800 million of subscription revenue during the year.
You tack on the services revenue, and that gets you to total revenue for the full year of just under $900 million that we reported. Let's extend this to fiscal 2025 and see what that model would look like. If we continue to add customers at the rate of, let's call it under 50, we don't even have to hit 50. Let's say 45 new customers a quarter. By fiscal 2025, that gets us to roughly 1,500 customers, a little bit more than that in that 100K+ population. Remember, those customers are growing on average. I said our net expansion rate is 135%. Let's not even count the full 135%. Let's just count an 18% rate, so roughly half of that.
Let's assume customers continue to spend with us at an increasing rate, and that rate of annual growth is only about 18%. By fiscal 2025, those customers on average will be spending about $1 million with us. We maintain the mix over time. It's been very stable for the past few years. That gets us to $2.2 billion in annual customer spend. From there, the math flows in terms of the rev rec, and you assume services is a constant attach rate, and that gets us to the $2 billion. If you think about it, the three core assumptions on which this model rests is the number of new customer adds, the growth rate for the customer spend, and the mix, right? The rest is all mechanical from there.
Let's take a look at what it would take to achieve these three assumptions. On the 45 net adds per quarter, we're already stronger than that. We've got a track record of anywhere between 50 and 70, as I talked about. Importantly, we're not actually dependent on any more new logos. Any new logos that get added to this is all addition. It's all gravy on top. Because if I look at our existing install base, we've already got 3,000 names in there, more than 3,000 names in there of enterprise and public sector customers. To get to 1,500+, we don't even need to penetrate that full base and get them all to $100K+.
There's plenty of room for us to continue to expand within the install base and continue to drive this motion. If I think about the 18% spend growth per customer, as I said, the net expansion rate in this pool is already 135%. We've got significant upside potential within that. New customers, you know, if you think about it, new customers that grow into this population are also expanding very rapidly. Finally, in terms of the 69% mix, as I said, it's been stable for about three years now. With the rate of customer additions we've gotten and how people below that 100K threshold are continuing to expand, that's a fairly stable percentage.
When I put all of these together, all of these get us that confidence and make me comfortable around the path to the $2 billion that we talked about. This is one model in terms of the land and expand. I'll switch gears. I'll talk about the second model in terms of how we get there with cloud and self-managed. From a cloud perspective, again, we've been incredibly proud of the strong growth rate that we've had in cloud. When we first came public, back in late 2018, it was our fiscal 2019, cloud was, as you can see, only roughly 17% of our revenue back then. Since then, we've grown quite dramatically. You know, back then, our revenue was a lot smaller.
In fiscal 2019, it was, I think, only about $271 million or thereabouts. Here we are, you know, at the cusp of $1 billion, and cloud is now 39% of our total revenue. Last year, cloud grew 80% year-over-year, and this year in Q1, it grew 62% in constant currency. Question is, what's sustaining this cloud momentum? There's a few things I'll call out. You know, if you look at the few points on the left of this slide, we've already got a strong track record of growth at scale in cloud. That growth, as I said, is largely organic. We're not driving any kind of artificial motion to go mine the install base and drive forced conversions or anything of the sort. This is coming from expanded workload growth.
Net new workloads are starting up in the cloud. Those workloads are expanding faster in the cloud. We've got very strong consumption patterns in terms of how our customers are consuming us. We've talked about the resilience of our use cases before. We talked about the investments that we've made in Rick Laner's organization around customer success and how those investments are scaling quite nicely for us. And all of this has driven the sustained organic mix shift to cloud up at 39% of total revenue now. I'll talk in a minute about the land and expand motion specifically as it relates to cloud as well. I mentioned more than 90% of our customers are landing in cloud. Those customers are continuing to expand quite aggressively and at a very fast pace.
We've mentioned before the net expansion rate of more than 100%, for cloud, which has been a net expansion rate that we've sustained for several quarters now. All of this is even before you think about the recent changes that we've made and the recent emphasis that we've put from a go-to-market perspective, the marketing motions that we changed in terms of how we think about customer acquisition, which we talked about having made just six months ago, the field performance, the compensation plan changes that we made, the Customer 360, all of the things that Michael talked about in terms of starting to build out those go-to-market motions, the hyperscaler partnerships which have been growing. They've been more than doubling in terms of year-over-year growth. All of those things continue to sustain the cloud momentum for us.
You know, as I mentioned, we've got very strong customer acquisition in the cloud. Of the 19,300 customers or more than that, more than 16,600 are in cloud. A lot of those are monthly cloud. A lot of those have these, the hybrid cloud deployments that we talked about. The vast majority of the customer base, in fact, is already on cloud and therefore, you know, it's logical to expect that the bulk of the expansion in the future will come from this customer base and therefore will naturally be in cloud. As you think about those expansion motions, we've said our cloud net expansion rate is in excess of 140%. What's interesting to me here is that the newer cohorts are actually growing even faster than the older cohorts.
If you look at the FY 2019, 2020, and 2021 cohorts, you'll see that each of the new cohorts has actually been growing faster than the cohort before it in terms of those expansion rates. That's reflective of some of the newer innovations that have come out over the last one to two years. It's reflective of all of the emphasis that we've put on not just the product, but in terms of the go-to-market motions around those customers as well, and the success that they've had. Even with the consumption model in cloud, that consumption model is one that we rolled out, you know, a couple of years ago, and it's helped eliminate a lot of friction from the customer process of consuming our technology.
Because they don't have to worry about getting hit with all kinds of on-demand charges and getting surprised with those. They control the rate of consumption, and should they choose to consume more, then they can incur those costs. And that actually makes it much more powerful for them because they have the control in their own hands. How does this tie back to the $2 billion number?
Well, when we rolled out guidance for this year and the midpoint of our guidance was $1,083 million, a lot of folks did the math quite simply and said, "Hey, you need to grow at 36% over the next couple of years, so are you implying some level of revenue re-acceleration?" First off, let's just take, you know, if you sort of think about this one step at a time, in fiscal 2023, we're already off to a great start, right? We're already growing in the quarter from the low 30s. Q1 2023 was 34% in constant currency. Our guide for Q2 is 32%. You also know that we've got a strong track record of continuing to outperform against the expectations that we set, not making any promises or guarantees here.
We'll continue to work hard, obviously, in terms of trying to overachieve against the fiscal 2023 goals that we've set forth. Even if you think about that level of, you know, call it growing from the low 30s to the mid-30s, if you just think about the cloud business, you've seen that it's been increasing in mix. Call it roughly six to seven percentage points a year. On a business that's growing roughly 40 percentage points faster, so cloud is growing roughly 40 percentage points faster than self-managed, 62% versus 21% in Q1 as an example in constant currency. For a business that's growing 40% faster with a seven-point mix shift, that gives you a natural three-point acceleration in the business every year, right?
That's a three-point acceleration in fiscal 2024, and then another three-point acceleration on top of that in fiscal 2025 as that math continues to hold. Again, when you just look at all of the drivers that we talked about in cloud and the sustained momentum that we've got around those, that gives me that confidence and that comfort around the escape velocity in cloud. I unpacked the growth model in a couple of different ways. Let me switch gears and talk about how we drive profitable growth. The natural question that comes up then is: so what's the extent of operating leverage that we've got inherent in this business model? Fundamentally, I think of us as traditional enterprise software business model.
In any one year, you can have lots of puts and takes, especially when we had to navigate the pandemic and things started to change and look a little bit different. But if you look at it over a longer period of time, I joined this business in fiscal 2018. In that year, in fiscal 2018, which was right, the year right before we went public, our non-GAAP operating margin was negative 20%. We achieved operating breakeven in fiscal 2022. In fiscal 2023, we're not delivering a lot of leverage because there's a couple of things that we're absorbing in the model. One is with the increase in the cloud mix at a more rapid pace, we'll have a slight headwind to gross margin in the near term, which I'll talk about as well.
Plus, of course, with the return to travel in a more normalized world after COVID, that's something that we need to factor in here as well. You know, if you think about it, our first analyst event was a virtual event. This one's in person. A lot of people have traveled here for it. You know, one way to think about it is in that four- to five-year timeframe, we are driving roughly a 20 percentage point improvement in operating margins or have driven that. That's sort of the nature of the natural operating margin expansion that that I think about is inherent in the business model fundamentally. The question is, how do you then drive this, you know, how do you drive this operating margin improvement of several percentage points every year?
Let's unpack each of the lines. If I think about gross margin, while gross margin will be a modest headwind in this fiscal year, fundamentally, over time, we have multiple levers to continue to drive gross margin higher. One is just the mix shift, right? You saw that the enterprise subscription tier, for example, has already increased to 13% in cloud, and stands to reason that it's our highest level tier, the most profitable tier. That will continue to help. You heard Steve Kearns and Shay earlier talk about some of the investments that we're making from a longer-term product architecture perspective. Those will naturally start to kick in. As we get scale, we have greater cost efficiencies from two sources. One is we just drive better pricing in terms of our own infrastructure purchases.
The second is operational efficiencies as we continue to get more efficient at managing across all the regions that we have in terms of places where we deploy capacity. Multiple sources of gross margin leverage, which start to kick in in fiscal 2024 and then amplify further in fiscal 2025. If I think about our innovation machine on the R&D side, it's just amazing how much innovation the engineering team turns out. Then we use all of that. It differentiates us from a competitive perspective. It allows us to drive rapid monetization of the technology. Even there, if I think about how we get leverage over time, there's several things. First off, we've talked about this extensively, we have a single code base across our stack and solution.
Even when you think about the 3.6 billion downloads that we talked about, we don't have a separate code base for the free downloads and then a separate code base for what we've got in terms of paid features. It's one code base. Think about the efficiencies that creates from the standpoint of development, from the standpoint of maintenance and all of those things. We also, if you think about the features that we've got that we build in the stack that we leverage across solutions, we talked about a number of those. Searchable Snapshots is a great example. Steve talked about a number of things in terms of platform roadmap and everything that's to come.
You heard the GMs for each of the solution areas talk about how they are leveraging those platform features within each of their areas. A thick platform and a thin solution layer on top just very naturally gives us a lot of leverage from an R&D perspective because you can build it once and deploy it across the three solutions rather than having to build it three times. Another one is just our distributed engineering team. Even from some of the presenters who came up here today, you see, you'll see that many of them came in from different regions, from different parts of our organization.
The engineering team for us has been distributed from day one by design, partly given our open source roots, partly given the international nature of the company and where we were founded. That's naturally given us efficiencies from an engineering perspective. Over time, we'll naturally continue to get some degree of leverage from an R&D standpoint, just as the overall top line of the company continues to grow faster than the overall rate of investment in R&D. On the go-to-market side, you know, many of you have noted quite astutely that we've actually increased our investment in sales and marketing over the last year, in particular in fiscal 2022. A big part of that was catching up on investments that we had not made previously.
If you think about the first few quarters of fiscal 2022, compare that to the rate at which we had added resources in the pandemic. Some of that was more in the nature of catch-up investment. We've talked extensively about how those, the people that we've hired, those folks all continue to ramp to productivity, where I think all of those investments have been working quite nicely for us. Michael also talked extensively about our focus on cloud, around our focus on enterprise, as well as the expansion momentum and expansion plays that he's driving across the organization. You know, all of those things will continue to give us operating leverage, particularly as we think about customers that have a higher propensity to spend. Over time, the sources of operating margin leverage for us from a sales and marketing perspective, there are many.
First off, just naturally higher productivity, right? I think that we'll expect that in sales organization every year, just as we should. Cloud is much more efficient in terms of our go-to-market motions. The ticket sizes are larger. A salesperson can be much more productive. We've talked about the hyperscalers and our partnerships with them and how they help us go to market and that drives a much more efficient growth engine for us. One of the things that sometimes people often you know don't think about is the investments on the product side actually make the go-to-market motion easier. Michael touched on this a little bit when he was talking about automation, when he was talking about product-led growth.
It's the product itself that is prompting customers to think about how best to expand. Have you considered these features? Can we make your life easier in that way? As that tends to naturally happen, it drives greater adoption, it drives greater consumption. As the consumption increases, when the salesperson goes back for a renewal or an expansion conversation, it's a much bigger conversation. All of these things will continue to help us grow top line faster than the rate of investment growth, from a sales and marketing perspective. Finally, G&A, which is obviously near and dear to my heart, will naturally just continue to grow at a healthy pace needed to support the business, but top line growth will exceed the pace of investments in G&A, and we'll just get efficiencies with scale.
As I think about what all of that means for the long term, you know, fundamentally, for us, the story is one about re-accelerating growth with non-GAAP operating margin expansion over time. We've talked about the $2 billion in fiscal 2025. Most of that obviously will come from subscriptions. We don't expect the services mix to change dramatically over time. We've talked about our cloud revenue being 50% of our total revenue by Q4 of next fiscal year, fiscal 2024. That continues to be our goal. We are tracking quite nicely against that. We've talked about non-GAAP operating margin expansion of several percentage points in fiscal 2024 and 2025. Just to give you a sense of how I think about that over a four- to five-year timeframe, we've driven improvement of 20 percentage points.
It gives you the sense of what that might look like over a longer term. In terms of thinking about free cash flow margins, fundamentally, there's no structural change in terms of the direction between free cash flow margin trends and non-GAAP operating margin trends. I'm not anticipating any big changes in working capital or anything like that in the model. As you see operating margin improve over time, you should expect to see adjusted free cash flow margin improve over time as well. We'll continue to maintain a fairly conservative balance sheet. We will pursue tuck-in acquisitions, as we always have. You've seen that, we've always used acquisitions as a way to pull our future into the present. I think that will continue in terms of our strategy.
Obviously, those tend to be opportunistic and hard to exactly predict. Finally, as I think about stock-based compensation and our equity programs, we've not done anything artificial or unnatural in terms of trying to, you know, trying to respond to the current market environment in any way. Elastic continues to be a preferred career destination for a lot of people. If you think about it, we hired so many people in fiscal 2022. That itself should be demonstration of our ability to attract talent. We would target about a 5% annual share count dilution, which of course excludes acquisitions to the extent there are any. We'll continue with that same financial framework that we've laid out here.
Just to bring it all home in the spirit of trying to keep us here on track, what you've heard about today from Ash, from the product GMs, from Michael, from our customers, from a technology perspective, it's a very strong product market fit. Like, the tech is exceptional, right? It's really unmatched. A differentiated value proposition across all the solution areas. We've had a very strong track record of continuing to have great execution. We've delivered every single quarter. You saw that on the first chart. Michael is driving the team to take that to even greater levels. We're on track with our cloud goals of achieving 50% of total revenue by Q4 of next year. We feel very confident around our path to $2 billion.
I showed you a couple of ways that we'll actually get there. Finally, in terms of how we manage the business as just good stewards of capital, we will continue to drive operating margin expansion over time, similar to what we've said before. With that, let's move into the Q&A section, and I'll invite up a bunch of the senior management team from Elastic to come join me here for the Q&A session. Just as a reminder, we'll have Q&A for about a half hour, and then after that, we've got the reception in the room across the hall. Feel free to join us. We can continue to talk over there as well for additional questions that you might have. In terms of the logistics, I think we'll have people walking around with mics.
Yes, thank you very much. If you can just raise your hand so they know to come to you, and please just make sure to speak into the mic so we get your audio for the webcast. Come on up, folks.
I'm okay to stand.
Go ahead. I'll stand. It's okay.
Oh. Yes. Is the mic working?
Yeah, it's working. Oh, sorry. I thought that was Kash's voice.
It's working now?
Just go ahead, Kash. We won't need that.
I was just super nice to Olivia. She gave me the mic. Congratulations. I mean, a tremendous amount of detail, thoughtfulness, strategy, product, metrics, et cetera. Shay, you'll not be surprised by this question. I've asked you the same question on other conference calls before. You've talked about, and I think feel free anybody to chime in, the convergence of buying centers between security, observability and any other buying center that you might add in the future. It looks like that's key to the company unlocking its path to $2 billion and beyond. How do you see that playing out? Are we gonna have one chief data officer, because everything is a data problem, right, that will lead to the convergence of how these individual technologies are procured and standardized in the enterprise?
How do you see that playing out, Ash or Shay, anybody that wants to jump in, and both of you guys can jump in too. Thank you.
Happy to. I can start. I think the first part is that we see it as more, you know, there's the observability side, security side, and enterprise search. Enterprise search is typically more focused on developers, and they tend to report up to a chief product officer or something along those lines, most of the time. There's also IT. In observability and security, the first part I would say is that there's still a lot left for convergence to happen within each one. Observability, we still see pockets of, you know, people that are responsible for APM versus people that are responsible for logs versus people that are responsible for infrastructure monitoring or monitoring. Now their Kubernetes deployment falls under the SRE team or other teams.
I think there's still a lot of work left to have convergence there. Not even only in terms of responsibility, but also in terms of tools. You have quite a lot of variety of tools that tend to address each one. I think the same thing is in security in terms of the products that you have. That's like a very fragmented place, but that tends to roll up into a single CISO, if you will. Moving forward, I think the need for products consolidation is evident when you talk to team members, and that's like everybody see it, especially, we see it a lot because we come from the data place.
SIEM and logs are very similar in the way they tend to represent a large portion of the data that security teams deal with and observability team deals with. That's top of mind for people. I think from an organizational perspective, that takes longer. People need to feel comfortable with using each other's data, and people need to get comfortable in terms of building workflows and infrastructure to support their own, like, usage of what is pretty much the same data. We see it happening, but I think it's gonna take a few years to happen.
In terms of how it ends up looking like, my best guess, if you're pointing at the CIO, that's probably the one that will end up being responsible for it. We see it in some of the larger organization, where the top-level CIO ends up having the security team fold into their responsibility. That's great for us 'cause today we talk a lot to the CIO when it comes to enterprise search and observability use cases and talk to the CISO, and then that tend to happen. Whether that CIO will end up having a different title, I don't know.
I'll maybe touch upon that. You know, what was great was we had several customer speakers as you observed, and you know, just I'll pick on Biji. I don't know. There he is at the back. So Biji is at USAA, and he talked about how he's part of the security organization. If you heard what he was saying, one of the things he talked about was also how they're looking at observability. I remember my first meeting with Biji was at their offices in San Antonio, and it was a meeting where we had Biji and folks from the security team, and we also had folks from their observability team.
In some ways, I think it is, it's not necessarily a question of is it converged in terms of both of those teams being the same, but more about how closely do these teams work with each other, and we are already seeing that. Like, we are already seeing that, you know, the teams are, you know, often not just in collaborations, but they definitely help us bridge from working with one team to working with multiple teams. To Shay's point, I think that's been an organic journey that organizations have been on for some time. But the need for being able to leverage data in more ways is pretty evident. Organizations at their own pace are finding, you know, different ways to do it. We are clearly benefiting from it.
I think one of the things that I will also say is to the macro point of unstructured data, when you're, you know, when you're dealing with unstructured data, which tends to be one of the common themes that you heard from multiple people, like the awareness that there is this technology called Elastic that has all of these capabilities, that awareness is there across observability teams, across security teams, and we are naturally able to sort of make those connections. So I would see that as being the evolution and continued evolution, but that's what gives us the confidence that we're gonna be able to not only grow within each of these areas, but across each of them. You know, I think the data proves our ability to do it.
Tyler, yeah, you got the mic.
Oh.
Great. Thanks for taking the question. Tyler Radke from Citi. The first question I wanted to ask, and Janesh, it was great to kind of see the different approaches on how you get to $2 billion. Just in terms of the product side, how should we be thinking about, you know, the specific product drivers within kinda your three main buckets that get you there? I mean, which are gonna be the kinda the fastest growing products you're most excited about? Secondly, can you help us understand, you know, what you're assuming for, you know, any conversions of existing on-prem customers to the cloud?
Maybe not shutting off like the on-prem stuff, but just in terms of that incremental SaaS dollars for the equivalent workload, is there any type of uplift that you're modeling? Thank you.
Yeah. On the first part of that, Tyler, as I think about the solutions mix, if I look at where our business is today, security, as we said, is roughly 25% of the ACV in the business. Observability is a little bit more than 40%. Enterprise search and the long tail of other kinds of search applications, which we all call enterprise search, that makes up the rest of the business. We're not discreetly modeling any particular mix shift or, you know, necessarily trying to hit a particular target in terms of a certain solution area. Security has been growing the fastest. I do expect that security and observability will continue to grow quite fast.
You'll see in some of the slides we had the total addressable market, and fundamentally, you've got the underlying markets that are growing at different paces. We're not trying to actively drive one or the other, but I think a lot of what you're seeing is just natural trends in terms of customer adoption. We're also not dependent on convergence necessarily across all of those, nor are we anticipating, to the second part of your question, driving any kind of installed-base migration. Some of that happens quite naturally. Again, you heard from some of the customers with the journey that they are on. We're not necessarily dependent on driving that. Our view has always been that we'll continue to be there for customers wherever we are.
Over the course of this year, since Ash became CEO, we've certainly increased our emphasis on cloud, put a lot more focus on it, created a lot more clarity, intentionality in terms of the selling process. But at the end of the day, a customer's gonna choose cloud or self-managed based on their broader decision around where applications are, where infrastructure resides, and they'll make those decisions based on their overall architecture. As I mentioned, for hybrid cloud, when customers have these mixed deployments, we're really one of the only ones that can actually help them through that journey, partly because we're not forcing their hand one way or another, and second, because we are there both on-prem as well as there, in the cloud for them. I think there was a. Yeah, go ahead, Matt.
Is this on? Oh, hey, thanks. Matt Hedberg from RBC. Thank you as well for this today. Ash, I think since you've been here, it's been clear cloud first, and you're leading with cloud, and that's an important piece of the mix shift to $2 billion. There seems to be lots of drivers of cloud growth, but hyperscalers growing at over 100% certainly seems like a super important aspect. I guess I'm wondering as a sort of a two-part question, what are they doing to specifically drive Elastic growth? Obviously, you can use cloud credits, but what are they doing to help you accelerate that hypercloud? Is there a way to think about. I know, Janesh, you didn't quantify what percentage hyperscaler is within the cloud mix, but is it 25%?
Like, what might that look like in fiscal 2025?
Maybe I'll kick off the response, and then Janesh can pick up from there. Just in terms of the things that we are doing with them, you know, first of all, in my mind, it starts with the product experience because that's what the customers really value. Like, what is it that they get when they come to the cloud marketplace? They look for Elastic. The product experience, what we've been working on with all three cloud hyperscalers, whether it's AWS, Azure or GCP, is how do we make it possible for you to work with Elastic in the most easy way possible to get started?
An example of this would be the work that we have done with Azure, with Microsoft to effectively, like, you can go to their console and, install Elastic or launch Elastic Cloud, deploy all the resources, almost as if they were Azure resources. The customer experience is amazing because you never have to come to our console. You do everything from their console, and that's just an amazing frictionless experience if you're an Azure customer. That's, you know, we have similar integrations that we have built with the hyperscalers, the other ones. The second thing is, in terms of the partnering that we do with them, because in many cases, their sales reps effectively get quota relief when they sell anything that's sold through their marketplace. They have a natural incentive to work with us and partner with us.
Like I've said, we have a very strong brand recognition anytime it comes to dealing with unstructured data, search use cases, observability use cases, security use cases. We see them actively coming to us, bringing us deals, bringing us opportunities. And you know, they have larger sales teams than us, so to the extent that we can leverage their sales teams effectively to bring us opportunities that we might not have otherwise had access to as seamlessly, that just makes our selling motion that much more efficient. We are seeing a lot of that. You know, in the past, with Azure and GCP, with Microsoft and GCP, you know, we had done a wonderful job of getting there. As you've seen, we've been spending a lot of energy in making sure that our relationship with AWS continues to improve.
You've seen the proof points of that, and we're actually seeing that play out in the market in terms of our sales teams collaborating even better. I see the marketplace momentum as continuing to be something that's meaningful. In terms of what that contributes to our overall cloud business, it's a meaningful number. We haven't broken it out, but it's a meaningful number, and I expect that that'll be a continued tailwind for us. Janesh, you want to add anything there?
You touched on the second part already, so I'm good.
I just wanna add.
Yeah. Go ahead, Michael.
To Ash's point, wherever I'm going in the world, they are literally pursuing us, the hyperscalers, wanting to meet, sit down, asking for intros from me to our theater leaders or even a leader at the AVP level and wanting to pull teams together and go out there together. It's been really encouraging.
I've heard a hyperscaler partner describe it as, you know, them loving everything that spins their meters. I'd never used that expression, but I'd never heard it before. Spin the meters. Our workloads tend to be very data rich. Any workload that's data-rich spins a lot of meters for the cloud provider. There is a natural bias and interest in working with us.
Let's go to Raimo, and then we can come back to Steve.
Oh, thanks. Actually a question for Mike. Since you joined, and if you look at the organization, there seems to be a big change because, you know, Elastic forever when kind of shy when being in public was like people would come to you because it's, you know, an amazing solution, and it was more like customers kind of driving it almost rather than you driving it. Now, you talked a little bit about the top-down momentum, and it feels like it's the journey because you kind of need to get the sales organization on the right focus. They need to kind of think slightly differently rather than just executing orders. You need to go out. Where are you on that journey?
Kind of you touched a little bit in your presentation, but maybe you can give us a little bit more where you are and, you know, where you want to go in terms of the end stage and where you are on that journey. Thank you.
Sure, sure. Great question. I appreciate it. I don't think it's a dramatic change. I think it's augmenting and really evolving more than anything else. What's played out, and will continue, but we're stacking this on top of, if you will. We're accelerating. You know, I talked about time's always the enemy, but really I think it's complementary. For example, the bottoms-up approach continues. Customers continue to drive us in many different ways, which is great for us. On top of that, really bringing this top-down approach, bringing the ecosystem into play. A lot of the other things I talked about to me are complementary and just further accelerating. Where are we in that journey?
I don't know if early days would be right because I always want it immediately, but I really like the pace upon which we continue to augment and complement the foundation that's already been in place.
Steve.
Thanks, guys. Great. Steve Koenig from SMBC. Hey, thanks for a great session. I want to call out to the USAA speaker for securing my data since I'm a customer. I want to ask you, it's you know, related to the journey Elastic is on. I mean, your company has been just so incredibly impressive with what you've done to date. Now, as markets are converging and you're moving into observability and security, these are very crowded markets with a lot of players coming from different angles.
I'm wondering, as you look at that large market opportunity, what do you see as the natural sweet spot for you guys or the types of customers that are going to gravitate towards you know, versus the Datadog, the Splunk, the Dynatrace of the world? Thanks very much.
Yeah, maybe I can take a crack at that and then offer it up to anybody else who wants to jump in. I don't think it's a category of customer. You know, to me, what it comes down to is, at the end of the day, I'll just take security as an example. We talk about the fact that, you know, security, yes, there is some convergence that constantly happens. People want to do more with less. If you think about security, A, it's a massive market, and B, there is a lot of specialization that exists within that market, right? You have folks who specialize in endpoint security. You have those who specialize in network security. You have those who specialize in SIEM.
In the same way, when it comes to observability, there are, you know, there is a specialization around log analytics. There is a specialization around APM. There's a specialization around infrastructure monitoring. Yes, customers are always going to try and do more with less. If they can consolidate, they're going to try and consolidate. You've heard a lot of those stories today from our customers. Fundamentally, when I see customers coming to Elastic, you know, it is when they have a problem that involves unstructured data at scale. Show me a customer, mid, small or large, that does not have to deal with logs. Everybody has to deal with logs. That is our sweet spot when it comes to observability and security.
You know, in all the examples that you heard of, they're doing a lot today with Elastic, but they are all, you know, if you look at one common element, if they started on observability or security, where did they start from? It was either log analytics for observability or SIEM for security. From there, then that becomes the natural opportunity for us to expand. I would expect that we will continue to be in a place where we cement our leadership in those areas, because that's our core sweet spot. We will land from there. Those are big subsegments of the market. Absolutely from there, we will continue expanding. That's the reason why we really focus on our pricing model, because the consumption-based pricing model is just designed to be viral in terms of adoption and expansion.
Once you've landed with log analytics, it's a much easier conversation to tell somebody, "Hey, you don't need to count the number of applications that you want to instrument with APM. You can just try it out on a few apps. Then if you want to instrument more, go ahead and instrument more, because it's that incremental resource consumption that you're going to pay us a little bit more for. But you're not having to count that you have 500 applications, then make a big upfront purchase and outlay of commitment to us." It just doesn't work that way with our pricing model. The growth is more graceful, but it's more sustainable. That's really what we love about it. I would expect that journey is going to continue. I don't know. Should I?
No, makes sense. Koji, go ahead.
Hey, guys. Koji Ikeda from Bank of America. Thanks for doing this. I wanted to follow up a little bit on Raimo's question, thinking about kind of the enterprise sales motion. Matt, you talked a lot about enterprise go-to-market, how you're thinking about these different levers. When I think about Elastic too, though, I also think a lot about a product-led growth, you know, bottom-up product-led growth motion. I guess a pretty basic question, you know, when we think about the $2 billion revenue target, if we could maybe bifurcate that into how much of that is coming from a traditional enterprise sales motion versus more of your traditional product-led growth?
Yeah. Maybe Koji, I'll talk about that. Look, I mean, when I think about the mix of business, it's really hard to disaggregate because, you know, as you heard people talk about the customer journey, right? Michael had several great examples in his deck of how people started really small then extended to another use case, and before you know it, we were starting to penetrate the enterprise. Look, we want our salespeople focused on areas where they will be the most productive. You know, you don't want enterprise salespeople focused on very small ticket items because then the model doesn't work. Fundamentally, it's those customers. If you think about the land and expand motion as those customers continue to grow with us, the enterprise selling motion kicks in.
It's not trying to necessarily say it's either this motion or that motion and a hard line in the middle. They actually end up merging together. It's almost like a continuum, and it's bottom-up meets top-down. That's the way conceptually I think about it. Anything you'd add to that?
I'm viewing the same, almost as a natural evolution, if you will. It's just really occurring. I talked a little bit about some of the large banks I've spoken to recently who have a lot of instances of Elastic across their organization, and we've evolved there. It's just naturally fitting that we have an AE, a senior AE coming over the top and have conversations with more senior executives and really tie it all together. That's what the customer's looking for, giving them that visibility, centralization, and then looking strategically and improving our relevance over time. It can only be positive. That's naturally happening, and we're just accelerating that in my view.
Yeah.
I would add to that, like what you heard with some of our customers today. I'd add to that, when you're mission critical with large customers, you have to show up and service them that way. That's something else that we're evolving through our organization to make sure that we provide great service to companies like Biji. His operation is at absolute massive scale. At an enterprise level, it's not just in sales, but it's also through the organization, through our support organization, CS organization, how we respond and support them through their growth.
Pinjalim, you had a question.
Yes. Hey, a great presentation, everybody. This is Pinjalim Bora from J.P. Morgan. Shay, I want to ask you one question on OpenTelemetry, and what do you think its impact could be on observability, and if you think it actually reduces the cost of switching over time in the observability's landscape?
Yeah, happy to. OpenTelemetry is an effort by the Cloud Native Foundation to create a more consistent set of APIs to monitor your infrastructure, starting with areas like APM and infrastructure metrics, and then moving towards logging as well. We've been participants, by the way, in this effort for a few years now. We're actually contributing some aspects, for example, our schema definitions and things along those lines. I think the opportunity that OpenTelemetry presents is a lot of potential greenfield. If you look at, for example, one of the challenges that companies have today in terms of monitoring is just instrumenting your infrastructure.
If you look at the number of applications that are deployed across an organization, when you have to go and ask them, "Hey, like, go and start to instrument all of your servers, your instances, your applications," and they need to go and do that's like a very expensive process. I think OpenTelemetry presents an opportunity to just go and have an always-on instrumentation at a level that is consistent from an API perspective. Everybody supports the same thing, everybody supports the same infrastructure. Then there's a question of, where do you ship that data? To me, that represents a significant increase in the amount of observability data that someone can capture broadly, right?
It's like if the market has, I don't know, six something gigabytes of observability data you can capture, that represents a significant increase of it. Then it gets to the capabilities of where you actually can store the data. Yes, that can help reduce the switching costs, but to me, by the way, this is music to my ears 'cause that means that people can move to Elastic faster, and they can realize our, to a degree, almost like unfair advantages as an observability platform quicker, like our ability to handle unstructured data, our ability to store large volumes of data at a significantly better cost compared to other competitors.
That gets me excited because when, you know, you take the OpenTelemetry Firehose and you put it at another place, and suddenly that comes in, and that also means that you could instrument much more of your applications in your infrastructure. That's a very exciting proposition to have.
One for Janesh. It's pretty clear you're very confident on the $2 billion number. I was thinking about the operating margin side, right? Why not give us a target on the operating margin side as well, more granularity there? Are you trying to kind of keep some powder dry, I guess, in case growth accelerates and you need to invest more in the business?
No, that's not the reason why I haven't put a specific number out there. Look, when we set the goals out there for fiscal 2025 and set the goal at $2 billion in terms of revenue, and then talked about several points of operating margin expansion, again, we just didn't specifically quantify it, because we haven't done that internally yet. It's just too early for us to do that in terms of how we think about the planning cycles and so forth. That's the main reason. Now, look, if growth re-accelerates and we see ourselves, you know, driving significantly above the growth rates than we talked about, then we will stop and ask ourselves, should we continue to make investments in the business at an appropriate rate to drive that growth?
I think that would be, you know, a good set of conversations to have, healthy set of conversations to have. That's not. I'm not trying to hedge my bets and keep optionality open. I'm just, we just haven't gotten that far ahead.
Hello. The customers mentioned several things on the panel. One uniform message was cost savings, and cost either ingest data or whatever. The hyperscalers love you because, you know, you're running the meter, but customers are using you for getting the meter down. This cost advantage seems to be playing out. Maybe you can dimensionalize that, especially in logs and SIEM. It seems like you have a material cost advantage. Why is that not yet playing out in APM and IM? I think you've said it, Ash, a couple times to me that, you know, you're really centering around logs and SIEM, and APM and IM. Well, maybe that can be pulled through later.
If you have this material cost advantage where your customers have even articulated that, why and when will that cost advantage, especially in cloud, and especially with cloud ingest and all that, manifest in APM and infrastructure monitoring, which are maybe the fortress Europe of your peers? Second question is on share dilution. You used a 5% target. Why is that the right number? Seems high. Thank you.
Maybe let me touch the first one, and then I'll ask Janesh to respond to the second. On the first topic of the advantages that we have in terms of being able to deal with massive amounts of data at scale in a very cost-effective way, that is not just relevant to logs. Obviously, we are able to deal with logs in terms of being able to ingest, index everything really well, but that cost advantage applies to everything. We are continuing to make it better. I think one of the things that Steve touched upon was the work that we are doing in terms of reducing the storage costs for time series data. That's gonna, you know, show up in our products in the not-too-distant future.
That's gonna keep making that part of the story even better. You know, what I'd say is, the reality is that we started with logs and log analytics. We then delivered APM. I think we've given some stats around the fact that we have over 2,000 customers using us for APM on cloud alone. Our APM adoption has been growing quite nicely. We've delivered functionality to sort of round out all of these capabilities in a particular sequence, and what you're seeing is nothing other than that natural march along that sequence. On security, we started with SIEM, which is why you see the greatest adoption of where we are with SIEM today. We then came up with endpoint security, what we call XDR, and then cloud security most recently.
I would expect all of those to flow through just in that sense of time. That's all that's being talked about here, nothing else. There is no natural disadvantage for us in APM or infrastructure monitoring, or for that matter, in the other areas of security. Having said that, when you have a solution that is incredibly mature in terms of SIEM for security and log analytics for observability, I would any day of the week and twice on Sundays, start with that. Absolutely intend to always lead with that, because that gives us the ability to land, have a very strong cemented position in a part of the market that is large and growing, and then expand from there. That motion will continue. Can you talk about the other piece?
Yeah, sure. On the share dilution, the reason we set the target at approximately 5%, couple of things. One is we previously had a target of just under 5% out there, so it's actually not, no real change effectively from that. It's maintaining that, in essence. And also when we look at, you know, where the industry is in general and compare ourselves against many other companies, we in fact think that that is a target that is ahead of where many other people are.
Also I'll point out that, you know, if you look at our actual track record against that, in fiscal 2022, which also included a period of significant share price volatility and reduction in light of everything that was happening in the markets more broadly, our actual dilution was only 2.6%. So we've got plenty of room within that as well, but that's how I think about the framework. Again, we sort of manage that quite tightly within the company. So I know we're slightly over time. If there are any last one or two questions, we can go over by three or four minutes. If not, we can break and go over and enjoy some drinks and watch some live demos next door. Drinks sound good. Okay, let's do that instead.
All right. Thank you, everyone.
Well, thank you again, everyone, for coming. Ash?
Thank you.
Thank you.
Thank you. Hope you're able to join us. I know that the product folks are eager to continue the conversation. Just be gentle on them. They love to show you the products, but have fun. Thank you.
Thanks, everyone. Thanks again for coming.