Elastic N.V. (ESTC) Earnings Call Transcript & Summary

Good afternoon, everyone. I'm Sanjit Singh. I run the infrastructure software practice on the Morgan Stanley software team. Super thrilled to have once again this year, I think it's like 2 or 3 years in a row now. We initiated coverage this year, but it's good to have you back to the TMT conference. We have CEO, Ash Kulkarni; and we have Global VP of Finance, Eric Prengel. Eric, thank you for joining us. Before we get started, let me go through the disclosures real quick. For important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. If you have any questions, please reach out to your Morgan Stanley sales representative.

So maybe just to level set, Ash, we're coming off a quite an impressive quarter, post the Q1 sales execution initiatives you put up some really solid results since then, revenue growth sustained at 17%. Your cloud business actually accelerated a little bit to 26% from 25%. What surprised you about fiscal Q3, particularly with respect to your cloud performance? And how would you characterize the breadth of strength across your core segments and particularly in your enterprise segment?

Yes. Just in terms of the actual commitments that we saw the sales performance was very strong. We saw continued demand for our products. And it was pretty broad-based. So in search, which is arguably what we are best known for, GenAI continues to be a good driver for us. So in terms of commitments, we had 5 deals greater than $1 million in commitments that were related to GenAI, which is an uptick from the 3 that we saw in Q2, which was great. So search continues to do very well for us. But we also saw strength in observability and security. And in terms of the revenue performance, you talked about the cloud growth rate of 26%. For cloud, because it's a consumption-based model, consumption can be -- at times, it can fluctuate a little bit. But what we did see was we had several of our larger customers actually consuming faster than we expected, which is a really, really positive sign. And that group was different from the group that really was ahead of their consumption expectations in Q2, which gives a sense that it's nice and broad-based, which is something that we are very happy about. But the core fundamentals of the business remained the same. It was the strength that we are seeing in GenAI. It's the fact that we are able to get more and more customers to consolidate onto our platform for observability and security and the fact that sales execution came back to what I'd describe as the pace that we were used to in the past.

Awesome. And then maybe, Eric, for you, as we look at the outlook for Q4, it does imply a slowdown back to the double-digit growth level. What are the specific areas of where the team is applying prudence to the Q4 outlook and maybe the assumptions behind that?

Yes. So as you think about the issues that we had in Q1 around commitments, that's still playing through. That's a multi-quarter issue and that's still going to be a little bit of a headwind to Q4. If you think about year-over-year growth, there's some FX impact. And so we've calibrated that and we've obviously guided the actual currency as well as the constant currency growth rate. Interestingly, this year there was a leap year in FY '24. So there's one less day in FY '25 Q4 than there was in FY '24, that's about 1% headwind. And then as Ash talked about in Q3 on the sequential revenue growth associated with some of our consumption revenue, and that's the cloud consumption business. And some of that strength came from some larger customers consuming at elevated levels. And so as we thought about the Q4 guide, we wanted to put in a level of prudence around that, knowing that, that can fluctuate from quarter-to-quarter. There's also the monthly cloud business, and we've talked about that being generally a flattish business. And our expectation is that it continues to be flattish. And so that's baked into Q4. And then one thing that we haven't talked about as much as some of the assumptions that we're making around the macro. And so there's -- we're baking in the appropriate levels of prudence there for a lot of moving parts that we're seeing, and that's how we came up with our Q4 guide.

And looking at some of the themes from this year, one of the comments that you made, Ash, on last week's earnings call was execution getting back, getting back to pre Q1 levels. Can you describe what the reasons behind the original go-to-market adjustments and fine-tuning that you were doing and what is the potential for sales productivity, not just back to where we were, but to actually improve?

Yes. So just stepping back, what we did at the beginning of the fiscal year, there were some sales segmentation changes that we made. And the changes that we made were not atypical of what other organizations, our sizes are larger, have done in the past, just as they've grown. And there were 3 specific changes. One was -- at the very top of our customer segment pyramid, we call that our strategic segment. We added more accounts into that segment, and we added more sellers into that segment. That's a segment where each account executive has one to 2 accounts, but these are large high-propensity accounts that we believe we can grow them into multimillion dollar accounts for us. The second thing that we did was the next tranches are enterprise segment. In the enterprise segment, historically, we had a lot of accounts per account executive. Sort of not in line with what is considered to be industry norm or industry best practice. So we normalize that because we want to drive the right kind of behavior. We want to have more focus in our accounts and our account executives to go deeper and drive more meaningful deals so we can become a bigger and bigger part of an organization's infrastructure. And then the third thing that we did was all of the accounts that got freed up because we reduced the number of accounts per rep. We turned them into greenfield territories and assigned hunter profiled reps to those. So those were the things that we did. The changes themselves, we had a lot of conviction. We continue to have a lot of conviction that they were the right changes. The way we implemented them, we had a misstep in terms of the actual account transitions that didn't go very smoothly. And that affected 2 things. It has affected the pace at which we were building pipeline and the pace at which we were progressing pipeline. And that had an impact in Q1. Once we recognize the problem, we immediately started addressing it. And you've seen us do all the right things in Q2 and Q3. And like I said, the execution -- rate of execution has come back. The other thing which is important is the original rationale for the changes, we are starting to see some of those benefits. In the first 3 quarters of FY '25 we have added meaningfully more million-dollar customers than we did in all of FY '24. And to me, that's a really good early sign of the kind of activity that's happening in the sales organization. The fact that our reps are actually doing what we expect and what we desire of them not just close smaller transactions but close more meaningful transactions. And as I look forward, like I want to make sure that we are continuing to drive that same kind of behavior in the organization because as we grow and we scale to $3 million and beyond, it's going to be that kind of activity that will get us there.

Yes. And maybe just a follow-up then, as we still got a big Q4 ahead of us. As we start to think about fiscal year '26, Ash, what are your key strategic initiatives you plan to focus on to drive growth in fiscal year '26? What are the 2 or 3 objectives that you really feel like you have to hit from a product, go-to-market change management perspective?

Yes. So from a -- let me first start with the go-to-market because that's something that we were just talking about. From a go-to-market perspective, the changes that we made, we are really happy about, right? So they've all settled down, as we talked about. And we are right now in the middle of our FY '26 planning but where we sit right now, we're not anticipating any changes. But we'll make sure that as we get to the start of the fiscal year, we communicate transparently about everything that we are doing. But at this point, we're not anticipating any meaningful changes. Then in terms of the focus areas for FY '26. Arguably, the biggest momentum that we are seeing is in the area of generative AI. So I don't think that should be lost on anybody that what is happening now in the industry is we are seeing customers use us as a true runtime platform for a retrieval augmented generation as they are building these conversational AI apps, as they are thinking about agentic workflows or even something as simple as just building semantic search use cases, like we are being seen as the retrieval runtime platform, and that's a massive opportunity because we are so early in this entire evolution. You take any average organization, you have hundreds of applications in an organization, if not thousands, and we are still at the phase where it's just a few handful of applications that are being sort of refactored to induce AI capabilities to automate through AI. So there's a lot of opportunity ahead of us in the coming years. And we want to make sure that we are one of the platforms of choice. We continue to be in that position in the years to come. So there's going to be investment -- continued investment on the product side and continued investment on the go-to-market side to make sure that we can capture that opportunity. Beyond that, obviously, AI is helping us compete in everything we do. Features like Attack Discovery are making us more competitive in security analytics and SIEM capabilities like auto import are making us more appealing when it comes to observability and log analytics and so on. So that is part of the game plan. We want to make sure that we leverage the fact that we have a native AI stack and that we leverage it to make our observability and security solutions better and continue to drive in a more competitive way in FY '26.

Yes, makes a lot of sense. I really want to spend the balance of this conversation talking about those multiple opportunity sets and how AI is influencing them. Before we get though, Eric, I wanted to just visit the margin story because there's been a ton of progress on that front. How is the team thinking about balancing the large opportunities that Ash has described about? And why is now the time to drive maybe more margin expansion versus leaning in to capture that opportunity from an investment perspective?

Yes. So let's be rewind it to the start of '25, and we thought we were going to see a modest margin expansion in '25. We had the commitment issue in Q1, which slowed down the business a little bit. And when we saw that happen, we wanted to put a little bit of a hold on investing, just make sure that we could reconfigure the business to really make sure that we're driving the appropriate level of growth. And so we slowed down investment a little bit in Q1. In Q2 and Q3, we saw a strong commitment. We saw strong consumption that obviously drove upside to revenue. With that, we saw a strong operating margin upside. And so as you think about our FY '25 non-GAAP operating margin, the current guidance we have out there is for 14.7%. So strong operating margin, a meaningful increase from the 11% that we saw in FY '24. And we hadn't frankly anticipated it increasing quite as much as it did. As we look forward to FY '26, we see that the business -- the go-to-market is operating in the way that we would want it to we see an opportunity to invest in putting more capacity on the sales team into play. And obviously, as we invest in capacity, reps are going to have to get ramped and that's going to turn into commitments, which then are later going to turn into revenue. But we see an opportunity to invest in capacity in the field. We see an opportunity to invest in some of the overlays that have a solution specific focus. So we have a GenAI-focused presales team. We have a security-focused presales team. And so to bring more of those people on to help with solution-specific selling an opportunity to invest in marketing, and there's also an opportunity to continue to invest in product to maintain that leadership position that we've earned through all the product development that we've been putting out over multiple years. So with all that opportunity, we are going to be investing in FY '26. We're still going to be focused on balancing revenue growth with profitability. And as we said, there is going to be an increase in the operating margin in FY '26. It's just going to be a very modest one. And so you'll see our business grow, and you'll see our business also expand from an operating margin perspective.

Yes, it makes sense, especially given the better execution and the opportunities ahead of you, I think that makes sense from an investment perspective. So let's dive into the AI search opportunity at Elastic. Maybe start like high level, right? So in software, a lot of investors have been trying to figure out the timing of when this really starts to meaningfully impact different companies in the space. When it comes from your perspective and the Elastic customer base perspective, where are we in the cycle from your vantage point in terms of customers getting those meaningful consequential AI applications in production and that starting to impact numbers at Elastic?

Yes. So the first thing to understand it that we are truly very early in this whole journey. Where we are today, like you see some of the adoption metrics that we talk about, just in Elastic Cloud alone, we have now over 1,750 customers using us for building all kinds of GenAI applications. Think of that as design wins, right? Those are customers that have decided to bake us into their application. Not all of those applications will go into full-scale production. But next phase of that is -- that journey becomes the commitments. When customers decided they're going to put something into production, they'll usually talk to us about what their sizing needs are and we'll do a contract with them. They'll make a commitment and they commit to a certain spend, whatever that might be. And you've started to see some of that commitment show up in the 0fact that we now, in Q3, we did 5 deals over $1 million, right? So that's -- so the GenAI contribution to commitments is starting to now add up, which is a good thing. Over time, those commitments translate into revenue as those customers consume against those commitments. But I would argue that we haven't seen the inflection yet, right? Because you'll see any inflection that happens on the commitment side before it shows up on the revenue. And the reason why I'd say that we are still very early is if you just think about -- like I mentioned earlier, just the total number of applications, IT applications that organizations end up having within their business. And if you're a midsized company like Elastic, it's probably several hundreds of applications. Across different functions, across different parts of what IT implements and business implement. If you look at a larger organization, it might be several thousand. If you talk about like really large, like banks, telcos, et cetera, it's often in the tens of thousands of applications across different divisions, different global regions, et cetera. How many AI applications are you seeing being talked about in any organization today? It's in the dozens. In the largest, maybe it's in a few hundred. But you're not talking about anything more than 1% or 2% of their overall IT landscape. If you truly believe that large language models. And what you can now do with the combination of LLM and retrieval engines like Elastic, if you can automate just about anything that involves unstructured data and human processes, you can now automate them because of the fact that these language models have the ability to do inference and reasoning. You're talking about a very large opportunity over time. But where you are right now, we are clearly still very early, which is why a lot of the spend is still accruing to sort of the lowest levels of the stack, which is on the chips and on infrastructure, compute build-out. But this is a story that's been played out in the past with cloud computing and other things. Eventually, it rises to the infrastructure and then the application stack, software stack. Our goal is to make sure that we are preparing for that by being in as many places in as many of these enterprise and mid-market accounts as we can be to become their retrievable engine of choice. And our strength is the fact that we are experts at unstructured data. The kind of data and processes that people are trying to automate, Elastic has always been known as the expert in dealing with all of this kind of information, whether it's work documents, it's logs, it's PDF, like that's where we shine. So our goal right now is to become that platform of choice. And as and when that inflection hits, that's going to be something that we benefit from naturally.

Awesome. Just one follow-up on how you sort of framed out the journey, right? There's commitments and then that turns on the revenue and then we get more applications -- more of the application, real estate. But just going to the [indiscernible], what's the journey? What are customers doing before they make that commitment? It's like -- is that a lot of trial and usage of like the community offerings? What's sort of the journey to commitment, if you will?

Yes. So obviously, there will be a class of customers that will start with the community addition of Elasticsearch, in which case, they might not be paying us anything, but they are using it in a very basic way. A lot of the AI-related features are only available in our paid additions. So when they start to use those, like they need to first come and talk to us, either if they want to run it on-prem, they need to purchase a license from us or they go and just start using Elastic Cloud. Now Elastic Cloud, we have a self-service motion. So most smaller organizations will -- what think of as SMB, we'll just naturally go directly there and start to use it on a month-by-month basis. Most enterprises typically don't permit that, right? Most enterprises don't allow their users to just go and swipe a credit card. So those typically end up being our customers anyway. So they'll talk to us and they'll start -- if they have an Elastic Cloud contract. The great thing about Elastic Cloud is you're buying credits from us. You are not buying discrete licenses because we have -- we don't sell -- we don't have a different AI SKU. We don't have a different security or observability SKU. We just have a platform SKU. So if you have purchased Elastic, you have the right and the ability to start using all of these features, and we see that. Naturally, customers will start to use it. And that's all from the telemetry we can tell that there are over 1,750 customers that are using Elastic for building all kinds of AI applications because from the telemetry we can tell that they're storing dense vectors, they're querying dense vectors. They are using the ELSER embedding model, et cetera. And that then becomes an opportunity for us to engage with them and say, "Hey, how can we help you? What are you trying to accomplish? Let's -- we have a lot of best practices." And the presales specialist team that Eric talked about engages with these larger customers. We do workshops. We have a lot of prescriptive information on how to address specific cases around customer support our cases around e-discovery or cases around what have you, all the patterns that we have seen effectively and what LLM choices might be appropriate for them because I don't believe there is one LLM to rule them all. And you've seen -- we've seen different performance in terms of accuracy and relevance based on the domain and questions. So those are all the things that tend to happen before a customer goes, okay, this is -- I've decided this is what we're going to do. This is my size and capacity requirements, and that's when the commitments happen.

Awesome. Let's talk a little about the ESRE and [ NAI ] search. I think one of the challenges for investors is that, frankly, this category is in English. There's a lot of acronyms. And I think broadly, people know that there's a vector search thing, they may be heard of RAG and a lot of companies doing RAG and vector search. So maybe if you could sort of pinpoint the value proposition that ESRE and Elastic brings to bear when it comes to running these RAG style applications that differentiates you from like the guy, the operational database guys that are trying to embed vector search into their platform. Why will demand follow to Elastic versus others?

Yes. So first of all, when you think about a vector database is just an end in and of itself, I think you missed the forest to the trees. A vector database is a functionality that solves a particular problem. But really, what you're trying to accomplish is accurate, relevant retrieval right? That's the problem. What you're trying to do is make sure that you are allowing the model, you're giving the large language model just the right context to answer the particular question that might have been asked. And that could be as part of a chat-style conversational app. It could be as part of agentic workflow. It doesn't really matter. Retrieval is what matters in relevance is the most important thing. Vector search is one technique, but you'll often need a combination of vector search and textual search. You will often need to rerank the results because you might not get the ideal result and you might need to boost certain results to get the optimum overall result, which is why rerankers have become so popular, reranking models. The way you create the embeddings and how you chunk the data has an enormous impact on the accuracy of the results that you get. So having the right kind of embedding model, having the right chunking strategy, all of these things become really, really important. We always had this idea in the sense that we want to have the world's best vector database in terms of performance, in terms of scalability, et cetera. But we also want to think of this in terms of what is it that somebody is trying to solve? It's retrieval. Creating the end-to-end platform that provides all of these capabilities with the view of giving the best, most accurate results at the lowest possible cost in the most efficient way. That's what we've built. That's what we continue to focus on. And the last thing I'd say is making it such that every Elasticsearch developer is able to build an AI application without having to relearn a whole new set of skills. So we built our vector functionality directly into Elastic search in a very native way. And that's been our big strength. That's why we win.

Yes. I think the point to me was -- you need a solution.

Absolutely.

These are not like -- it's not just features. It's more than just vector search. And frankly, you've seen some acquisitions in this space in a couple of fields that sort of exactly points to the importance of embedding models and reranking capabilities which it seems like you guys have had a head start on.

We -- I mean, clearly, it's a validation of everything that we've been doing, right? We were very early to the space. We saw the promise in this. We worked on -- we started working on our vector database product like over 5 years ago. Nobody was talking about this. We built our ELSER model, our embedding model. We have had it out now for the last couple of years, our reranker. And you're seeing that others are trying to catch up. So it's natural, but -- this is our core competency. We know unstructured data better than that's our core strength. So we are going to keep running with it.

What do you see the opportunity around inference as a service? And what are you seeing in Elastic's opportunity to monetize more of an inference stack? Or do you see a chance for Elastic to become a customer's inference as a service vendor to pull in more of the revenue directly related to O&M on top of some of the other use cases like RAG functionality.

It's a definite part of our story going forward, right? So the first step that we did was we created a common inference API. We've always had this additive that we want to be agnostic to models, even though we have our own embedding model, our own reranking model we'll support other embedding models and reranking models. We don't really care like where you get it from as long as you're running it on our platform. And then even with LLM, we've had that same approach. So we built an inference API, which abstracts away the individual complexities and differences between different LLM APIs. The next step is this is something that we've talked about publicly at our user conferences is we are working on our Elastic inference service. And the idea there is like we'll first start with providing the ability to access open source influence models through the Elastic inference service. And -- that -- there's Llama. We see a lot of usage of Llama, there's Mistral, there are a whole bunch of large and midsized models that are now available on Hugging Face that are incredibly good at dealing with different domains of data. And that's going to be part of our story going forward. I can't tell you the exact dates, but like that's definitely something that we've talked about in user conferences and watch that space.

Looking forward to it. Maybe let's move the conversation over to monetization a little bit. And I'll show it up to Eric and Ash, definitely interested in your perspective as well. In terms of the vectors of modernization, Eric, when you look at the features that Ash spoke about. A lot of these capabilities are in the enterprise and platinum tiers, which have a higher rate card. What's sort of the storyline in terms of the uplift you see from when customers go from standard to platinum to enterprise?

Yes. We see a steady uplift in pricing as they go from standard to a platinum enterprise. That's obviously, it's a meaningful percentage. What we've seen that's been really interesting is that over the last multiple quarters, we have seen more and more customers migrating from platinum to enterprise. They can -- there's a lot of features that they can unlock with enterprise, some of the generative AI functionality lies in enterprises. So there's a lot of capabilities that people want to be able to access at that enterprise tier and it's really helped us to monetize that business. The way you think about, though, just broadly generative AI and how we can monetize it is one of the ways it gets monetized is that more people are going to be moving towards the higher tiers that they can utilize the feature set that is available to them. But also they're going to be spinning the cost faster, too, as they've got bigger workloads, more workloads that are running through our ecosystem. There's just effectively more data that we're managing for them. That's one way that we can monetize the business more as you think about GenAI. And Ash, I don't know if there's anything else you add to that.

The only thing I'd maybe add is that on our website on the pricing page, we do clearly call out the rate cards, if you will, between our different tiers. So it's very easy to get a sense of what is the incremental uplift. But like to Eric's point, our monetization strategy is pretty simple features that bring down your overall infrastructure cost in a very meaningful way. We monetize those because to our customer, the ROI is very obvious, right? They don't mind paying us because they're going to reduce their overall underlying infrastructure cost. Another area is where you're talking about management and monitoring of very large clusters, like that is something that customers are happy to pay for because that is a pretty significant pain point. A lot of -- all of our AI functionality is in monetizable tiers. And then we have solution-specific features like endpoint security, like that's kind of monetized. So there's a -- there are 4 categories roughly where we have meaty features that we monetize. And we've been quite successful at getting customers to vote up tier but also bring more workloads onto our platform because data -- the more data comes into our platform, the better our monetization story becomes.

Maybe using that framework. And I'll offer it up to both of you again. One of the popular questions that we get is if we take a traditional Elastic customer that's been using you for a keyword, BM25 search, and they're thinking about expanding with semantic search. What does that sort of do in terms of the revenue uplift for those customers that take an application that's doing traditional keyword search and now, hey, we're going to make this application do hybrid search? What's sort of the revenue uplift on those specific type of customer use cases?

It tends to be quite varied, and it depends largely on the data. And what I mean by that is that, first of all, what I'd say is that it's always more consumption because the compute intensity of these algorithms that you're talking about tend to be greater than the compute intensity or just a traditional BM25 algorithm. Now having said that, depending upon the type of data, if it's just a single log -- just logstream and every data -- every log line is what you're turning into a vector, that is probably going to be less intense than if you have entire documents or libraries of documents where depending upon how you chunk up that document can have a very meaningful difference and impact on the accuracy of your results. So think about it this way. If you ask a question, depending upon how you've chunked or broken up the document, your answer might be in multiple chunks. You might need to do multipass or do something that allows you to actually look across multiple segments. So those kinds of complexities have an impact on how much compute you tend to see, and we've seen anything from 20% to 30% on the low end and 3 to 4x on the high end, but it's just -- it's a spectrum. And that's why it's really hard to give a number, your mileage will vary, but it always tends to be more intensive just from a compute standpoint.

That makes tons of sense. Let's actually talk about observability because it's actually the biggest piece of your ARR and we're seeing momentum there as well. I'd love to get a sense as of like the scope of Elastic ambition and observability. Does it remain focused on sort of the log analytics opportunity? Or do you see your observability strategy expanding into things like incident management, ITSM, on-call, you've seen a lot of the traditional observability players kind of expand into adjacent categories. Talk to me about the scope of Elastic's observability strategy.

Yes, our ambition is definitely to go beyond log analytics. The first thing I'd say is like log analytics is a big, big market in itself, right? Because the one thing you can be very sure of is the more applications that get built and we've been talking about AI and all the new AI applications that are going to get built and are being built. Every one of them as they go into production, as they mature are going to need observability. So log analytics is going to be a continuing growing market. We want to obviously be a leader in that space. But beyond that, whether it's tracing, whether it's metrics and infrastructure monitoring, our ambitions are beyond just logs. Having said that, like the way I think about it is there are some natural areas where I don't know if in the near term we would want to compete, right? And there, like ITSM, you talked about since you brought up ITSM, we have things like the ability to do remediation or the amenity to take actions, but we'll often integrate very nicely with the players that tend to be strong in this space. Whether it's a ServiceNow or a PagerDuty or what have you. We have deep integrations with them. Our customers tend to use this together, and that's how we prefer to go with this sort of a partner route but the core observability area where you are trying to look across different signal types to understand whether something is going wrong, predict if some things are going to go wrong based on the data and then suggest some remedial action. And if something goes wrong, then root cause it quickly. Like that's our core sweet spot, and we're going to keep improving and increasing the breadth of our portfolio.

Awesome a minute left to go. For investors that are newer to Elastic story or maybe they visited once upon a time, it's been a couple of years. Given where the business is standing today, you have a $1 billion-plus business, your margins are improving. Ash, what are you most excited about from here looking forward, like what gets you up in the morning that really drives your enthusiasm for the Elastic opportunity from an investment perspective?

Personally for me, it's everything to do with AI. And even the reason that I came to Elastic a little over 4 years ago, was because I had used Elasticsearch in my prior life. And what I found was that when it came to unstructured data and any kind of real-time analysis, I had not seen anything nearly as good as Elasticsearch, like miles ahead of everything else that I had seen. When you think about what generative AI is doing, it's fundamentally changing how we are able to use automation for decision-making, analysis and actions on unstructured information. Whether it's opening a new bank account, applying for a mortgage, like looking through case law and making a decision like everything. There are so many processes, irrespective of the business that you're in, that involve human beings, reading documents, reading basically unstructured information. Stuff that can't be put into a SQL database and have a very precise deterministic query. That's what large language models are allowing us to automate. We are excellent at that. This has been our core strength. Now how we ramp into that and how that grows? That's going to be -- if anybody has a crystal ball, that would be awesome. All of you are better at that than I am. But the long term, is pretty obvious to me. The long term is that this is, in my mind, a very big technology shift. I started my career in the early '90s, and I was -- I saw how the Internet progressed. I saw how cloud computing progressed. It always starts with the lowest layer in the stack in silicon and then compute build-out, and then it percolates up. I'm very excited by what this means to every application that we have today that I believe is going to be reworked in the coming years.

Exciting times. We're out of time. Thank you so much for giving us an update on the Elastic story. Thank you.

Thank you.