Tyler Radke at Citi Software Sector. Welcome to the afternoon track of a busy day two at the Citi Conference. We got the Elastic team here with us for a great discussion. Ashutosh Kulkarni to my left, the CEO, and the recently appointed CFO, Navam Welihinda. I'm sorry if I got it right. Okay. Good. Gentlemen, thanks for making the appearance. You know, for folks in the room that maybe are less familiar with Elastic, could you just give a quick overview on the company, and how kind of the evolution of AI has impacted the business? Sure. So, Elastic fundamentally as a company, was founded on an open source project, Elasticsearch, which was written by our co-founders. The best way to think about it is it's a search platform. It's a technology that's designed for bringing in any and all kinds of messy, unstructured information and then making all of it searchable. Through that, you can then analyze that data in all kinds of ways. Over the years, we started in that sort of basic search area, but then grew into observability, specifically starting with log analytics because logs tend to be extremely messy. They're voluminous. They're hard to analyze. Over time, we got into security as well and cybersecurity, specifically starting with SIEM, in the security event management and monitoring space. That became the cornerstone, but then we also expanded into other areas in security, specifically endpoint security and so on. As AI has become more and more prevalent in terms of how people are not only using AI for building conversational apps and so on, but also for building agentic workflows that are being used to automate more and more business processes, what is very obvious is these large language models, which truly are, in my opinion, like the operating system of the future. The way you program these large language models is using English, but these large language models only know what they've been trained on. All of that is what is publicly available out there. These large language models have no context about your private information. If you're using a large language model, if you're building these kinds of agents within your company, you need to somehow provide context to these large language models. That whole process of what's called context engineering or data retrieval for context engineering, that is how our product is being increasingly used today. Our vector database and everything else surrounding it, that is what gets used in building these agents, these kinds of applications. That has also benefited us in security and observability because increasingly for those processes, whether it's a SOC analyst or a site reliability engineer, we through our AI stack are able to make their jobs easier through automation of those systems as well. Great. That's a good overview. I guess double clicking on the AI opportunity, just for, again, for those unfamiliar, like, how do you, there's a lot of different software companies talking about AI. You know, you have application companies, some other infrastructure companies, like, how do you monetize AI today? How should investors kind of think about how AI impacts the numbers side of the equation? The biggest impact for us is on our search business because when we think about our search business, you know, that encapsulates anytime you're building any kind of custom application on top of our core platform. This data retrieval, this context engineering that I talked about, that is all effectively a search problem. You're trying to find just the right set of data within your overall environment that is relevant to answer that particular question. We are seeing an expansion of our search opportunity, and that's resulting in search effectively becoming the fastest growing part of our business. You know, when you think about search, observability, and security, these three areas, search has become the fastest growing part of our business, and it's because of the tailwinds of AI that we have seen. For observability and security, it's helping us compete better. We lead in, in the case of security, we have launched capabilities about a year ago. We unveiled something called Attack Discovery that instead of just showing you alerts in your SIEM, now looks at those alerts and is able to, using AI, identify the cybersecurity attacks, the attack patterns that are happening within your data. It's effectively doing a lot of the job that a SOC analyst does. It's helping automate a lot of that work. It's helping simplify and actually acts as an aid, an accelerant, for that SOC analyst. All of that is what our field teams tend to lead with because that's a big differentiator. That's something that we are able to do that others aren't able to replicate, and that's making our security business more and more competitive. It's making it, you know, easier for us to win. We're seeing that same thing also play out on the observability side, and that's why we feel that it's going to, you know, help us increasingly in all three parts of our business as we go ahead. Okay. Great. I think one of the unique parts of the Elastic story is, I mean, the product can kind of be interchangeably used across all those three use cases. Right. Meaning, you buy a subscription or consumption credits, and you can use it across a wide range of use cases. I'm curious though, how do you think about opportunities also to amplify the product's reach? Meaning, yes, this is a great developer platform, but there are other vendors both in the search space, whether it's Glean on the AI side. Obviously, you have a number of observability and cybersecurity vendors that offer something a little more packaged out of the box. Clearly, there's been a lot of rise of vibe coding startups that make developing software easier. Is there an opportunity to make this stuff easier to use and ultimately drive more consumption, or just how do you think about that high level? If I just step back and I look at the AI opportunity, what I'm seeing is more and more applications being built that take advantage of all of the unstructured data that we have sitting within our organizations and automating business processes that depend on that unstructured data. That's really what's happening because we've always had the ability to write applications that worked on structured data in deterministic ways. CRM systems, or, you know, ERP systems, they all depended on very structured data, and they had very well-defined processes that you could automate on top of that structured data. With LLMs, the big opportunity is to do that same kind of automation on unstructured data because that unstructured data, you know, whether it's employee onboarding or it is customer support, there is a lot of stuff that you need to figure out on the fly. You need to reason on information that is not all structured. You're trying to respond to a customer that might have a particular problem. You need to look at so many different things. You need to look at what is the specific issue that they're facing. What do you know about the product and where you might have issues that might be known issues that exist in your product. You might want to look at other customer questions that might have come in to see is there anything that we can learn from there. All of these are unstructured sets of information. You need to be able to automate based on all of that. That's where LLMs are really, really good. Now you need to provide this context to those LLMs. The opportunity that I see for Elastic is that as more and more business processes get built to automate, you know, using AI, I see the opportunity for Elastic to be embedded as the vector database, as the platform for doing that context engineering, and as many of those AI applications as possible. That is the real opportunity for us, which is why we are so keen and so excited about what AI represents for Elastic. Okay. Great. As you think about that AI opportunity, I think clearly it's going to be a huge market or early days, but there seems to be the opportunity both with AI natives. Right? We're seeing a lot of these, obviously, you got the model providers, the coding platforms, you know, code generation startups, and then you have the enterprise building their own custom AI. How are you positioned in each of those segments? We look at both motions as being equally relevant to us. Right? At the end of the day, whether it's a software vendor that's building the next vibe coding platform, or the next ERP solution or the next HRMS solution that's evolving out there, each of them is looking for some way to build an AI-native application that's going to make that application more and more interesting, more and more exciting for users to use. We see that as the ISV play. Right? We want to be embedded in as many of those applications as possible. We've publicly talked about existing customers, whether it's a DocuSign, whether it's a Seismic. There are various examples that we have given of customers that have already done that with us. These are companies that have, and we also have DevOps companies that have used us as part of their agents within their coding platforms. We have lots of those examples. At the same time, we have enterprise customers that are using us for building applications, whether those are agents or just conversational chat style applications, or implementing semantic search or what have you, in their environment where they're pulling data from different systems. It's a custom-built application that they're creating. It's for just their use case. It is not something that they're selling to customers. That's perfectly fine too. We've seen banks, we've seen telcos, we've seen e-commerce companies. Lots of use cases. Fundamentally, Tyler, the way I look at it is, AI represents a very different way to build applications in the future. Today, if you ask any major organization, if I just look at your company, your bank, Citi, you probably have dozens of agents that you are building or have built within your organization, and you probably, over time, will build hundreds, if not thousands, of these agents and applications. We're still in the early days. For us, the trick is getting into as many of these as possible right in the early stage so we are part of that core fabric. We are part of that core infrastructure layer as they're building these applications. As you build more and more applications, we just grow with you. I think that's the real opportunity here. Yeah. I think we're still a ways away from using agents at a large bank like this. I'm sure somebody within your company is because we already work with various other banks. So I'm sure, For sure. Maybe there'll be an agent up here asking you questions next year. You better watch out. Navam, I thought we'd bring you into this. As long as they use Elastic for data retrieval, I'm good. Okay. Yeah. Navam, you know, I thought we'd bring you into the discussion here just, you know, fresh off results. I think, I don't know, maybe your first full quarter as CFO. You know, pretty strong beat across the board, across both cloud and self-managed. You know, I know there was a lot of questions around the price impacts in the quarter as well. Maybe just frame for us kind of the key puts and takes on the quarter from your perspective now that we're almost a week past when you released. Yeah. It's been a great first quarter, let me tell you that much. From what you said, that's absolutely true. Q1 was a strong quarter across the board, and we beat the top line by $18 million, and we had a strong bottom line performance as well. The way I think about the health of the business is around two aspects, which is how are commitments going and how's consumption going, right? Both of those in Q1 were very strong. Commitments, if you think about what happened there, year -over -year, we saw growth. The growth was across all geos. It was balanced. No outliers. That's great news. On consumption, it was the same thing. Year -over -year growth, no outliers and strong commitments across the board in Q4. Overall, from a Q4 perspective, I thought it was a great quarter, and we were very happy with what we delivered. The other thing is when you think about the underlying consumption level that we saw from Q4 to Q1, that increase was very strong. We're happy with that consumption increase that we saw in Q4 to Q1 as well. Moving on to your price comment, I think it's worthwhile parsing back a little bit and understand most software companies do price changes, and we're no different from any of those other software companies. This isn't the first one we've done. We did a price change last year on the self-managed side. We did a cloud and self-managed price change a couple of years before that, and we just did one in May. This is more of a history of us periodically looking at prices and making changes just like most other software companies do. It's more of a matter of course of business for us, right? Mhmm. The reason we are okay with changing prices and the reason we feel good about changing prices is because of the way we introduce functionality into our product. Our customers don't get separate SKUs for new things that we deliver. We sell a platform, and all the new product introductions go into that platform. Over time, what we're delivering is more value for money in that platform, which we capture periodically through price increases. That's an important point to remember. This is no different from any other software company practice, and it's no different from the history of what we've been doing in the past. The second point I want to make is that when you think about price changes, what matters is how does consumption change with the changes in price. When you think about consumption, there are multiple puts and takes you need to understand. In any given quarter, people are optimizing their usage. There's data coming in, which increases their consumption. They're also optimizing their usage, which puts downward pressure on consumption. That's happening in any given quarter. The second thing is we introduce feature functionality that is meant to drive more efficiency in our customers. Two specific examples of some things we've done in the past are Searchable Snapshots and Logsdb. If you adopted those two features, you're naturally going to be more efficient and consume less. That puts downward pressure on consumption. Then there's pricing, which adds upward pressure on consumption, and also people react to price. There's elasticity, and people change consumption. What matters to us in a consumption business is very different from a seat-based model, which is a simple P times Q math. What matters is how does consumption change in relation to all these changes that I've talked about earlier, and is the net consumption growing. That's the biggest thing we care about. Mhmm. What we saw in Q1, right? That's the main point I wanna make about pricing. You can't really, in consumption models, isolate one individual variable across the many variables that I talked about and then do simple math and say, absent this, it was X, Y, Z. That's the big misconception that I think we need to clear about as we consume how to think about pricing and consumption models. Right. And some of those efficiency capabilities that you highlighted, you know, the Elastic Searchable Snapshots and some of the logging functionality. Was there any, like, significant additions to the portfolio in Q1, or was this kind of more of a comment on, like. No, no. Logsdb was introduced in Q4. Q4. Yeah, yeah. This was, but again, like. In conjunction with the. You know, we don't think about it necessarily in conjunction. Tyler, the best way to think about it is we introduced those kinds of features. You know, Searchable Snapshots was about five, six years ago. Another thing that we did some time ago was supporting some of the newer chipsets, Arm-based chipsets, Graviton chipsets from AWS. That made the system more efficient, so it brought down consumption. We introduced better compression, and we have done that in the past. That brings down consumption. The whole goal is to keep making the platform better and better. Competitively, this looks like the absolute best choice for customers, and they keep bringing more and more workloads to us. Mhmm. What we see is when we do that, for any given workload, we might see a near-term pressure, but very quickly in the mid to long term, people bring more workloads, and we see growth. Searchable snapshots, when we introduced it six years ago, it immediately put pressure on existing workloads. As people started retaining data longer, they started bringing other workloads. We started to see that was one of the features that drove more and more people to use us as compared to Splunk. Mhmm. Even in those days, that pays off very nicely in the long run, right? That's why we do these things. To Navam's point, trying to disaggregate any one factor is just meaningless. Okay. Very, very clear. You feel very good about the consumption growth in Q1, and I assume based on the raise of the year, you're expecting that. Yeah. We expect because of what we are seeing both in commitments and in consumption, we feel really good about the strength of the business in the year. Great. No, super helpful. Speaking of the changes between Q4 and Q1, I think you sounded a bit more upbeat on the macro environment. Maybe upbeat's a little strong of a word, but obviously, the federal weakness that you saw in Q4 was pretty widespread across the software space. Just remind us, what did you see get better in Q1 versus Q4, both in federal and across other industries? Let me tell you about federal, and then, you know, Navam can also add to it. In federal, I spend a lot of time with customers. I'm generally out on the road a fair bit. Next week, I'm going to be at the Billington Cybersecurity Conference in D.C. On the federal side, the way I describe it is, in Q1, it felt like the new administration has sort of settled in. Right? Clearly, they have a greater focus on efficiency than we have seen from prior administrations. There is the fact that they're settled basically means that we are not seeing a lot of personnel changes on an ongoing basis. Things are just more settled. People are making decisions. Even in that new normal, even though there might be a greater consideration and focus on efficiency, that's a world that's more stable, and we know how to operate in that world. Right? It's a world where we know the strength of our product is going to continue to hold very, very well because, Tyler, as you know, when you look at value for price, we've always had an outstanding value proposition. From a competitive standpoint, we feel really good. Now that people are making decisions, it is an environment that we feel really good about operating in. From that perspective, like we said, the public sector environment, especially in the U.S., felt stable. Mhmm. Right? It felt good, compared to the uncertainty that we were experiencing, you know, 90-ish days ago. Right. I don't know. No. I would echo that. Going into the year, we had detailed out the headwinds we were or the pressure we were seeing and then assumptions on what could happen beyond the U.S. public sector. Civilian agencies, to Ash's point, throughout the quarter, we saw that the U.S. public sector was much more stable. We know how to operate, and the teams are primed to operate in that environment now. Our products resonate in the administrations focused on doing more with less. Right? We have a good footing in this new normal. That's number one. Clearly, we did not see the scenario play out where things spread across other geos and unrelated sectors to create more headwinds. It was a much more stable environment than what we had feared or guided to in Q1 or in Q4. The result is, we had a great quarter. We feel good about the year, and we reflected that by raising the guide, more than what we had beat. The bottom end of the guide, we beat by 18. The bottom end of the guide got increased by $24 million and the midpoint by $22 million. That's to signify that we feel much better about how the year is going, and every quarter, we're going to execute and revisit that number again. Right. You would describe the guidance as is still pretty prudent in terms of macro assumptions and everything. That's right. I think I'd always aim to give a prudent guide, and this one is no different. Like I said, we feel good about the year, which is why we raised beyond what we delivered. Every quarter, it's going to be we execute and go, give you a new view of the year. Got it. Ash, I'd love to just ask you about the competitive landscape. Sure. Obviously, there's a lot of different areas that the Elastic product touches, everything from cybersecurity to observability and then search and AI with vector search. There's certainly been a lot of consolidation pressures, if you will, in both the security and observability market, whether it's budget constraints or, you know, open telemetry. How are you seeing that impact the Elastic business, you know, either positively or negatively? In terms of where we see our competitive dynamics play in our favor, in security, we've always talked about security as being a data problem. Anytime you have a situation where customers appreciate the need to bring in all the data and retain it for long periods of time because they understand that the cyber landscape is pretty complex, that plays to our strengths. Anytime they feel that it's in their benefit to use AI to drive more automation, that plays to our strengths. Lastly, when you look at security, if the customer has a desire to run that security workload on prem in their own data centers, we are really one of only a couple of choices at scale that works because many security products are only available in the cloud. One of the reasons why you will continue to see us do very well in our self-managed business is for that reason. There are tremendous advantages that we have, not only as this data-oriented security player, but also because of our AI functionality and our ability to have a really strong offering in a self-managed mode. When it comes to observability, it is a large growing market, but it's also a very busy market. Mhmm. Our core strength is when the data is messy. That's why we lead with logs, because when it comes to logs, especially application logs, the messier the information, the harder it is to query, the more you need a product like ours, a platform like ours that's inherently designed for unstructured, messy data. If the data is structured, if you're dealing with metrics and so on, there are other players that, you know, naturally have been in the market for longer, and we see that competition is there. When it comes to logs, we have a true differentiation. The work that we're doing in terms of both driving more efficiency into the platform, but also using AI more effectively, is a continued strength for us. On the search side, you saw this movement where there were a lot of attempts at trying to define vector databases as a separate market. I think it is becoming more and more clear to people that vector databases are a feature. Mhmm. As opposed to a category in and of themselves, for us, our focus is always our center of gravity is always going to be on unstructured data. You can absolutely put structured data into Elastic, but what we do uniquely well is when it comes to unstructured data, the ability to query that, to be able to search and do all kinds of analytics against it. We are very, very good at that. What I expect is going to happen, and we're starting to see that, is you're going to start to see centers of gravity continue to strengthen on platforms for different types of data. If you are structured, you're going to end up with certain kinds of platforms that are really good at analytics. If you are unstructured, you're going to tend to see companies like us. I would expect our competition to be primarily the hyperscalers in that area. Okay. We know how to compete very well with them. We've been doing it our entire existence. Right. Right. Right. I guess just as you think about sort of how to drive the durable growth of Elastic and, you know, this goal to be the embedded kind of platform within these emerging AI applications. Obviously, top of funnel, developer affinity, those are important things to the next generation. You did make the return to open source AGPL. Yeah. I think over a year ago, if I'm not mistaken. Give us an update on how that's gone. What are some of the metrics you also look at to measure this, top of funnel, momentum? Yeah. The reason why we, you know, adopted the AGPL open-source license, before that, we had the SSPL license that we supported and the Elastic V2 license. Elastic V2 is very, very permissive, but neither SSPL nor Elastic license were OSI compliant. AGPL is an OSI compliance, so you get the official stamp of being open source. The reason why that was important for us is in the community of open source developers, as people are looking at open source alternatives, you know, they basically look at what's OSI compliant. Where this matters most is in the area of vector databases. That's the evolving space. Right? That's the area where we are still very, very early in the overall AI market. Given how early we are, that's the land grab right now. It matters to us how we are, where we are seen, how people find our technology, download it, use it. To me, it's less relevant whether they start by being a paid customer to begin with because these developers are going to just play around with open source technology. Over time, as what they're building becomes more meaningful, they're going to look to a commercial vendor, and they're going to stay on that platform. What we look at in terms of metrics, we look at how we show up in various open source forums. We look at the number of downloads of our product. Those are things that we track. We obviously look at the number of trials that are happening on our products. There are various metrics. The AGPL open-source license is something that we cared about because we wanted to have that top of funnel activity. Everything that we are seeing gives us a sense that it's really been the right decision. Okay. Great. And then, Navam, on your end, we have an Analyst Day coming up in a little over a month. Obviously, you're still putting together the plans in terms of what you're going to share. How are you just thinking about targets both on growth and in profitability? I know it's something the company has guided to in the past. You may have a different philosophy, but how should we be thinking about how you think about long term about the business, obviously, without giving away too much ahead of Investor Day? Yeah, I don't wanna give the punchline that you show up, Tyler. Yeah. I encourage you all to attend Analyst Day. It's going to happen in about a month. It's happening in conjunction with one of our user conferences, Elasticon. We have a few of them. This one is in New York. Analyst Day will be in parallel with that. If you do attend, you'll get to see the keynotes as well as some of the demo booths and our users in full swing. That's a worthwhile experience. On the Analyst Day side, which is parallel to ElasticON, you get to meet our team and talk about things in a broader context. On the product side, you'd see Ken and our GMs, one of whom is with us here, Steve, talk more about the broader vision on product along with Ash. On the GTM side, Mark will talk about all the changes he's done to GTM and get some information on that as well. In finance, one of the sections we're going to have is the medium term model for the business and how we balance growth and profitability and what you should be thinking about in terms of our growth algorithm. I don't want to give it away because then you won't show up. Mhmm. That would be what I'd expect to see in Analyst Day. Okay. Great. I guess just go to market. I know we only have a couple of minutes left, but, you know, a little over a year ago, you did make some changes there that, you know, proved to be more disruptive in Q1 a year ago, but seemed like you kinda bounced back from there. Just remind us, like, what were those changes, and have you started to kinda see the returns on some of those investments or changes that you made? Yeah. For the last four quarters, if you look at our sales-led subscription revenue, you just have to go and see Navam's script. We have shown consistent and very strong execution, and that was possible because of those changes that we made. The changes that we made five quarters ago did two things. One is they created greater focus on enterprise and mid-market accounts where fewer accounts per rep meant that our reps were able to go deeper and broader into those accounts. As you can imagine, that leads to greater focus. That results in higher quality deals, larger deals. We've seen the benefit of that in the last four quarters. In the greenfield territories that we created, we created more of a dedicated hunter motion, and that has also been paying off. If you look at the number of net new million dollar deals that we've been adding, the $100,000 deals, we are seeing the benefit in all of those metrics. The best metric to me is sales-led subscription revenue. It doesn't matter if it's self-managed or cloud, but that is what we focus on. That is what we drive. You just need to look at the data to see that it's been really strong execution throughout these last four quarters. We're excited. Great. Let's wrap it up there. I think we're out of time. Thank you both for the discussion, and look forward to seeing you at the investor day in about a month. Thank you, Tyler. Thank you very much, folks. Thank you. Thank you.