Welcome to the next session. I'm really happy and excited to have the team from Elastic here. It's timely conference for us, actually, because you've delivered really kind of strong results last week. Maybe, Ash, we should start with you, like, what were the highlights from your perspective?
Yeah. So, like I said, the results were quite strong, 20% growth, cloud grew by 31%, 13% operating margins. And I just wanna step back and think about what really drove the business in the quarter. And there were three things that are worthy of calling out. First is, we continue to see momentum with the work that we've been doing with AI. I talked about in Q1, I had called out that we had hundreds of customers that were using our Elasticsearch relevance and RAG functionality for retrieval engine AI use cases. So hundreds more in Q2. Great momentum with customers, both at our conferences, but in general, throughout our feedback traction in that area.
The second thing that we saw is, you know, the plays we've been running now for several quarters on consolidating workloads onto our platform. We continue to see good traction with that, and some of those workloads also started to ramp up.
Mm.
You know, commitments that customers have made in the past, so that also contributed quite nicely to revenue. And then the third thing that is worthy of calling out, especially in the cloud, was the optimization trends that we had seen that started about a year ago, and not a year ago. They seem to have stabilized.
Yeah.
Talked about that in Q1, and we saw that again in Q2. And the sense that, you know, I get when I talk to customers and we're looking at the metrics is that, customers generally seem to have reached where they want to be. So now it's about them bringing more workloads on, and that's what we're focused on.
Yeah. And before I kind of... Given you worked in technology for quite a long time and have seen what, Ash, I wanted to kind of ask more bigger pictures. If we think about generative AI and what we understand about it now already, can you maybe talk a little bit about the role of data? Because, like, you guys, you know, are a company that kind of has been dealing with data all your life, in a way, working with data. Like, how important is data and clean data, et cetera, for generative AI?
It's incredibly important. I mean, think about the two things that you really need to build any kind of generative AI application. You know, we need the large language models.
Yeah.
These large language models, they all have been trained on some data set. These are inference models, right? So they're coming up with... They're inferring what's the right next word and then the right next word and so on. So you need the LLMs, but they've all been trained on publicly available information.
Mm.
If you want to use them in any business context, what you need is some way to ground that large language model in your business context in real time. And so the second thing that you need, naturally, is a platform that will let you do that.
Yeah.
That requires search. That requires search with the right context, with the right relevance, and that's where we come in. So the data itself, all your, your private business data, you know, we see as customers have so much of that on Elastic, our ability to now provide that bridge is something that is very exciting for us.
And then, without going too technical, but like, what are the tools that we think about and that we should think about like that you are kind of providing, to be able to deliver that?
So, there are a few aspects to this. So the first is, the vector database functionality or the vector search functionality that we provide within the platform.
Yeah.
We built that core into the platform itself, like, deep into the platform. There are some really nice benefits because Elastic was always... Elasticsearch was always written to be highly performant and highly scalable.
Mm.
We have customers that have literally tens of thousands of nodes of Elastic running in a horizontally scalable manner, and now our vector database can scale in exactly that same way because of the way we built it.
Yeah.
So it performs incredibly well. It scales incredibly well. The second big thing that you get when you get our X-ray functionality, the aspect of generative AI that I talked about, is everything else that you need to build a true Gen AI application, right? It's not enough to have vector search, because what people are realizing now is you need different kinds of search techniques to build the most, to get the most relevant answers.
Yeah.
And there is a term that is now incredibly popular called hybrid search. What hybrid search refers to is, you know, you use vector search, but you also use semantic search, you use text search, and, you re-rank the answers that you get from the techniques to get the best possible answer.
Yeah.
You need to do it with additional context, like geolocation. So if you ask a question and you're based in Canada, and if I ask a question and I'm based in the U.S., there might be a reason to get slightly different answers, because if they're talking about vacation policies and so on, that might be different.
Yeah.
Personalization needs to come into the picture. Document-level permission needs to come into the picture. Your access might be different from mine. So we provide that holistic set of functionality, along with our own embedding models. Like, so we've done a lot of work even in building out these embedding models for machine learning jobs to create these vectors.
Yeah.
The third thing that becomes really important is the APIs to connect to any and all large language models. So look, we believe in choice. Our customers keep telling us that they've tried using different large language models and to give they get different kinds of results.
Mm.
- depending upon their domain and their use case. So in our platform, we try to provide the ability to connect to OpenAI, Llama 2, Cohere, Vertex, Bedrock. Like, we just want to be the open platform that lets you choose whatever you want, and then do it in a platform where the fact that you already have your data in Elastic just becomes a natural advantage. So we are building all of that ease of use directly into our platform. So now you don't have to worry about, you know, learning a new set of APIs. You know, we've built integrations with, you know, technologies like Hugging Face and LangChain that are quickly evolving to be part of this new AI stack.
Then actually we think, like, there obviously is a lot of enthusiasm, like people are excited about AI and different startups coming, and they're kind of having apparently they're all the next best tool, et cetera. If you think about vector database and you're offering, you know, like, you're kind of in the market with the product, et cetera. Like, is there a way to think about that you're like... People like some of the private competitors will say, like, "Oh, we have like dimensionality. We have like so much dimensionality." Like, if you think about that, it sounds yours is more holistic because it's kind of, it, it's not just the vector database, there's a lot more like play in there.
Is there kind of a, from your perspective, like a playing field or a toolbox that you need to have to kind of deliver all of it?
When you think about what it takes to build an end-to-end application, you quickly start to realize that the vector database is a feature, not a product.
Yeah.
Because you have to first and foremost, bring all your data into that platform. Then you need to turn it into vectors, that requires, embedding models, and you need to, you know, build all that pipeline. Then you need to create into vectors. You store those vectors in the vector database, then you need to have the ability to query that vector database and get all the right results from it. Then you have to worry about, like I said, hybrid search, because oftentimes you're not going to get great results from vector databases. Then you need to apply other techniques for semantic search, for textual search, re- rank with results, the connectivity with the LLM.
There are so many aspects that if all you're using is a vector database, you're going to end up assembling something with 10 other tools and then building all these integrations yourself. You know, the reality is that the vector database, when the pure plays that emerged, you know, there was a lot of noise in the market, pretty natural for any new technology. But the majority of the successes that we have had, all those customers started with some vector database, because, I mean, we've really only been working on our vector technology for the last 4.5 years.
Mm.
But you only saw us really go into the market very aggressively in the last... since June, and made our announcement. So most customers had been playing around with something else and recognized that there's so much more that you need, and, you know, we provide it all. And we have an amazing vector database itself that has... You know, people throw up things like dimensionality, and we have more dimensionality than what any embedding model needs. So, it's kind of a, it's a fake premise, that you'll often hear about. But that's, we're in a place where we're actually, like, doing incredibly well in the market because of it.
Yeah, and it... Do you see that in the customer understanding, customer conversations as well?
Yeah, people have, look, the first three months were chaotic. Everybody is trying to figure out which side is up, and now there has been so much intense activity on it. You know, we participate in public benchmarks. We are very open. Like, our homepage has always been open, right? So whatever we do, we talk about it publicly, we publish benchmarks and so on. But it's become pretty clear that we have been innovating at a very fast pace, and everything that we bring to the table in terms of the breadth, that's what customers are recognizing that they need, and that's what's driving our success.
And then, yeah, to do a little bit on the... If you think about it, it does seem like AI, ESRE, et cetera, is helping you on the kind of search side of your business. And that's kind of something that, you know, with observability and security, we haven't really talked that much about the class. Hope, hope I'm not... And I say the classic search, you know?
Yeah.
Like, can you remind us, like how, you know, like, did you break it out, like how, how important was search? And am I... You know, do you see like a revival of that part of the business right now that, you know, it's becoming very, very critical in this new era?
Yeah, it's actually, I know you're thinking about it the right way, right? If I think about the personas, you've got the DevSecOps persona that, that buys observability and security solutions, and you've got the app dev persona, where they're getting the technology being used to build gen AI applications.
Yeah.
So we talked a lot about the building of gen AI applications. Even on the observability and security side, we do get used quite extensively out there in general. And with AI, what we're starting to see is that we're using our own technology to build additional capabilities like the AI that we talked about for observability, security already GA.
Mm.
People use those to actually improve the capabilities of the outcomes or to improve the outcomes that they're getting in the business. So, it's actually helping quite a bit, and those features are available only in the highest spheres. If somebody wants to access those features and use those, it requires them to use the enterprise level of the system.
Yeah.
That's the way we see it making.
And then, obviously, we're still early in that journey, but like, I've also seen you guys being very active with clients. I hear a lot of events from you guys. Has that started to translate into, like, customer conversations, pipeline building, et cetera?
Yes, significant. And, you know, if I think about our engagement with the community, and we think about the various ways in which we interacted them, one of those is our ElasticON Tours, which we do around the world. We had our ElasticON AI tour stop in San Francisco a few months ago, and since then, we've had sessions in Amsterdam, in Frankfurt, Bangalore, all coming up around the world. At every one of those, we see hundreds of customers show up, lots of participating on stage talking with their customers. They are using the technology. Partners are there.
Yeah.
On the sidelines of those, our sales people are having conversations with customers. This is front and center on, you know, for every senior IT executive in every enterprise account that we have.
Yeah. Then how would that translate into... Sorry, that's terrible. My actual question, how does that translate to revenue? Is that more platform usage, or is it an extra, do you have any extra, like?
Yeah, I mean, our monetization model is twofold. One is, for certain features, people will need to use higher subscription tiers.
Yeah.
For example, on the learning features, which you would use to then train ML models and so on, those are available only in the Platinum tier.
Mm.
If you think about the AI assistants that I talked about, those are available only in the Enterprise tier.
Yeah
Depending on what features a customer wants to use, they would need to be in the appropriate tier. The second piece is just the compute intensity of many of these workloads. It causes the meters to tick faster, and then that drives higher consumption, and then that drives higher revenue for us over time.
Okay. Yeah, yeah. Okay. But it's like, it does feel that we're still early on that journey to kind of.
Yeah, I think it's still relatively early. You know, if you think about how people are building applications, you know, as Ash said, for the last several months, people were still trying to figure out how that experience is great up.
Yeah.
Now they are making their choices and picking Elastic to be their partner of choice, and they'll build the applications. The applications need to be deployed, the applications to scale. In general, I think what you see when you have major shifts in the industries, people tend to underestimate how big they can be, but they tend to overestimate how quickly they'll play out.
Yeah.
I think AI will be similar. It's going to be a significant opportunity for us, but we're still in the very early innings. We're very pleased with the progress we've had so far, but I do think it takes time to ramp, and we are super excited about it.
And then, does it play into the client side or self-managed versus Elastic Cloud? Because as you said, there's a lot of compute, there's a lot of new stuff in there.
Yeah.
Does that change customer thinking about where they do, where they kind of utilize it?
I think customers can use the Elasticsearch platform, both self-managed and cloud. If you think about large language models, because of the size of those models, those tend to obviously be cloud-based, led by the hyperscaler. But if you think about where we come in, and if a customer's got their enterprise data in a self-managed form, by the way, self-managed doesn't necessarily mean on-prem.
Yeah.
It could be deployed in a cloud and managed by them in a cloud, in a different kind of setting.
Yeah.
We'll tend to continue to use AWS where they are, but over time, I do think we've got a lot of natural shifts, more out of the cloud, more expansion happening in the cloud.
Yeah.
We have seen significant progress from a cloud perspective over the course of the past several quarters now, as we continue to grow the business. I continue to think that cloud will continue to grow faster than the rest of the business.
Yeah. Okay. Ash, back to you, and I didn't want to have, like, the whole conversation on AI. In terms of like, you know, the advancement around platform, what you're offering, like, can you talk a little bit about the other work you're doing? You know, I'm sure you're not doing 100% R&D on AI now.
No, no. So AI has obviously been a big part of what we have done, but, you know, there's so much additional work that we've been doing in other parts of the platform. So one of the things that is very exciting to me is the new query language that we delivered called Elasticsearch Query Language, ES|QL. This is a pipe, and just to give you some context behind that, we always had very rich query functionality, search functionality, visualization functionality in the platform. But one of the areas that we often got feedback from customers on was, you know, something like a small query language, where it had some nice semantics to, you know, pipe the results of one stage of the query to the next stage. You know, they found that to be quite useful.
It made it very easy for developers to do very iterative kinds of query building, testing, et cetera. And they found that to be sort of a sticky feature of our platform. And as we have been doing more to consolidate workloads on our platform, spacing improvements, and it was really important to deliver that functionality. So we've been working on that capability for some time. It's not only a new query language, but a new query processor that does this kind of... And it allows you to do piping, it allows you to do lookups. In the future, it'll do even more enrichment with joins and so on. And so that piece, in beta, we got tremendous feedback from customers, lots of excitement.
Some of the wins that we have seen recently, where customers have moved on to our platform, significant improvements have come because of some of these innovations. Another very interesting thing that I'm excited about is the work that we have been doing on storage. We haven't been talking too much about it, but it's in, we're getting wonderful feedback from customers, and we keep everybody updated as that goes from preview to, you know, beta and then to GA. But, you know, we are focused on making the platform easier, more cloud native, making it possible for you to support different use cases powered by AI. I think there's a lot that's been going on, that's what gives us a lot of confidence.
And the question, you query language, which kind of closed the gap a lot to one of the incumbents. In your market or in your broader market around especially observability, there have been a lot of consolidation of late, or consolidation in a way that taken out, and even people get taken out option. Have you seen an impact already? Because there's only, you know, there's not many. I remember you provided a big note about observability war, and everyone was coming there, and now the field is getting smaller and smaller, like one of the last years. Have you seen an impact of that?
Yeah. So, we started to see increasing interest about a year ago, but it was not necessarily because of some of the consolidation that is now happening.
Yeah.
But really, you know, a lot of these incumbents were notorious for pricing models that weren't customer friendly. Just the overall costs associated with operating the platforms were exorbitant. But you know, when you have something that's sticky, customers need a really strong reason to consider something.
Yeah.
What changed was just the quantitative tightening that we saw and the budgets becoming tighter and people getting more worried about what they have to implement with flatter or reduced budgets. And our platform, we have, you know, we've built a platform over the years that scaled incredibly well, that was faster than these incumbents, was cheaper to operate at scale. But when you have something that's established for 10 years, ripping that out requires more than that. And the economic pressure started to create incentive for a lot of customers. So we started to see more and more activity. And, you know, that's why in the last few quarters, we've been talking about making commitments to us, moving existing, you know, moving workloads onto our platform-
Yeah.
Displacing incumbent. We have started to see how... That's why we doubled down on things like ESQL
Mm.
which takes away, you know, the last remaining hurdle, if you will.
Yeah.
In terms of the M&A activity, I can't say that I've discreetly seen someone come to me and say, "Because of that, I want to switch over right now.
Yeah.
But this has been a trickle that's now has been building for some time.
Yeah.
I feel very good about not only what this means for us in the out years, but we are leaning into it.
Yeah. Yeah. And then, you talked about, like, people are aware of how much they're spending, and, you know, that brings me to the cloud optimization part. What are you seeing there in the field? And question, it's kind of slightly to ask you guys, is that first of your business model, you were always kind of, you know, like, pick the right word, like, yeah, I could take more. You know, it was always possible months ago with you because, like, the way you priced it, was always an advantage. Like, did, did you see kind of the cost optimization, like, as much as the other guys, or how are you all?
You know, I don't know. I don't want to compare between us and other people necessarily, but you're absolutely right that when it comes to, like, the value that we delivered for a price, you know-
Yeah.
That's why we saw more and more successes. It's got tighter. You know, what I'd say is that, in terms of the optimizations that, you know, we've seen, that's kinda looks stabilized at this point.
Right.
So, I believe that our price model continue to get better.
Yeah. Okay.
I don't know if there's anything that you-
Yeah, go ahead.
No, like, like, on that note, like, if you think about it, the one question I got, like, the results for the quarter were really good, and then you're so good optimistic. How do I kind of maybe talk a little bit about your thinking there?
Yeah. I'll go back to the themes that Ash mentioned at the start that we saw in the quarter, right? One is we started to see some initial contribution from GenAI and lots of, lots of customer activity engagement, but some very early contributions in terms of revenue.
Yeah.
We also started to see, we've talked for some time now how customers have been making commitments to us, and we start to see people ramp against their consumption, against the commitments that they previously made. And the third theme was around the optimization, trend, and that sort of leveling off. And so as we thought about all of those and started to think about the future and how we think about guidance, you know, the reality is that customers can fluctuate from time to time, and we've seen that certainly in the last several quarters, the industry broadly.
Yeah.
That's just something that we wanted to keep in mind as we built the guidance. It's not like we've seen anything dramatically different in the business that we are signaling in any way.
Mm.
But we just wanted to be... We wanted to be thoughtful and make sure that to the extent that there is some level of future fluctuation, that we consider that as we build the guide. So we were just being prudent.
Yeah. Yeah, yeah, more prudent. Okay, makes sense. I mean, have you-... at the end of the year, we believe it.
Well, and consumption can fluctuate. We just wanted to be clear.
Yeah. Yeah, yeah. Okay. Perfect. Makes sense. And then, let's turn over to cost and effect a little bit like, some players have started to think more about, like, okay, how do I think about investments now? We don't want to be forever. There's always, like, lead time in terms of... How are you thinking about that, you know, the market? And you delivered really, really so far.
Yeah, no, we appreciate that. It's been a lot of hard work from a lot of people to make sure that we deliver those numbers. But we fundamentally think about the margin on a full year basis.
Yeah.
So we're very happy with where we are on that, some of the numbers that we've committed to for this fiscal year. Fundamentally, the approach that we've taken is to continue to invest in the business, but to invest in areas where we see the best opportunity to continue to drive growth.
Mm.
There is very natural operating leverage that is inherent in our business model. As we continue to do that, we are making investments in the right places. I think on the Q1 call, we had talked about shifting some of our investments towards GenAI and R&D and in marketing.
Yeah.
We've continued to do that, as well. We're making sure that we invest in the right way in the growth market functions to make sure that we build in that capacity going into next year. A lot in the R&D areas and many of the areas that I've talked about. But all of that within the overall construct of making sure that revenue grows faster than overall expense.
Yeah.
That will give us the operating margin expansion that we're looking for.
Yeah, that's good. And then, like this time last year, you were here at the conference, and we talked a little bit about, like, how you budget and how fundamentally you're changing a little bit, how you do that, how it's more returns focused, given the high interest rate environment, et cetera. Is that still playing out, and do you think is that like the new modus operandi, like how you do this going forward as well?
I think in general, yes. What we want to be is be disciplined about how we're investing in the business. And we've brought in a lot of experience with in many different areas. When we hire sales people, we've got our models around, and I want to see that. When we make investments in marketing, when we make investments in engineering. As long as we're doing the right thing, seeing the right results, if we fail, it's okay, but we want to fail fast and make sure that we course correct pretty quickly so that we can then continue to shift dollars to the, to the right things. That's been the approach that we've been offered. It's worked quite quickly for us. So, yeah.
Yeah. And you don't have to answer, but like, more conceptually, how you think about it, and it's more for the industry. The number one question I'm getting all the time is around... Okay, so we're having a, you know, suffer, you know, we can't, like, everybody came down to a certain level. And so now we need to think about, like, how do you think about it conceptually in terms of, like, the deceleration, but then, you know, everyone got bigger?
Yeah.
So, how do you think about, like, this regrowth in terms of, you know, like, a much bigger opportunity set, ever bigger? Like, how does this, how will this play out going forward?
I give you my broader perspective, and then left for national also. But, you know, when I look at the observability and security model, I think those, I don't see any TAM expansion. I don't see anything that's necessarily going to expand the TAM on those. AI does not seem to be expanding the TAM. I haven't seen anything that would. So to me, like, the budgets, at least in the conversations that I'm having with CIOs, seems to suggest that budgets are going to be somewhat flat.
Yeah.
So it's going to be more about, you know, being able to take share, and that's what we're focused on. So how do we use GenAI to, with the AI assistant, I think, as an example, to be a different computer?
Yeah.
Now, to take share because of some of the market factors that you talked about, like, that's where I see the energy and where to put it. When you look at the search market, GenAI is driving a TAM expansion there.
Mm.
How much, how big, how fast? I think that is, you know, TBD. But, DocuSign use case that I mentioned on the call, like they In the past, they used to only do metadata search for documents. Now they want to do full document search across images, across, you know, using semantic search to search for like the context and meaning of certain things and so on. And that is a massive expansion of the use case that, you know, never existed in the past.
Yeah.
So I fully expect that in the coming years you're going to see search have that expansion. We'll see how much. And so as we think about where we are focusing energy and what's the product mixture, it's not yet clear what the rate of the TAM expansion might mean and by when.
Yeah.
But we are being very focused on GenAI because we are seeing that with search, and then we are looking to see how we can leverage our GenAI leadership to take more security. That's, that's going to be the name of the game in the coming, in the coming years.
Yeah.
Yeah. And from my perspective, I think everything Ashutosh about how the business moves up into the right over the long term makes sense. As we navigate that quarter by quarter, we'll see where, how we execute a lot of our 25%. So we just want to navigate that. I'm sure we don't get too far ahead of ourselves in terms of how we think about the future, but, overall, from a long-term perspective, super excited.
Yeah. Perfect.
Yeah.
Good. Closing. Thank you, guys. Thanks, guys.