Good morning, everyone. Thank you for attending. I'm Rob Owens with Piper Sandler. I'm the Co-Head of Tech Research, and I manage our Security and Infrastructure Software Practice. So thank you for attending this morning, day two, and really happy to somewhat continue the open source theme, I guess, with Elastic here, and Ken Exner, who is the Chief Product Officer. So, Anthony is with us, hiding in the front corner if you wanna beat him up later.
But, really a unique opportunity, I think, to sit down with Ken and understand the Elastic product set and where you guys fit in the world, and I think that it's always, you know, from my perspective, been a very interesting toolkit that's applicable to a lot of different applications.
Yeah.
But there's a lot of best-of-breed technologies that you go after, so obviously, a consolidation play. And then you have GenAI overtones as well. So, maybe we'll start there, and I think it's just a massive opportunity for the company. Why do you feel Elastic is one of the better-positioned companies to benefit fom GenAI?
From Generative AI. Hi, yeah, glad to be here. I'll start with one of the things I talk about internally at Elastic when I talk about this with our teams. I think with every one of these sort of replatforming events in technology, there's usually two types of winners. There's the people that build sort of the foundational technologies, the picks and the shovels, and then there's the people that, you know, build things on top.
Right
Like, the solutions on top. I think at Elastic, we actually have a unique opportunity to play on both sides. So we have an opportunity to be a foundational technology with Generative AI with things like Vector Search and ESRE. But we also have the ability to use these capabilities to power our own solutions, to make sure that we can create a transformative experience for observability and security. So I'm excited about both sides. I'm assuming, though, when you're asking about this, you're talking about more on the foundational-
Yeah
C apabilities. And there, I think we've benefited from a couple of things. A little bit of luck, a little bit of history, a little bit of smarts. But we started down this path about five years ago. We were one of the first vector database. I think someone actually said, "You're the original vector database." About five years ago, we started adding Vector Search capabilities to Elasticsearch. And this is because customers wanted to do image search, they wanted to do video search, they wanted to do semantic search, and these kind of things require you to store vector embeddings and query vector embeddings.
And we started adding these capabilities to Elasticsearch to support those use cases. And by the time Generative AI came about, where people wanted to use vector databases to pass context to an LLM or build Generative AI applications, we had a fairly mature offering that not only handled Vector Search, but all the other things that someone might need. So if you want to connect to different data stores in your business, you have 200 and 50 connectors. If you want to run inference on data and store a vector embedding, we have that.
If you are trying to combine different techniques, like geospatial search or graph traversal, together with Vector Search, you can do that. So I think the combination of these technologies made us sort of, y ou know, put us in a very good position where we were the clear leaders for enterprises because we had all the enterprise capabilities, you know, RBAC and ABAC and login, audit login, but we also had the best search capabilities.
Mm-hmm.
When you look at what people wanna do with vector databases, they're essentially trying to do a search operation. A vector database by itself is a fairly simple operation. You're storing vector embeddings and querying them. What are you trying to do with that? You're usually trying to do something like image search. You're usually trying to do something like semantic search, or you're trying to do RAG. When people realize that, they realize that what they actually need is a search engine. Because when you're trying to do these kind of things, it's a search operation.
Mm-hmm.
You're trying to get the most relevant results to do something, to pass an answer to a customer, to pass an answer to an LLM, and you're gonna want to have the most relevant results. I often say, like, if you're gonna build a Generative AI application, you want it to give you the right answer, right? You know, you're. It's gonna give you one answer. That one answer better be right. So, like, what are you gonna use?
Are you gonna use sort of a standard, you know, Vector Search result, or are you gonna use something that's been tuned and optimized for your particular use case? And I think that's where we're different. We have all the capabilities to optimize retrieval and get the most relevant results.
That's fantastic. And can I, I did jump the gun a little bit.
Yeah, no, of course.
I apologize, because you're probably newer to this audience, so maybe we'll have you introduce yourself a little bit at the-
I'm Ken, yeah.
Second question, and, just how long you've been at Elastic, your background?
I'm Ken Exner, Chief Product Officer at Elastic. I've been with the company for just over two years. I hit my two-year anniversary just a couple weeks ago. And before that, I was with Amazon. I was with AWS for more than sixteen years. I was actually there from the beginning of AWS. So I got to see AWS in its wild ride, and finally left.
That's fantastic
A couple of years ago. Yeah.
So what brought you to Elastic then? As you looked at the-- you obviously knew the technology.
Yeah. Well, it's a couple of things. I saw that the company had, you know, good technology, but they were playing in three spaces that I knew had good opportunity.
Mm-hmm.
They were leaders in search, and I saw search and Generative AI growing. They were sort of having the start in security, and I saw the security market was growing, like crazy. But they had a really good foundation, which was they had a security analytics platform, a SIEM, that could be the sort of the foundation for building a SOC platform. And then same thing with logs and observability.
Again, a high-growth space, where they were the leaders in logs, and they could use this leadership in logs as a way to get into other spaces in observability. So, you know, everyone needs logs. Everyone every company needs logs.
Not everyone needs APM, not everyone needs the other parts of observability, but if you can land with logs, you can expand to the adjacent spaces. So I saw a good technology, a good foundation for getting into each of these spaces.
Mm-hmm.
And the start of a leadership position. So leadership in AI and Search, leadership in SIEM that could be expanded into adjacent spaces, and leadership in Logs that could be expanded into adjacent spaces and observability.
Excellent. One topic that's come up recently is just the change in open source licensing, and maybe you can help explain to the audience what that means relative-
Sure
T o Elastic.
Sure. So a quick background here is, Elastic was started as an open source company, has always sort of been an open source company. The ethos inside the team, you know, we live and breathe open source. We participate in the communities. We blog about things as we're developing them, so the company thinks of itself as an open source company. But a few years back, we changed a license to use SSPL, which is the same license that MongoDB uses. It's a derivative of AGPL, with an additional provision that cloud services can't be built on this.
One clause, that's the only thing that differentiates it from AGPL. Because of that, and it's not considered an open source license. OSI governs what can be called an open source license and what cannot, and AGPL is an open source license, SSPL is not. So we started to have to use the term, free and open.
Mm-hmm.
So people could still download it, development still happens in open source. It is still free to use. It is still part of this developer motion that we have, but we had to call it free and open, as opposed to open source. The thing that changed recently is we added AGPL. We essentially triple licensed our source code, so that we added this as a new choice for people that wanted to use AGPL, and because of that, we can start calling it open source again. This matters, I think, for people that want. Like, have religion about this and want to know that something is open source in the OSI definition of open source.
And as we look towards the Generative AI space, a lot of the Generative AI space is happening in local tools. You know, developer or practitioner downloads something to their laptop, and they start developing there, and they play there, and they typically use open source tools. So this would allow us to call ourselves an open source vector database, and be competing very directly against the other open source vector databases. So it was an important change for us to make for that reason. I think if it helps us just a little bit with people that care about that, then it's worth the change.
Excellent. It's often noted your solutions come at very competitive price points.
Mm-hmm.
Is there something structural there, or is this more a pricing philosophy for Elastic?
It's more structural, so I think if you look at other companies, they typically have sort of a portfolio of products that are priced independently. They have a metrics product and an APM product and a RUM product-
Mm-hmm
E t cetera. Same thing as security. And they price them differently. What we do is we have a consolidation play, but it's a consolidation platform. We are one SKU. So if you are buying us for observability, you are buying an observability platform. If you're buying us for security, you're buying a security platform, one SKU. So in observability, you get metrics, you get APM, you get RUM, all of it as part of one product. And the reason we're able to do this is that we have a unified data store that works with all the different types of data.
So it is agnostic to whether it is storing tracing data or metrics data or log data. It is one data store. And what this allows us to do is it gives us the efficiency of having one data store, as opposed to 10 different data stores and 10 different products and 10 different. And we can optimize it, because we own the data store.
Mm-hmm.
So like, if you look at what we've done in the observability space, we've in the past year, we launched something called TSDB. It's a time series database optimization for metrics data that allows us to optimize and lower the cost of storage of metrics. We can do that because we own the data store. And we were doing something similar right now with LogsDB. It allows us to further optimize storage of logs information. We own the data store, and it's a unified data store across all these different products. So structurally, that's very different than what others do.
Fantastic.
In fact, some of the other vendors also build on top of our data store.
Sure, they absolutely do.
We have the advantage of being both the solution and the data store.
So when you talked about coming to Elastic, obviously it's a, it's a fantastic solution-
Mm-hmm
Right? That has extensive opportunities. We think about SIEM and next-gen security as we think about observability, but you've got incumbents in those markets, that's all they do. What's Elastic's challenge to really move into leadership, and you've seen success, but I think you're known as the search company still, right?
Mm-hmm.
You kind of dominate that angle of it, and we all talk about GenAI relative to your search opportunity, but maybe start to weave in-
Sure
T he other two vertical opportunities and what it takes for you to really move into a leadership position.
So we are growing quite quickly. According to IDC, we are the fourth largest SIEM, and the fastest growing. So the ones ahead of us are the incumbents.
Mm-hmm
The first generation SIEM. So we're like the fastest growing, and we're the largest of the new generation. And then same thing in observability, where we're growing quickly. I think there's a couple things that play to our advantage. One, if you look across both observability and security, we've had a couple generations of products. There was the first generation of SIEMs that happened about twenty years ago, and they're being replaced by a second generation of SIEMs currently. The difference is, essentially, the first generation of SIEMs were about collecting data, putting it in one place.
Right.
The second generation is about doing automatic detections and automated response. Same thing with observability. We've gone through a couple generations. There was the first generation of monitoring tools, which were point solutions, and then there was the cloud-based observability platforms, which is what we have now. I think both of these spaces are ripe for disruption by Generative AI. And when I talk about this, I know everyone's kind of tired of hearing, you know, AI washing. Everyone's talking about AI.
You know, you go to a conference, and everyone's introducing some chatbot or something that, you know, "We have AI, too." But there's something different about observability and security, and that's that if you think about what these practitioners do, there's a ton of pattern matching that's involved in their day-to-day work, and there's specialized knowledge that they accrue over time. And the combination of these two things make it incredibly ripe for disruption because you can take the specialized knowledge that this person builds up over time, and you can teach that to an LLM.
You can take the pattern matching, and you can have a machine do that. Machines are better at pattern matching than people. So I do think that both spaces will be fundamentally disrupted by Generative AI, and it's gonna happen in the workflows.
So when people first started introducing AI into applications, they did this as a thing on the side. But what you're gonna start to see, and what you're starting to see with Elastic products, is the workflows themselves change. We recently launched something called Attack Discovery in our security product. And what Attack Discovery does is it automates the work that a security analyst does to figure out alert triage.
Mm-hmm.
So, a security analyst. Just quick background, they'll spend their entire day looking at alerts. They get fired on detection rules. So, they'll literally have hundreds of these that they have to sift through, and what they're trying to do is to figure out which ones are false positives, which ones are real, and which ones are part of a coordinated attack. What we've done is we take all these different alerts, these hundreds of alerts, and we feed them into an LLM, and we automatically map the attack path.
So, automatically figure out these ones are false positives, these ones are actually real, these ones are part of a coordinated attack, and here's what you should do about it. And we're able to essentially take hours and hours of work and automate it for a security analyst.
And we show this. We launched this at RSA, and we had people like almost crying because they were like, "You've eliminated like ten hours of tedious work for me every day." So I think that's where Generative AI has the possibility to change both the security and observability products is when you start to automate the work that practitioners do and make every practitioner an expert practitioner.
And it's always a bit dangerous to ask the product guy a marketing question, but is there something that Elastic needs to do, in your view, to kinda unlock those opportunities? I mean, obviously, the product set has those capabilities, and we talk about consolidation quite a bit-
Mm-hmm.
And it makes sense. But just to get that leg up, 'cause it's a very strong product set, and obviously-
Yes
From an ROI perspective, offers tremendous benefits.
I think the hard part for us is when people see it, they get it. They-- Like, when I just, what I just described, like, so-
And so you think those buying centers are coming together, too, if I start to look-
Oh-
At security and observability, or are they still somewhat disparate?
I think they're still-
That remains a challenge.
I think they're still somewhat disparate. There's opportunities in top-down selling to appeal to consolidation. People are looking to, you know, save money, but they're also wanting to invest in platforms that are gonna be future-proof. So you're not gonna wanna invest in something that's gonna create even more lock-in. So people are cautiously moving towards consolidation, but they wanna invest in something that they know is not gonna, you know, create problems down the road and, you know, lock them into something that is proprietary.
So I think the story we offer businesses that we're completely OTel-based, that we are completely standards-based-
Mm-hmm
I s a very compelling story because if you're gonna consolidate, you should consolidate onto an open platform. And like, they're like, everyone we talk to, like, nods, like, "Of course, why would I want to consolidate onto something that creates even further lock-in, that doesn't actually help me long term?"
Sure.
But in terms of the Generative AI aspects of this, and like, our challenge is showing people. I wanna show every single customer, because when they see it, they’re bought in. So it’s creating those opportunities where we can show this in action, because when we show, we win. When we bake off, we win.
But to that end, I think that, metrics around ESRE-
Mm-hmm
A nd I am gonna use a Better Than Ezra title at some point, Anthony, just for you, has been very encouraging.
Yeah.
Big question we're all contemplating, and I'd love your crystal ball, is just: When do a lot of these gen AI applications move into production? Where do we just really begin to see the knee and the curve versus the environment we're currently in?
I think we're starting to see that. I mean, we've seen the encouraging start over the last couple quarters, where we're starting to see the beginning of growth for these types of workloads. If you think of... If you step back and sort of think of history as sort of before ChatGPT and after ChatGPT. So after ChatGPT, it was, it's less than two years now. So yeah-
It's crazy.
Less than two years ago, ChatGPT launched, and I think what happened is people spent the first four to six months, just sort of coming to terms with what just happened.
We were writing limericks, let's be honest.
Writing limericks? Yeah.
Limicks.
We were trying to figure out-
Limick to letters.
How to get-
Please apologize to my wife.
Poetry in Yoda's voice. So yeah, people were playing, and then it dawned on us, like, this is gonna change everything. So then I think what happened the rest of last year is people started figuring out what is gonna be our AI strategy. Every executive I was talking to was trying to figure out what their AI strategy was. And because they were getting pressure from their boards-
Sure, absolutely.
And everyone else. In some ways, it reminded me of cloud computing, like, around 2010, where everyone was trying to figure out what their cloud computing strategy was. Same thing was happening last year around Generative AI. Everyone was trying to figure out: what is our Generative AI strategy? It created new budgets. It created sort of an atmosphere of experimentation. Everyone wanted to start prototyping and picking things to start working with. And then coming into this year, I think we started seeing this aspiration turn into experiments.
People started actually developing things this year and starting to take it to production. I think this grows a couple ways. One is, those experiments are gonna turn into things that turned into production, and they're gonna grow as they get rolled out into production, but they're also just the start. What people are doing is they're picking one thing to start with, to learn and experiment.
Mm-hmm.
And then, you know, with that success, they're gonna move on to other things. Like I look internally at what we've done at Elastic, and we started with one thing that we said, "Let's try doing this," and we built a Generative AI application. It worked. It was great. We said, "Okay, let's build another one, and another one, and another one." And I think that's what's gonna happen, is it's not just taking that first thing to production that's gonna cause growth.
It's gonna be realizing the success of that, and then moving on to other areas of a business and saying, "Okay, we automated some marketing materials. Let's automate some support. Let's automate some legal work. Let's automate some other areas." And that's gonna create, y ou know, one success is gonna turn into multiple successes, and it's gonna create many opportunities within these.
And are we in a broader GenAI arms race, where it's better to be first to market, or are we at kind of this point of making the right decisions? And as you think about a build versus buy, a lot of capital coming into the space, a lot of companies, you know, what's Elastic's view on how quick you need to be versus how careful you need to be?
I think it's a bit of both. I think everyone spent a bit of time trying to get started quickly, but then they ran into the reality of, you know, whatever they're developing needs to meet their enterprise needs, needs to be compliant with their InfoSec requirements, needs to be enterprise-grade, and I think this is where we often see opportunity, is not just with existing customers who are starting to pick us up because they already use us, but with customers who use something else and then realize: I actually need audit logging. I actually need ABAC.
I actually need these other capabilities that my InfoSec team is requiring, and the existing products I have don't do that. Or they start with something, and while they're prototyping, that doesn't scale to their needs, and they move to us because they actually scale to a level where they're actually not able to use that existing solution. They're not able to use the pgvector because it creates contention with their transactional database, or they're not able to use one of the purpose-built databases because it doesn't scale to their needs. And then they come to us.
We got time to take a couple questions from the floor if there are any. Go ahead, Ethan.
Yeah, like, kind of an ongoing debate around what the effect Generative AI will have on developer headcount, given the productivity increases, right?
Yeah.
I appreciate Elastic isn't priced on a per-headcount basis, but given your background, I'd love to kind of understand your view on that debate. Do you think the developer population is gonna shrink? Is it just gonna grow slower in the future? You know, how do you think this kind of changes?
I think the work of development changes. So, I have obviously been following a lot about the developer productivity aspects of Generative AI, and I view it as, like, when sort of the modern IDEs came out, they had autocomplete. And it was a game changer for productivity for developers because you no longer had to have... We used to have to have manuals of all the different, you know, function names and method names and have to constantly go back and forth, but you could suddenly have all that autocomplete.
You can not have to remember all the different variables and parameters. You can just autocomplete it. It was a boon for productivity, and it was like a 10x improvement in productivity. I think the same thing is happening now, where that autocomplete is autocompleting with entire snippets of code, entire functions that get generated, so it is the next 10x improvement there, but just as the last 10x improvement didn't cause there to be fewer developers, it just created more productive developers, more capacity. I think the same thing is gonna happen here.
It's gonna change the nature of development to be more about design and less about coding. The coding tasks become something that you know trust the LLM to do. It's more about designing and architecting. It allows everyone to sort of up-level their game and focus at that level.
Yes. I guess one final question: What excites you most in a two-to-three-year roadmap about Elastic? This is all obviously moving very quickly, but you're speaking to a room of investors, and three years from now, they'll say, "If I just would've listened to him then..." But if you look out on the horizon, what probably gets you the most excited about this story?
I'll tie it back to where I started, which is our opportunity to disrupt the observability and security space and our opportunity to be a foundational part of the modern Generative AI tech stack. I think both of those are very real, and I think we're the only ones who are thinking and are actually starting to show the product features that automate away the tedious tasks of observability and security. And like we're building this next-generation SIEM, we're building this next-generation observability platform.
So our opportunity there is not to compete you know symmetrically against you know the current players on a feature by feature battle, but to show what a next-generation observability and security platform looks like. People when they see it believe it. It is clear that the industry is heading in that direction, and then the same thing for sort of the foundational part of this, the foundational building blocks that we provide. We have an opportunity to start becoming a part of the critical part of the modern Generative AI tech stack. And that's exciting to me to be.
You know, when people go to build a Generative AI application, they build it on us because we are the vector database, we are the inference service, we are the capabilities that allow them to get the most relevant results to pass to an LLM. Being that foundational part of a Generative AI tech stack gives us a lot of room to grow and be relevant for years to come.
All right, Ken. Well, thank you very much.
Yeah.
Thank you.
Thank you.