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

Global Vice President of Finance, Eric Prengel.

All right. Hello, everybody. Welcome to Financial Analyst Day. Now before we begin, I want to get the obligatory disclaimer language out of the way. Today's event will be webcast and recorded for future playback. Information and risks pertaining to forward-looking statements as well as a reconciliation to our GAAP and non-GAAP results, are available in today's presentation materials, which will be posted on the Investor website at ir.elastic.co at the conclusion of the event. With that out of the way, on to the fun stuff. So for those of you who don't know me, my name is Eric Prengel, and I'm the Global Vice President of Elastic as well as the Head of Investor Relations. I was an investment banker for a long time before joining the company almost 3 years ago. And I've known Elastic for a while because I worked on the IPO, and some of you worked on it with me. It was a lot of fun. Since joining, I've gotten to know the company a lot better. And I'm really looking forward to sharing with all of you what the team has built and all of the exciting things that we have and the trajectory that we're on. It's great to have so many familiar faces together. And all in the place I grew up no less. Ash mentioned the Yankees because I went to the game last night. I was sad to see the loss, but there will be next year. We have a great program for you today. Ash is going to lead off and go through the opportunity. Ken, Steve and Santosh are going to talk about product. Then we're going to have Mark talk about go-to-market, and then Navam is going to talk through the financials of the business. Unfortunately, Shay couldn't be here with us today due to a family health issue that he needed to attend to. He's regularly at ElasticON events. And actually, if any of you are able to make it out to Amsterdam on October 30, he will definitely be there. And with that, I'm very excited to hand it off to our CEO, Ash Kulkarni.

All right. Good afternoon, everybody. Thank you for joining us today. So I'm going to kick things off. My job today is to set the stage, talk about our strategy, our vision, who we are as a company. For those of you who might not be that familiar with Elastic, I want to make sure that you have a firm understanding of where we differentiate, the role that we play in the IT organizations of all of our customers, the opportunity that we have in AI and how we are helping customers in AI today and what that means for our future. And the rest of the agenda is going to be folks in products, walking through the new product capabilities. We had 6 new product announcements today. We are going to have Mark talk about our go-to-market efforts and everything that we have done there. And then finally Navam bringing it home. So with that, let's get started. The most important thing that you need to understand about Elastic is the role that we play in helping our customers deal with unstructured data. We are the world's most popular data platform when it comes to unstructured data. And oftentimes, when people say unstructured data, it's hard to know exactly what you mean by that. Take a look at a log file. A log file is effectively every developer putting notes for them in the future to be able to go back and debug their own code. Typically, these messages tend to be very, very free-form. They tend to be very, very messy. The information in there is different from line to line. When you look at a log file like this, you can't put it into a regular database. It doesn't matter if it's a SQL database, doesn't matter if it's a document database, doesn't matter if it's a columnar store. You can't put this information into a rigid schema. By definition, if you're looking for patterns in it, if you're trying to identify and sift through it, analyze it, you need a different paradigm. You need a search platform. And that same thing applies even when you're dealing with freeform documents, Word documents, PDFs, all of these kinds of structures. They do not have a good schema. That's unstructured data. Because of our dominance in this area today, over the years, we've had over 5.5 billion downloads of our software. That's over 3 downloads a second over these 15 years, if you average it out. We have been ranked as the #1 search engine in vector database according to DB Engines. And if you look at the GitHub stars, it clearly indicates the popularity that we have. And all of this is because, yes, we can deal with structured data, but most importantly, what we can do with unstructured data with the power of relevance, that is something that is very specifically, our greatest competitive advantage. And because of this, we have built a tremendous incumbency when it comes to this kind of information. So we have estimated just the data that is in Elastic Cloud that we have a lot of clear access to and visibility into, but also our estimates of the data that exists in paid Elasticsearch self-managed clusters around the globe. Every day, over 30 petabytes of new data gets ingested into Elastic paid clusters around the globe. 30 billion queries per day just on Elastic Cloud. And when we look at the total data that's under storage, it's well over 1.3 exabytes. That is incumbency. This is unstructured data. And when that unstructured data gets utilized for AI, gets utilized for observability, for security, where do you expect people would go? The first place that they go is Elasticsearch. This data is already there. So when they look to automate things using new modern AI techniques, we are the natural platform of choice. This incumbency is a huge advantage. And as unstructured data has grown, so is our revenue. We have built a strong at-scale business as unstructured data, which is the fastest-growing type of data has continued to expand. The most exciting thing that's happened is the fact that with the advent of LLMs, the importance of unstructured data has just grown manifold. You look at any application in the past, whether it was CRM systems, ERP applications, HCM systems, they were all built on structured information, account ID, opportunity ID, customer name, et cetera. All the notes that you put into your CRM system are just shoved in there, it's freeform text that you can't really analyze in any way. It's just an attachment that some human being has to read. Large language models have completely changed what you can do with unstructured data. Large language models are really the new operating system. We believe it, we believe it firmly. And you program not using Java or C or Rust or Python, but you program using English. That's the amazing part about these systems. And these systems are knowledge systems, but they are only as knowledgeable as the data they've been trained on. So to use them within the enterprise, you have to provide them with context. You have to provide them with relevant data to be able to address the problem that they're trying to address. So AI fundamentally depends on data, being able to have access to it. And relevance is key to making any AI system actually worthy, production grade. This is right in our wheelhouse. AI has literally come to us. It has made unstructured data, more interesting, more valuable. Our ability, this is what we've always been known for. This is what we were created for as a company. Our ability to be able to ingest, bring in all of this unstructured information, index it, make it searchable, allow you to run all kinds of interesting algorithms, ML queries on top of this data. This is what we have always been good at. And this is what is really needed to build new AI experiences. We've been working on this ever since the company was formed. Relevance is not a new concept for us. From the earliest days, it was all about relevance. It was all about trying to figure out how to make sure that you can surface the most relevant information for the search query that you were firing at Elasticsearch and surface that. And over the years, we continued investing in machine learning, in AI. When it was clear that transformer models were going to be an interesting thing, we started investing in building out our vector database well over 5 years ago. Since then, we have made our vector database more and more capable, highly scalable. Today, we have customers that are using it to store and retrieve billions of vectors at scale in a very high-performance way with great efficiency. We built our own embedding and retriever models. We built our own reranking models. We added additional capabilities like MCP tools. We are working on GPU-based acceleration with NVIDIA. There's a lot that we have been doing in this area. This is not something that we just woke up and decided to do a year ago. This has been in the making because unstructured data has been at the core of what we've always done. So today, we feel confident that we have the best platform for context engineering. Now what is context engineering? If you ask ChatGPT, which is what you should do, it will tell you that context engineering is the techniques involved in ensuring that you provide the right data and the right tools to a large language model to allow it to do its job accurately. The right data and the right tools. That takes more than just a vector database. Of course, you need a vector database when you're dealing with data that might not easily be searched through textual techniques. Also if you want to do things like semantic search, but you need more than just a vector database. You need to be able to deliver hybrid search. You need to be able to ensure that you can actually verify the outputs. So have a playground. You need LLM observability. You need embedding models that you're constantly tuning and improving relevance with. All of this becomes critical. So why do we win? We win today because of these 3 broad reasons. First and foremost, like I said, we have been investing a lot in making our vector database and our overall retrieval platform the absolute best when it comes to speed, scale and efficiency. And I'll talk a little bit more about that. But more importantly, the team is going to go into it and actually go into the details. In the past, we've talked about things like better binary quantization, BBQ, allows you to manage vectors in a much denser way. So you're having to use much lesser memory and CPU. We've talked about capabilities like ACORN, a new filtering algorithm that allows us to improve query performance because any search always happens with some amount of filtering. You don't search for restaurants, you search for restaurants in a particular neighborhood. In a particular neighborhood is a filter. You need to be able to do that efficiently. That's what we do incredibly well. The second reason why we win is relevance. We have put a lot of effort and energy into optimizing our models for relevance. And we don't just do this using vector search, but with the rerankers that we've built, the ability to use multiple techniques, hybrid search, semantic search. And then on top of it, to use reranking to get the best possible output, the most relevant data. And lastly, because we have assembled all of the tooling that you need to be able to build these chatbots, these agentic workflows, these agents in an efficient manner. Today morning, we made two announcements in this area. The first is a new capability called Agent Builder. And the team is going to demonstrate this. If data is the most important aspect for context, wouldn't you want to start with the data as you're building these agents? How do you start directly on top of your data with almost a conversational experience, explore your data and assemble the right tools that you need to quickly build a complete agent with workflow capabilities and everything that is needed? But a completely different approach that's truly relevance-centric, that's all about context engineering. The second thing that we announced is the Elastic Inference Service. This is our own GPU accelerated service in Elastic Cloud, where we make it possible for our users to get access to our embedding models, the retriever models, our reranking models. And over time, more and more models that we'll deliver so you have everything that you need, not just for Agent Builder, but even outside of it, through this easy API. We also today morning, announced our acquisition of Jina AI. We have been partnering with Jina for a long time. Our ELSER model is a world-class Sparse EncodeR model, but it was English only. With Jina, we get access to an amazing multilingual and most importantly, multimodal set of models, both for retrieval as well as reranking. When you look at any document, most documents will often have multiple types of information in it, some text, but also images. If you are dealing with a single-mode model, you would need to break up that information, separate the text from the images, run it through two separate models to chunk it up differently, just makes the whole system extremely complicated, and you don't get the relevance, the accuracy that you need. With a multimodal approach like what Jina has been able to do, you can put all of that information through a single model and get exactly what you need. This is going to be available through our inference service and will be available to our customers as we integrate this. This team, the team of researchers, the work that they have done, they have made it -- they do a lot of work with academia. They have published a lot of reports about the relevance quality that they're able to generate. We are very excited about what this brings to Elastic. This is allowing us to win the kinds of customers and have the kinds of customer successes that we are incredibly proud of. And I wanted to just talk about a few of these examples. So the first is DocuSign. The reason they chose Elastic was because they were trying to build what they call their Intelligent Agreement Management platform, a new service that they're delivering to their customers. They want to go beyond just being able to sign documents. For that to work, they needed to have some ability to be able to search across each and every document, literally many, many billions of documents that are in the DocuSign store. How do you make that possible across the different modes of data, like we talked about? At scale, with immense relevance. We were the only ones in their testing that they found capable of doing it. Legora, an AI native company. It's all about how do you use AI to improve the process by which lawyers are able to do research on case law, write drafts, optimize those drafts, do their work better and faster. They chose us because of the quality of relevance. When you're dealing with legal case law, the kinds of semantics involved are very specific. You need to understand those semantics. They found that the relevance quality that we were able to deliver was amazing. Another example I'll touch upon is the National Health Service in the U.K. They use Elastic as the platform for bringing in all of their patient records and being able to search across them for helping doctors decide what's the best next step in terms of the procedures that they want to recommend, helping their doctors work faster, more efficiently. Now they chose Elastic not just because of our scale, not just because of our relevance, but also because we were the only platform that had the very fine grained document-level permissions that gave them the confidence that it would not violate patient privacy rights. We are the only platform that has the ability to do all of this. What's interesting when I look at this slide is, first, the variety of customers. We are not looking at just customers that are AI native companies. Looking at ISVs like DocuSign and Seismic, looking at agencies like the National Health Service, that breadth is what gives us tremendous strength. This is diversified. The second thing that I'll call out is these use cases are very durable use cases. This is not experimentation. The National Health Service is not trying to experiment with people's health. Legora, their whole business model is built on this. DocuSign, the entire new business that they created is dependent on this. These aren't experiments. These are durable use cases. And the last thing I'll call out is just the fact that they had very clear understanding of the differentiation and the competitive advantage that Elasticsearch has that made us the only right choice for their needs, scale, relevance and the ability to do everything in one single platform. You look at the numbers of customers that we have today. I look at that middle box. That represents about 20% of our customers in the cohort that is paying us over $100,000 a year. That's 20%. But that tells me 2 things. One, that there is a tremendous opportunity still ahead of us because we still have 80% of that population to go after. And second, that each of these companies, even the companies listed here, they have built one really amazing application on our platform that's AI-centric. But that's just the first. There's so much more that they are planning to do, intending to do, and we are in such a great place as part of their infrastructure for AI that we feel very excited about what this means for the future. Now I'm going to shift gears a little bit. Everything that I've talked about so far has been about search. But what does this mean to our observability business? Well, first, why did we even get into observability as a company? I'll take you back to what I started with. We are the best data store for unstructured, messy data. Observability data tends to be incredibly messy. Logs are the messiest form of machine-generated information. Our ability to get into this entire space started with the functionality that we delivered in log analytics. But over the years, we've assembled a complete platform, everything from infrastructure monitoring, APM, AIOps, real user monitoring and more. And the reason why we win today are these three. The first thing is very simply, what you see at the bottom. We have the best data store, and we continue to invest in this. We have the best data store capable of ingesting every possible type of signal needed for observability in one single store, allowing you to run complex correlations, allowing you to see the links between the infrastructure, showing you that the CPU is running hot, to understanding the specific services and application components that might be falling behind to then getting to the relevant logs, the specific logs related to those issues, to then be able to diagnose the root cause. Having one data store that's optimized, and we have built distinct back-end specialized stores within Elasticsearch for logs, for time series data like metrics and so on. So we can store each of these data types efficiently and still get you correlations across all of it using a singular query language, ESQL. The second reason why we win is because of our big bet on open standards. Observability is a mess, generally because of the fact that in the past, none of the data was ever normalized. It was really hard to do any kind of correlation. OpenTelemetry has started to change that in a very material way, in a very material way. And so we leaned in hard because with the open standards that OpenTelemetry provides, we can now have an OTel native way all the way from collecting data with OTel collectors and OTel SDKs, all the way to a OTel native back-end schema. Incidentally, we donated our Elastic common schema to the OpenTelemetry project, and their common schema for logs is based directly off of Elastic's common schema. What that means for our customers is when they use OTel to bring in the data, the dashboards automatically light up. The data is naturally correlated with each other. That is a huge advantage. And lastly, because we have been more aggressively using AI to help with investigations when you're dealing with any kind of issue in observability than anybody else. When you're dealing with observability, the thing that matters most is mean time to detection, mean time to resolution. How quickly can you spot the problem? How quickly can you understand the root cause? How quickly can you do something about it? And towards that end, the fourth big announcement that we made earlier today was something called streams and significant events, and the team will demonstrate this. Using AI to automatically help you get all the richness and all the information that exists in logs because logs have always been the last port of call. That's where every developer goes at the end when they want to root cause exactly what line in their code is causing issues. But it has never been the first port of call because logs are hard to work with. They're required to do a lot of work. You have to write a lot of Grok rules. You have to parse the data, you bring it in, you got to write the right alerts. What if AI could do all of that for you? That is what Streams and Significant Events does. It automatically uncovers what's important in your log data. And our customers are showing with their faith in us that our approach is the right approach. So we've talked a lot in the past about our land and expand strategy. And this just gives you a sense of how much progress we've made even within the subcomponents of observability. We always start with log analytics. Over 90% of our Elastic Cloud Observability customers use us for log analytics. But what most people might not realize is over 35% of our Cloud Observability customers use us for what I describe as beyond logs, APM, infrastructure monitoring or metrics, AIOps and so on. This shows that our land and expand motion is working. It's proven. But there's also a lot more room for us to keep growing. This is what's exciting for us. That same approach also applies to security. Because if observability is a data problem, security is 10x more. You literally miss every single threat in the data that you don't ingest, analyze and alert off of. Security is absolutely a data problem. And that's why we started with SIEM because we have the world's best platform for unstructured data. And all of that security telemetry is unstructured. It's network logs, it's application logs, it's identity and access management logs. It's web logs. It's telemetry from endpoints. It is in so many different shapes and sizes, being able to bring it all together and actually do analytics on it is a data problem. Yes, we've invested a lot in first-party threat research and so on. But make no mistake, security is fundamentally a data problem. And by starting with SIEM, we then, over time, have expanded beyond adding EDR and XDR functionality, adding cloud detection and response functionality, adding UEBA or entity analytics. The reason why we win, and you'll see the themes here. First and foremost, at the bottom, the best TAM data store when it comes to being able to bring in all of this network telemetry at speed, at scale, very flexibly, irrespective of your deployment type in the cloud, on-prem, do threat hunting across all of your data. The second reason, we have truly used AI more aggressively in security than anyone else. Attack Discovery, the functionality that we released 1.5 years ago, which is now being used very widely across our entire security portfolio by our customers, takes away the job of an analyst to try and sift through all of the alerts and figure out what are the real attack patterns in there. The AI does it for you. And the third reason, the ability to not just unify all of the signals, but then to act on it, to remediate, and I'm going to touch upon that in a second. Because the fifth big announcement -- product announcement that we made earlier today was this. We announced Elastic Workflows. This was the acquisition of Keep that we made about 6 or 7 months ago, and we have very quickly integrated that functionality directly into the platform. So now not only do you get security alerts, not only can you do threat hunting in the platform, but you can create the remediation workflows. And depending upon how automated or semi-automated a way you want to actually do those remediations in, you can fire off those remediations, complete case management, complete workflow, stateful everything, incredibly powerful. And our customers are proving that we are the right choice. So again, over 95% of our Elastic Cloud Security customers use us for SIEM. This is where we land. Just like in observability, we land with log analytics. In security, we land with SIEM. What most people might not realize is that over 20% of these Elastic Cloud Security customers are using us for what I call beyond SIEM. They are using us for EDR and XDR use cases. We don't lead with EDR, but we expand with EDR when we are in, because those endpoints that are bringing in the data required for SIEM have the functionality built in for things like ransomware protection, for things like host isolation. So once that agent is deployed, all the customer needs to do is turn on the configuration flag, and that's how we expand. That's how we grow our consumption. So I've talked about land and expand over and over again. So I'll try and put it in a visual form for all of you. What's great about Elastic is given that we have had such strong roots in open source, it is really hard for me to meet a prospect, somebody who's never done business with us, where there isn't somebody who is already familiar with Elasticsearch is actively using Elasticsearch. Maybe the community editing, the free edition, that's fine. But they already know us. They are already champions in there. Awareness starts through our open source routes. And then when we land, when we have the first transaction with a customer, it happens in 1 of 2 ways. Either the self-service motion, if it's an SMB customer, they just come to monthly cloud and they start using us that way. It's typical for our SMB cohort. And our sales-led efforts where we go after the enterprise and mid-market accounts. After we have that first land, then we focus on customer usage. And Mark will touch upon this. But we have a customer architect team that focuses just on this. There are engineers who work with our customers to make them successful with that implementation. When that implementation is successful, consumption starts to fly, the flywheel spins because as they do that, the next natural thing then with the customer is a conversation about how else can we help you. Just as I talked about beyond logs, beyond SIEM, and then going from one solution to an additional solution, that's how we grow. Customers adopt higher tiers if they want our AI Assistant, if they want searchable snapshots, these capabilities that we put in the premium enterprise tier, and on and on. Another thing that's important about Elastic is the fact that we have been very disciplined about making sure that we meet our customer where they want us to be. We have, of course, Elastic Cloud hosted and Elastic Cloud serverless, but also the self-managed offering. And that gives us tremendous advantages. There are lots of customers even today who want to run AI workloads in their own data centers because the data is regulated information that they don't want to put in cloud. That gives us an asymmetric advantage. We are one of the few that can do this. All of this taken together is setting us up -- has set us up in such a great way in a market that has a massive total addressable market. We are very excited about this. And we can see the recognition that we've been getting and earning from analysts. In the most recent reports, we are now a leader in each of the areas that we play in according to Gartner and Forrester. We didn't have this 5, 6 years ago. But we still have so much headroom ahead of us. That box on the bottom right, I always think about that. We have over 50% of the Fortune 500 companies as customers. That means there's 50% more that we can go and get. And that is exactly what Mark and his team are focused on from a go-to-market motion. And the work that they have done in the last 18 months, transforming our execution, making it more predictable, making it more consistent is something that I'm very, very excited about. What I'm most excited about is the fact that as we've been able to improve our efficiency, improve our productivity, we are very confident that even as this engine is humming, there is still more room for optimization. I know that we can continue to do even better. That's exciting. So I'm going to bring it home. We are not chasing AI hype AI is really a wave that has come to us because of the role that we've always played with unstructured data, that's what our customers know us for. And that unstructured data has now become incredibly exciting and important. It is what's fueling a lot of the work that's going on in AI. And that just means that we have a natural seat at the table. That just means that we have a natural advantage, an incumbency that we are taking advantage of. And if I leave you with these 5 pillars, I think that's the most important way to think about Elastic. We are trusted by developers all over the globe. We are trusted by enterprises. We have clear Gen AI leadership. Analyst recognition is only helping us do this more efficiently with our go-to-market motion. And lastly, we've been able to consistently deliver strong financial performance. So now I'm going to hand it over to Ken to really take us through the products. Ken?

Hey, folks. Thank you for being here. I get to do what I love now, which is talk about products. And I'm going to begin with a point that Ash made earlier. Ash was talking about the explosion of data that we're seeing, especially unstructured data. This is something I see all the time when I talk to customers. They're talking about how much data they have, how hard it is to manage it, how much that data is growing all the time. But it's not just the volume, the amount of data that they're struggling with. It's also that it's siloed across the enterprise in a mix of structured and unstructured formats. And even when it's structured, it tends to be different schemas. So they're dealing with all this messy siloed data. But they also know that in the age of AI, this data, this unstructured messy data is something has new value. If they could put it to work. If they could use this together with generative AI and agentic AI systems, this new messy unstructured siloed data has new value. But how do they do that? Enter Elastic and the Elasticsearch platform. Now for years, we've been helping customers get more value added data. No matter how messy that data is, no matter how siloed, no matter what type of data is, we help customers get value out of data. And in the age of AI to build these AI experiences on top of that data. Now in a few minutes, Steve and Santosh are going to walk you through the Search AI, Observability and Security businesses that build on top of this platform. But I want to talk about the platform itself, the Elasticsearch platform because I think that is why we win. Put very simply, we win because of the Elasticsearch platform, period. For me, this means 3 things, though. One, it's a world-class data store. It's a blazing fast data store. It is highly performing. It is highly scalable. Now customers always know us for being the search engine, but they sometimes forget that we're also a data store. And I would argue not just any data store, but the best data store for unstructured data. Second, we're the leader in relevance. As a search engine, as a vector database, as a context engineering solution, we excel at relevance. We're the leader in relevance. No one has a deeper set of capabilities than us for getting relevant context out of data and presenting that context to other systems. No one has more customers. No one has been doing this longer than us. We are the leaders in relevance. And third, we win because we win with developers. We are loved by and we are built for developers. Developers worldwide, millions of developers have used Elasticsearch over the last 15 years. And we have deep roots in open source. And because of these deep roots in open source, we've built out a huge community that take Elasticsearch into the enterprise, where we're able to convert into paying customers. All right. But let's unpack this a little bit, beginning with why we win as a data store. It starts by supporting all the data. It doesn't matter what kind of data it is. The Elasticsearch data store supports everything. It could be metrics. It could be traces. It could be IoT data. It could be product telemetry. It could be business data, any type of data, structured or unstructured, we support that. And one of the things I think that is really, really unique about us is that we support the type of capabilities that are only typically supported by structured data stores, but for unstructured data. And let me give you an example of what that means. So it means that we can combine all this data together. Even though it is not the same format, even though not the same schema, even if it's different types of data, even if it's unstructured. And we can do things that are typically only possible with structured data tools. For example, you can join data. You can -- using ESQL, or query language, you can actually literally do joins on 2 different data sets that are not only the different schemas, but are unstructured. You can create fields on the fly. You can do math operations on unstructured data. You can do sorting and filtering. These are things that are typically not done with unstructured data, but you can do that with the Elasticsearch platform. Or if you want to run ML jobs or AI or various types of analytics on this. It doesn't matter that the data is all kinds of different formats. It doesn't matter if it's unstructured. You can run that across this. So you can run correlation analysis. You can run anomaly detection, not just on metrics and traces, but also on business data. No one else can do this. And it's not just that we support any type of data that makes us a special world-class data store. It is also because we have a reputation for being incredibly fast, highly scalable and highly performant. But it's not a reputation that we take for granted. We are constantly reinvesting in making sure we are highly efficient and highly performant and highly scalable. And typically, there's 2 types of investments we make. Sometimes, they're data type specific. We have types of improvements that we do to make logs really great on Elastic, to make metrics or vectors really great. For example, over the past year, we introduced LogsDB and TSDB. Both of these improvements are delivering 70% storage efficiency for our customers over previous versions. We've also been doing a lot of work in vectors. Ash mentioned BBQ, Better Binary Quantization, my favorite product name. It is a type of compression for vectors. But what it does is it allows customers to have huge savings in terms of memory footprint and in terms of storage. So you get 95% efficiencies in terms of memory. But not just that, it also improves performance. It's actually made us 5x faster than OpenSearch with their default quantization techniques. But now sometimes, the improvements that we do in our data store affect all data in Elastic. For example, the work that we're doing with NVIDIA to do GPU acceleration of all data in Elastic or the work that we've done on a data lake architecture. Over the last few years, we introduced a new data lake architecture. It's built on object storage like other data lakes. But unlike other data lakes, you don't have to compromise performance. We are the only data store that allows you to have blazing fast performance, real-time performance. You can build real-time applications, latency-sensitive applications on top of our data lake. So you get the durability of object storage, you get the scalability, you get the efficiency of object storage, but you don't have to compromise performance and only we can do that. All right. You can't talk about Elastic without talking about search, and you can't talk about search without talking about relevance. And we are the leader in relevance. And we're especially good when it comes to finding context and intelligence in any data, no matter whether it's structured or unstructured. Now if you have structured data like you have a table or a relational database, there are plenty of good tools that you can use to query that data. But if you have unstructured data, what do you do? For example, if you have petabytes of logs, well, those logs might be different schemas. But even if it's the same schema, that same schema is going to have a mix of structured and unstructured content. So how do you sift through that? How do you find that needle in the haystack that could end up being a security vulnerability? Well, you need a great search engine. Or if you have a huge repository of documents or PDFs, like what our customer, DocuSign, has. And you want to do semantic search on that, or you want to build generative AI applications on top of it. How do you do that? Because these documents usually are opaque documents. And even if it's not opaque, it's usually a mix of structured and unstructured formats. But what you need is you need a vector database, you need a semantic search system, you need a retrieval system, a great retrieval system. And what makes a great search engine? What makes a great retrieval system? It's relevance. Relevance is at the heart of doing search and retrieval right. Now relevance has always mattered, but I think it matters even more in the age of AI. Back when we were mostly focused on returning results to a user like 10 blue links, you didn't want bad search results, right? You wanted good search results. But those are human there that can interpret those results. So you had room for error. But in the age of AI, you're not going to get 10 results, you're not going to get 100 results. You're going to ask a question of an LLM or an agent, and you're going to get one answer. And hopefully, that one answer is right. And for it to be right, that agent, that LLM has to be grounded in the right data, has to have the right context. But it gets even more interesting and more worries when you get into agentic AI, when you get into agentic AI, now it's not just answering a question. Now that AI, now that agent is performing an action, it's doing a task and potentially doing it badly. So now the consequence of not grounding that LLM or that agent, not giving it the right context could be disruptive, could be damaging. So this is why I think relevance matters so much in the age of AI, why context engineering as a concept is going to be talked about constantly as people move forward with agentic AI because it's all about having relevance. It's all about having context. That's what you need to do AI correctly. All right. So the vector database companies have been saying that the answer to this problem is a vector database. And it is, to an extent. And I'm saying this as a vector database company. We were one of the first vector databases out there. We are the most downloaded vector database. We are the best vector database. But I also know that vectors are not enough. Vectors are not enough. It is not enough to simply store and query vector embeddings. You have to do a lot more. For one, you have to help customers prep data and ingest data. And they're going to have all kinds of different data stores that you're going to need to help integrate, pull data from different places. You're going to have to parse that data. You're going to have to figure out a chunking strategy if you're going to chunk that in order to create the vector embeddings. To create vector embeddings, you're going to need a model, an embedding model. Hopefully, it's not just a single language embedding model, but maybe a multilingual embedding model, maybe a multimodal embedding model, but it doesn't stop there. Because now you need to work on retrieval. And one of the things that we have learned is that to get relevance right, you have to combine different techniques here. It is not enough to just do vector search. You're usually combining vector search and lexical search or doing graph traversal or you're doing geospatial search or filtering faceting like Ash talked about. And if you're combining different data sets, you're going to need to do reranking using the rerank model. These are all the different techniques that you use for tuning relevance. And if you're going to tune relevance, you're going to need a way to evaluate results and make sure that you're getting the right results from that tuning of relevance. So how do you do that? You need to have A/B testing. You need to have a framework for doing this evaluation. Then you're going to want to take this application to production. So you're going to need to monitor that application. You're going to need to have a query logging and metrics and tracing for that application. You're going to need to make sure that you're doing cost and token tracking. It doesn't stop there, though. Because now we're in the age of agentic AI. And the patterns change a little bit. So it's -- previously, you just focused on passing the right data to an LLM in a context window. But in an agentic architecture, you're doing things differently. Now what you're doing is you're exposing that data as an MCP tool to an agent or to an LLM and you're helping with tool selection. Well, now you need a whole new set of things. Now you need to have prompt management. Now you need to have memory management. You need to have a set of capabilities for building MCP tools and you need to have capabilities for helping the LLM dual tool selection, a lot more things you need to do. And all of this is context engineering. This is a term that you're going to hear a lot over the next couple of years. Context engineering is vital to doing agentic AI right. And it's not a term that we invented. It is something that's used popular in the industry, but we did pioneer a lot of the capabilities in this space. We did trailblaze a lot of the technologies here because you see, we do all of this. Now Steve is going to show you, many of the capabilities, especially the agentic AI capabilities that we've been working on. But I just want to make sure that it's clear, we didn't like pivot recently to doing this. As Ash pointed out, we've been doing this all along because context engineering is all about relevance, and we are the leaders in relevance. It is in our DNA. I believe that we were made for this moment. All right. Finally, we win because we win with developers. We have -- over the last 15 years, we've built a community of millions of developers that know and use Elasticsearch. According to the annual Stack Overflow survey, 17% of all professional developers in the past year have used or built on Elasticsearch. 19% of all AI developers worldwide use Elasticsearch. And a lot of this is because of our reputation in open source. As Ash pointed out, we have been downloaded 5.5 billion times, which makes us one of the most popular open source projects of all time. The other thing developers love about us is that we continue to expand our platform, offering additional capabilities all the time, make it possible for our customers, our developers to build amazing things on top of our platform. Over the years, I've been amazed at what people have built. They built all the ridesharing applications built on Elasticsearch, matchmaking sites built on Elasticsearch, fraud detection systems, signal intelligence systems, all these things built on Elasticsearch. And 3 of the most popular use cases have been in Search AI, Observability and Security. And to make it possible for our customers to get up and going, get up and running, using us in these 3 scenarios, we created out-of-the-box solutions that make it possible for them to get started immediately using us as an observability platform, using us immediately as a security platform and for Search AI. And with that, I want to turn the stage over to Steve and Santosh, who are going to walk us through these 3 solutions and these 3 businesses, beginning with Steve Kearns.

Thank you. All right. Hi, everyone. I'm Steve Kearns. I'm the GM of the Search business here at Elastic, and I've been with the company for 11 years. So I've lived through this time where Ash and Ken are saying, we've always been a search company. Relevance has always been at our heart. And I can tell you it's true. And it's really been fun to see the excitement, the interest around generative AI and how central relevance is to success in those applications and in those use cases. And so when you think about the kinds of use cases that people run on top of Elasticsearch, you can think about search powered applications. For the entire history of the company, we've been a great data store, a great platform for engineers to build compelling engaging experiences for their customers. In fact, I bet a number of you here today, I don't, looked up a coffee shop, might have placed an order. You might have then filed an expense report. All of those kinds of applications, those things are built on top of Elasticsearch. And so I met many of you experienced Elasticsearch in one way or another today as part of these experiences. And people build on us because of the performance, they build on us because of the relevance, but just as importantly, because of the flexibility that we provide as a platform. When you bring data into Elasticsearch, all of the fields in that data are instantly queriable by default out of the box. And that's a great experience for a developer, and it's why so many applications continue to be built on top of Elasticsearch. But as we see the excitement around AI, we're seeing these new workloads. AI is driving the development of new experiences often in response to new expectations from customers or from employees that are using AI tools at home, using AI tools in their personal life, and they want that same experience in the business. And when you think about these conversational experiences, and Ken talked about this really well. The conversational and agentic experiences, they depend so much more on getting the right business data, the right context in order to give you the right answers. And if you think about what does it take in AI to get that right answer, they're great. Language models have a wonderful world knowledge, but they don't know about your business. They don't know about your job. They don't know what problem you are trying to solve. And so the job then that we have to do on the data store side is to get the right context, the right information from your business to the model to help you with your task at hand. And this is really important with conversational AI just asking a question. If it doesn't give you the right answer, users are going to lose trust in that system very quickly. And it's even more important, as Ken said, when these systems start to take action on your behalf, the agentic workflows and they're taking action, they're changing things in your business, it has to do the right thing. It has to have that right information to make those decisions and make those choices. And so we really believe very strongly that relevance is at the heart of every successful AI implementation, every successful AI project. And so it's no surprise then that the core of why we win is all around relevance. Ken talked about all the features, all the capabilities that we've added into the platform. But when you think about relevance, it's not about one feature. It's not like, yes, we added one new feature, done, relevance is solved. Relevance is really personal. It's about what's the data that you have available? What's the information need that the user has? What are they trying to do? And do we have the information available to help them with that? And so relevance is about flexibility. The flexibility that we have as a platform to retrieve the right data to give you the tools. Because sometimes you're looking for one right answer, what's the document that contains the answer to the question that I have. Sometimes I need to see a chart. Sometimes they need to look at an outlier. Sometimes, I need to find just one piece of information, but see how it compares to the rest of the population. It's one customer, how do they compare to the rest of my customers. And so this is a really important element around why we win, this flexibility that we have to get the right relevance. Another thing Ken talked about a lot of the work that we've done around speed, scale and relevance. And this is really driving a lot of the places that we win. If you think about speed, e-commerce companies, a lot of the major e-commerce companies that we know and love are building their e-commerce search on top of Elasticsearch because they recognize that better search quality delivered faster leads to more business for them, right? You don't want to wait for your search to complete. You don't want to get the wrong results. It makes a huge difference in the e-commerce space. We also see this on the scale side. We've got one customer, document management customer in Elastic Cloud today, storing over 5 billion vectors in a single use case in Elastic Cloud, and they're only able to do this because of the efficiency work that we've done. Ken talked about BBQ. It's a new thing actually being talked about, I think, right now in the other room called just BBQ, another order of magnitude efficiency in memory, and this allows these multibillion document use cases to be built at all and to be built on top of Elasticsearch. And finally, we really do focus on developers, right? Developers are the ones very often making the technology choices. And so the better experience we can provide them, the more batteries included, the more we can provide out of the box to make this easy to get started, the better developers are going to find success building on top of us. And one of the key things, and Ash touched on this at the beginning. One of the key aspects of relevance, right, is actually having the right models, the right language models to generate embeddings toe power of vector search. These language models are really important. And so I couldn't be more excited to have the Jina team, Jina AI team joining Elastic. They're well known in the industry as a leading tier research organization, putting out incredible high accuracy, low resource usage, very efficient, very fast models. And one of the things that I love about the team, the models are great. I'll talk about those in a minute, but I love the way that the team works. With every model that they release, they publish a research paper along with it. This has done a tremendous amount to help build the credibility in the market, the trust and the reputation that they have comes from the way that they work in the open. They make their models available on hugging face under an open weights, but not open source license so people can download it, advance the state-of-the-art in research. And when they want to use it commercially, they need a license. This is a wonderful model, very well aligned with how we operate our business as well. Now the models themselves are really exceptional. They've got a multilingual model that allows you to do same language and cross-language searching in over 100 languages. They've got a multimodal model that allows you to do searching in the images, searching in the embedded text and charts and graphs inside of other documents. And this multimodal search is really important. But that efficiency part is also important. They just released a new V3 of the reranking model last week that has a novel approach to the way that they do reranking, but what it really allows them to do is provide a highly efficient model. It runs fast. It runs efficiently. And it does that while giving leading class accuracy. Really impressive work that the team has done, couldn't be happier to partner with them going forward. And it's not just about Jina. Every major player in the AI ecosystem has an integration with Elasticsearch. And it's because they all recognize how important relevance is to getting successful AI implemented in businesses. And so all of these partners, we partner with them very closely. But we talked about some of the work that we're doing with NVIDIA on GPU acceleration. We're also inside their AI factory along with Dell. And we've got integrations to every one of the agent building frames like LangChain and LlamaIndex, who are here today, and so forth. So this idea of being able to be the most flexible platform to integrate anywhere, it's central to our strategy. There's not going to be one model that's better than all of the others and rules. There's not going to be one agentic framework that wins. We want to make sure that no matter where you're starting, no matter what you're using, that you can use Elasticsearch to provide the relevant context for that application. And this really leads into not just the models, not just the integrations, but the platform and the product itself. And when you think about Elastic, it's a search engine. It's a vector database. It's a no-SQL store. It's a columnar store. It's a geospatial store. We have all of these capabilities, and we wrap it up in a single API. That's a lot for a developer to learn to be able to just get started and start building applications. And so we're continually working to simplify that process. When you bring data into Elastic, we use our first-party ELSER model by default out of the box, to generate embeddings for you, so you can get hybrid search without having to learn about what's a vector anyway. You don't have to start by doing that. But if you have a model that you prefer, if you fine-tune a model specific for your environment, great, you can plug it right in and there's a place for that. I mean this progressive disclosure of complexity, we really think about this a lot and how we can simplify the experiences for our users. And nowhere is this more true than in the agent building space. And so if you think about all the steps that it takes to build an agent, I pick a framework, get a language model, connect those things together, set up memory, figure out how to write queries against the engine. It's a lot of steps that people can take. And if you already have a framework, we partner with them. It's great. You have the best possible experience there. But if you're just starting with your data, you're saying well, what can I do with this? We want to do a lot to make that easier. And so that's why we introduced earlier today, our Agent Builder feature. And this is really designed to take any data that you have inside of Elasticsearch and instantly make it available for agents and chat and to build and extend from. And rather than try to describe it, why don't I just show it. If we can switch the laptop over, please, to the demo. Let's take a look. All right. Fantastic. You seen that? Good. So what we have here, this is a live running cluster on Elastic Cloud Serverless. And what I did is I loaded up just a simple set of data. This is a financial services data set here. And it's got some account data, some sort of semistructured data around accounts, assets and holdings and then a lot of unstructured data, which is very representative of what we've seen at a number of our customers. News data, Financial analyst reports and then interactions between our financial advisers and the customers. And I've done nothing other than that, I've just loaded the data. And when I come up to the system, right out of the box, I've got a new tab, I've got a new experience in the UI that says agents, and I can start asking questions. So now I can start asking questions of the system. And what's going to happen here is I'm asking the question, the system is now the agent, the built-in agent that we provide as part of Agent Builder is going to look at the data that it has in the system. It's going to use a set of native tools, understands my question, using this, picks an index that it's going to search, writes the query, crafts this query and then runs the query, passes that result back to the language model, and we're going to see the answers coming back in terms of what it actually believes from the reports, what it's going to see. And so right now, what I just did would normally take a developer days, hours, maybe even weeks, if you're just learning this technology to go and assemble all of the moving parts to answer this kind of a question. And you can keep going with these kinds of conversations, you can say sort of like, what are my customers -- so you can start to now just continue to ask these kinds of conversational questions with the data right out of the box. Again, I've done nothing to customize the system. I just loaded the data and started a conversation. This is how it should be. This is a POC, the time it takes to understand what kind of applications can I go and build on top of this, went from days and weeks to seconds. You can start that conversation. Now if I wanted to go further and say, "Hey, this is great. This looks like this is going to work for me. Now I want to customize this. I want to put this application in front of my actual financial advisers. I want to customize the system to make sure the most common tasks, the most common workflows are great. And for that, we give you an ability to customize the tools. When I said before, we want it simple out of the box, but still to have the full power of Elasticsearch at your fingertips, this is where custom tools come in. So I can come in and I can define a custom tool. In this case, I know my financial advisers are going to be doing summaries of portfolios on a regular basis. I can add this custom tool, and I can do that with the full power of the Elasticsearch query language. Here, I'm doing joins across the structured portions of the data. I'm doing hybrid search using a semantic search and a lexical search against the content and the news that might be related to this portfolio. And this ability to use the full power of Elasticsearch to customize it and provide that as a tool back to the language model is really powerful. So this is the first way that you can customize the agents that you build with Elastic. The second thing that you might want to do is actually change the way that the agent engages. Out of the box, the default agent we provide just generally tries to be helpful and answer the questions with whatever data it has. But if I'm going to put this in front of a specific type of user, I want to customize it. I want to give it more specific instructions. How do I help the users more? How do I make sure that this knows exactly what tools to take advantage of? And so here, you can see this custom agent that we created has a very specific prompt to help out AI advisers -- or sorry, help out human advisers, financial advisers. And we've got that set of tools, including the custom one we just looked at. And with nothing more than just a customized set of instructions, a custom prompt based on Elasticsearch query language, I can now start chatting with this specialized agent and start to ask a new set of questions. So I can ask a question like this. Who are the top customers? And again, just like we saw before, this agent is going to look at the tools that it has available, figure out what's the best strategy for getting the answer out of Elasticsearch. And it's going to then figure out, okay, in this case, they need to write an ESQL query. Great. Let me write that query to figure out who our top customers are. Go out, run that query, bring the results back. And it's really nice because this is, again, a capability that's provided right out of the box. And what you can see here is it's able to bring this data back. It sees that it's a tabular data. It sees that this is the kind of data that probably belongs in a chart, automatically starts charting it for me. Over time, I can customize. I'll be able to add this right to a dashboard straight from here to start building a regular view on top of the data. And if I come in and I wanted to just kind of look even more closely at some of the particular accounts, right? So let's say I'm getting ready to meet with a customer. I can ask for a specific summary of that portfolio. So now, I can ask a more sophisticated question. Look at their portfolio, summarize it for me, then look up, we have further investments for the portfolio that they have. What's related in the news that might be interesting that they're going to ask about when I have a meeting with them later? And so this idea here is going to trigger the model to, again, go and look and say, what tools do I have available to answer this question? And that custom tool that we took a look at before the portfolio summary tool is exactly what's needed for this sort of a query. And so I can go from having that top level query that says who are my top customers to a quick summary of their portfolio and then an understanding of how AI-related news might be affecting them. This idea of combining your structured view with your unstructured view is incredibly powerful. And so this is, again, the second major feature here is I just created a custom agent in like 5 minutes on stage live. This is incredibly powerful for accelerating the process of building these applications. And it's not just about like answering questions. This is really nice. It's also about taking actions. And so I can do this. I can actually say, e-mail this to one of my colleagues. And what's going to happen here is it's going to say, okay, great. It looks like you want to take an action. This is what Ash referred to as the Workflows feature. We've added a new set of capabilities for taking multistep complex actions into the system. And this ability to find these workflows is actually a set of technologies and capabilities we acquired from Keep. We acquired, I think, 2 or 3 quarters ago, really quickly integrated that, and we can see the send message here, very quickly looking it up. And what that's doing is it's actually going to our workflow system, and it's using this system to look up who is the contact that we want to send it to, let's look them up in the database, retrieve their e-mail, their contact preferences and send the right kind of message to that user. You can imagine how these workflows can get a lot more interesting over time. This is just a taste, but this is how we make actions available to these agents. The last thing that I'll touch on before I get to the end of the demo is everything that we saw today, everything that we looked at, everything from these charts to the interactions, the whole thing, this is all a set of APIs in Elasticsearch. So it's wonderful. We provide this chat experience. If you want to bring your customers, your employees directly into Cabana to access it, that's great. But if you already have an application where you might already be sending your users, you can extend that. These are just APIs. The tools are available over MCP, a very common way to connect agents to data or the agent itself is available over [ A-A ], so you can embed it directly into an agentic UI. And so this is just a sample of what you could build on top of Elasticsearch with those chat experiences as a native part of it. And so you can see here, able to just bring up the single account that we had just looked at with Elasticsearch queries directly. And then we can pull out the chat experience, and we can actually have that same kind of a conversation with the same agent that we just created right here in this custom application. And so this idea of being able to easily walk up to your data in Elastic and start a conversation with an agent, extend that agent specifically for the workflows and the tasks that you have, connect that into your business and take action and then build that into the experiences where your users are, this is a dramatic simplification of what it takes to build these kinds of agentic applications. All right. With that, can we switch back over to the slides, please? Just close out with one more here. So the thing that I wanted to just end on is the opportunity that we see in the AI space is huge. And I think it's a very simple story, right? We see, and I hope it's clear at this point that relevance is more important than ever before in the agentic world, in the AI-powered world. And Elasticsearch is the best platform for relevance. And so that means that our opportunity is massive when it comes to this emerging market opportunity for AI and context engineering. And with that, let me hand it over to Santosh to talk about our Security and Observability businesses.

Thanks, Steve. That's amazing. I'm Santosh Krishnan. I'm the GM for our Security and Observability businesses. And I'm really excited to share with you, some of our AI innovations as well as the platform advantages that we are bringing to the Security and Observability spaces. Starting with Observability. As Ash mentioned, in observability, customers typically start their journey with us with log analytics. And they use our platform to efficiently store all their logs and then use the platform capabilities in search, machine learning and AI in order to triage and investigate issues. That's the primary purpose where we land most of our customers in Observability. Over time, many of these customers have organically grown even in many cases, without our product, they have organically grown to add additional signals like metrics and traces and so on to bring together and use our entire end-to-end observability suite. One of the main reasons they do that is that they can now correlate all of these signals using a single platform, a single query language, a single set of workflows. This is the main reason why they actually come to us for end-to-end observability all the while taking advantage of all the data store optimizations that Ken spoke about the LogsDB, the TSDB, all the specific optimizations that we have made for those signals, customers benefit from that as well. The Observability space itself is changing. We have already seen a couple of generations. We have gone from legacy observability tools, which are really just alerting engines. They are health monitoring tools. It tells you what happened, but it does not really help you what to do about the thing that just happened. Those used to be the first generation of observability tools. I would say we are currently in the second generation, where there is an added focus on triage and investigations. Unfortunately, though, that added focus has come with very complex instrumentations that you have to build into your data sources so that you may investigate your issues later. Dealing with messy unstructured data, which is prevalent in observability, it's actually a [indiscernible] to try to lend structure to it or try to solve it using instrumentation and such. AI to the rescue, because things are changing again. Observability is changing again because harnessing the power of AI and all of that information, that dense information that resides in your logs in order to triage and rapidly resolve your issues is now going to be the focus of the next generation of observability. Make no mistake, logs with AI will be the foundation of how you do investigations in this next generation. Together, as we have spoken about, OpenTelemetry is the other trend that is also happening at the same time. And this is really the industry's way of trying to get away from all this complex instrumentation to understand the semantics of data and so on and so forth. So we recognize that. We adopted it. We contributed to it. So when you combine AI and OpenTelemetry, you essentially get to this next generation of observability. And of course, it's our goal to leverage our natural advantages in dealing with unstructured data and using the native AI capabilities to help our customers go through this journey. To summarize why we win, customers continue to choose us for our platform advantages in speed, fast investigations, efficiency in storing all of those signals in observability and of course, those specific data store optimizations, which I mentioned. More recently, customers have started selecting us for our native AI and relevance capabilities in order to investigate faster. And I'll speak a little bit more towards that in a minute. We bring all of these innovations in an open and extensible fashion. We have always been open source. But now with OpenTelemetry, we have gone all in. And over here, of course, we want to take this burden of instrumentation away from SRE teams. Logs are back. So now I'm talking about the modern generation of observability. If you actually look at all the signals that you use in observability, logs are the most information dense signals which you have at your disposal. Your metrics and infrastructure monitoring can tell you what happened, what went wrong. Your traces and your application monitoring tools can tell you where something went wrong, so you can trace, as the name suggests. But in order to do deeper investigations, you always have to go back to your -- to the information which is in logs in order to understand why something happened. Logs have always been the repository of knowledge, which answers the why question. And now with AI, you can actually harness all of that dense information that's already sitting. It's already sitting in your data store in order to get to rapid issue resolution faster. By the way, today, in Elastic, we already offer our customers a combination of search capabilities using ESQL that Ken talked about, machine learning capabilities for anomaly detection and such. And now we recently -- we just introduced this capability called Streams to just make logs magical. To give you an idea, even setting Elastic aside, in any observability tool today, if you want to get the -- get all the value out of logs, you need to understand where the logs are coming from, what are the sources of these logs. A human has to understand that, build integrations and such. You have to understand what is contained in those logs so that I may know later what to look for. And this is especially true because logs are messy. And it does not come with structure, a priority structure and semantics. And then last but not the least, okay, I figured all of that out. I now have to also decide -- I need to figure out what questions to ask. So when there is an issue, I have to go back to the system and figure out what queries to run, what questions to ask. And these have been burdens on the SRA teams until today. And we are taking all of those burdens away in understanding where your data is coming from, gleaning what it contains, as well as suggesting what one ought to be looking for and when time comes for investigation. With that, I actually want to show you -- I'm going to invite David Hope, who is a leader in our product team to show you what we just introduced.

Thank you very much, Santosh. Right. So as Santosh was saying there, right, logs are back. But of course, you may not have seen them, but trust me, they're everywhere, right? You click a button on your remote control, you make a trade, large volumes of log lines are generated. They're used by practitioners to understand your experience or how quickly your trades are going through. Today, logs are incredibly difficult to work with in any observability solution. The observability industry has left a lot of room on the table to get a lot of value out of logs. Engineers today have to code integrations. They have to create complex pipeline processing code, and they have to know exactly where and what to look for when they're doing investigations. Now off-the-shelf integrations like the ones you see on the screen here, they help a little bit. But quite often, you can't find the integration you need and application developers writing custom application code, they don't usually use a standard pattern or format, making it incredibly difficult and time-consuming to process logs. Now with Elastic Streams, we're changing things. No need to install any integrations anymore, no need to fully understand all your log sources. All you do is point your logs at Elastic, and we take care of it. No more complex pipeline processing code to manage. No more trying to figure out what systems your logs belong to and no more hunting high and low for integrations. The magic here is that LLMs are amazing at understanding unstructured data. And Elastic, with its expertise in logs and context engineering, means that Elastic Streams can automatically organize your logs, find meaning in your logs and find problems in your logs. So in this new world where logs flow smoothly to Elastic with any -- without any pre-interest pipeline processing overhead, the first thing that practitioners want to do is they want to organize their logs. What we do is like any good filing system, we look at the data to understand exactly how to organize our logs. And we do this with AI. So that click here suggests partitions with AI, and the AI goes off. And it's found 2 systems, a Hadoop system and a Spark system, perfect. I now have my logs nicely organized, and I can find what I need very, very quickly. Now the next thing the practitioners want to do now that we've had our logs organized nicely is they want to find the meaning in their logs. Now you heard today a lot about unstructured data. The majority of our customers send logs to Elastic in unstructured formats. And doing analytics on unstructured data can be pretty tricky, right? So if you took like a written piece of text and you tried to put it in your Excel function, you probably wouldn't get a great result, right? So Elastic is making things different here, right? We're an expert in dealing with unstructured data. And what we can do with Elastic Streams is we can find the patents and the meaning in the logs and the context. Let's just take a look at what that looks like here. I can create a processor, and I can use AI to look at the logs. And you can see straight away that it's found the patents in our log files. And it's not just found the patents, but there's some contextual data in here. All we gave it was the log file, and it has inferred that there are stock symbols, quantities and prices in here. Obviously, we're using trading data because we want to bring this into your language a little bit. So now once I apply what it's found, you can see straight away that I can now do really nice analytics on this data like trying to find what the most popular stock is over a particular time frame, for example. And I can quickly save that and move on to one of the most important things that practitioners want to do. We've organized our logs. We found meaning in our logs, but now we want to find problems in our logs, and we need to make this easy. I don't want to drown in logs or dig around trying to find queries that I need to find problems. I use significant events and significant events can quickly find problems in our logs for the specific systems that we're working with. So here, you can see that it's depicted that this is a Spark system, and these are specific problems that relate to Spark systems. I can bring these into Elastic. And all of a sudden, we can monitor our Spark systems for things like out-of-memory errors or where the task execution is performing correctly. When I do that, our machine learning-powered change point detection can immediately discover whether or not any of these problems have occurred. I'll give you an example here, this out-of-memory error. If we dig into this, we can see the query that was generated by AI. And we can see all of the logs that relate to that out-of-memory error. I can quickly dig into one of these, which makes lives a lot easier for practitioners, as you can imagine, and it automates the investigative process and root cause analysis. Here, if I use our AI assistant to ask what this message is about, I can immediately see the root cause of the problem with the Spark system. It's given me all the information that I need to see what the problem is, and it's recommending how to fix that problem, too. So in summary, organizing logs, finding meaning in logs and find problems in logs has been a problem that has plagued the observability industry. Elastic, with its expertise in applying AI to unstructured data is bringing clarity to this chaos. We're bringing Agentic AI and LLM technology to logs, to organize logs, to find meaning in logs and to find problems in logs in minutes, not hours, so that your trades can go through successfully. Thanks, Santosh.

Thanks, David. So to summarize in observability, as you can see in this future, where we bring to bear the power of logs and our ability to deal with unstructured data in general and how we apply AI towards automating to that root cause analysis. Our objective is, of course, to grow our market share in logs as logs themselves become more important in the age of AI. We are growing beyond logs as well. So that's our additional opportunity on top of what I said. If logs are the center of investigations, our objective is to grow, is to expand from that center by adding those additional signals towards the multi-signal observability end-to-end offering. So those are the 2 main opportunities in front of us in observability. Switching to Security. You will see a lot of similarities there. So in Security, customers typically start their journey with us with SIEM and security analytics use cases. This is largely to displace their -- or replace their existing SIEM or in many cases, to also augment them as well. So we do have both displacement as well as augmentation customers today. And the goal over here is exactly similar to the one which I showed in observability, which is to, in this case, to modernize your security operations center using the combination of the search, machine learning, anomaly detection as well as the AI capabilities that we offer. Over time, and on the backs of a lot of investments that we have made on endpoint and cloud protection, many of our customers are now organically growing into those areas as well. So from the same platform, single click, you would go into our console add endpoint. And now suddenly, we are actually your XDR solution as well. Now it is not just the ease of use and tool consolidation why people usually grow into these other use cases with us. We actually offer a truly unified platform approach to bring all signals, whether they are coming from endpoints, identity systems, firewalls, you can use all of these signals together for your detection and investigation needs. We are fast gaining momentum in endpoint Security. We have been making investments both in our threat research as well as in our agent capabilities for on-device blocking, remediation, forensics, all of those features that you expect in EDR tools. And on the back of all of that investment, we are now being recognized as one of the top-tier endpoint protection systems out there by third-party benchmarks like AV-Comparatives and such. When you look at how the Security space, SIEM, Security analytics, that entire space is evolving, that is changing as well. And one might even say it is changing even faster than the observability space. So here, we actually went from legacy SIEM systems, which is all about compliance, compliance, visibility, single pane of glass to see all your alerts in one place and so on and so forth. It is the next generation of SIEM, SIEM 2.0, next-gen SIEM, whichever name you want to use, the current generation of SIEM, where we actually expanded beyond that into actually finding issues. So detecting issues with rules and machine learning, workflows for investigation, response and remediation, all of this got added in the second generation of SIEM. We have been beneficiaries of that. So when I said that customers have been adopting us to displace their legacy SIEM, we have grown on the back of that transformation. But that's changing again as well. With AI, and this is not just a matter of adding an AI assistant or copilot or something to your existing SIEM. With AI, every key workflow that InfoSec analysts use is getting automated by AI. And by every key workflow, what I mean is all the way from ingesting data, writing your detection rules and dashboards and other content, triaging alerts and investigating attacks, finding the risk posture of your IT infrastructure, running workflows to take remediation steps. These are, I would say, the main things that you do with a SIEM. All of them are getting transformed with AI. And here, make no mistake, while we were beneficiaries of the previous generation, we are actually leading the charge over here. There is no customer conversation in security that I now have, which is not about modernizing their SOC with the AI capabilities, which we offer. We have been first to market in it. I'll show you a few of those in a minute. Why Elastic wins in Security? Well, customers do choose us still for the benefits of the Elasticsearch platform in terms of its speed. We are the fastest security tool out there for doing things like threat hunting and such, efficiently storing all your security data so that you don't have to prefilter things away using ingest pipelines and such because the moment you prefilter, you will lose some threats in the data which you dropped. So we offer those benefits. We -- as I said, we are redefining the SIEM as we speak. We are actually leading the charge over there, and we do win because of that already over the last 1.5 years or so. And last but not the least, we actually provide a true platform for unifying all your signals. This is why we are actually starting to win in XDR. And that's because it's really our architecture. We are not taking a SIEM in one place, an EDR in another place, drawing a circle around it and calling it a platform. It's actually a true platform that brings all the signals together for your consolidated detection, investigation and response needs. Let me speak a little bit more about what we have done when I say that we are embedding AI everywhere. Over the last 1.5 years or so, and I'm not even going to talk about our AI assistant, which, by the way, was the first one in the industry in the security space, we have actually embedded AI workflows throughout our product. We introduced a capability called automatic import, and this is to onboard your data. By the way, things like import used to take months and months in that initial part of the implementation. Now it takes weeks at worst. We introduced a capability called automatic migration so that you can bring all your rules and dashboards from your existing tools and they get migrated automatically into Elastic. And last year, a little more than a year ago at RSA last year, we actually introduced this capability called Attack Discovery. What that does is instead of your InfoSec analysts getting inundated by all the alerts that your detection rules and machine learning jobs actually found, it coalesces all of those alerts into a few attacks that matter. So now the InfoSec analysts instead of looking at each alert to triage, they can actually go into those attacks and go investigate them and spend their time in a prioritized fashion. This has been one of the most well-received capability in our security offering over the last year. Now we have made a lot of those innovations in AI. We are even allowing our customers to use our AI capabilities on top of their SIEM tools and XDR tools because we want to meet them on their journey on where they are so that we can add AI capabilities as an entry point and then over time, migrate the rest of the SIEM onto Elastic as well. So this is something that we introduced this year. The Elastic AI SOC Engine goes by the colloquial term ease. So now you can easily adopt Elastic with AI. Coming soon, we are going to be introducing the Elastic Workflow engine that Ash talked about on the backs of the acquisition by -- acquisition of Keep as well as AI-based entity analytics. But instead of talking about all of those, I'm going to invite James Spiteri, who's a leader in our product team in security to actually show you some of that.

All right. Hello, everyone. Let's dive right into Security. So can you all see this? Perfect. So just as David described how challenging it is to work with logs, Security teams face a very similar daunting challenge with what we call alerts. Every day, they log into their security analytics platform, and they're faced with screens like this. They're faced with situations where within a 24-hour period, they have 1,000 alerts or warning messages to deal with. These could all potentially indicate something bad happening within their enterprise. And these alerts can span multiple systems, multiple networks, multiple technologies. And the traditional way of trying to find out, hey, is this something I should investigate? Are they related? Are they not? Is to go through each and every single one of these manually and try and figure that out. Well, as everyone else was saying about 1.5 years ago was the security industry was figuring out how to embed AI, we released Attack Discovery, where users don't have to go through that mess of alerts anymore. They go from 1,000 things to deal with to -- in this case, we reduced that down to 9 active attacks for them, 9 attacks within the organization, which matter most. And we've made this so simple for our users that any analyst of any skill level can understand, and it will work across all types of alerts and all types of data sets. So this particular ransomware attack, for example, we explained it very clearly in natural language. We very clearly highlight the hosts and other entities involved. We describe what we call the attack chain. So as an attacker within my environment, what did they do to actually be successful with this attack. And it's a really easy way for someone to digest and understand. Just to see the impact of how powerful attack discovery is, this one attack alone is made up of 148 of those alerts. Can you imagine a human being sift through all those 1,000 alerts, find these 148, stitch them together and write out the story nicely and neatly. It would have taken hours. And that's unfortunately what the industry has been used to. But last year, as Elastic, we changed the game, thanks to the power of our platform and large language models. Now of course, usually, what would happen next is as a security analyst, I would need to grab this and do root cause analysis. I need to find out within our organization, how do we go from an attack like this one to be able to eradicate and contain the threat. And of course, this is something where like our AI assistant, our conversational agent comes into play. Traditionally, before this stuff was available, analysts would have hundreds of wickies or pages and procedural documents that they would need to follow. They would have to manually sift through them or use traditional searches to try and find keywords here and there. That no longer works in this particular situation. We've brought Search AI to the mix. We've grounded the responses from the AI assistant with their own data. And now for this particular attack, they have an extremely tailored guide of what they should be doing within their organization, really clear steps to follow, really clear evidence and advice of what to do next. We've generated queries for them in case they want to dig deeper, so on and so forth. As an example here, one of the first immediate steps that the assistant has told me is, look, you're going to want to take this host offline. You're going to want to eliminate the spread of this ransomware by isolating this host. Thanks to our XDR capabilities, as an analyst, I'm able to grab this remediation guidance from the assistant and straightaway run it in what we call our response console, meaning any endpoint, which I'm monitoring with Elastic Security, I can take this action right within the same window. And again, we're able to do that because of our advancements to XDR, and we can support multiple different systems with this. Now you can see already how much easier we've made the lives of our users with Attack Discovery with the Assistant and many other AI features that we've implemented. But we want to do even more. We want to be able to eliminate the few manual clicks that I did today. And this is where workflow automation is really going to come into play. We're going to be able to not only tell analysts when an attack happens or give them remediation advice, we're going to be able to do all the triage for them manually. We're going to be able to provide these agentic flows to eradicate the threat and they go from having to look at a view of alerts or a view of attacks to going to a view like this, where, "Hey, look, we already tried this with AI for you. We've decided we can close these alerts or perhaps this alert needs a bit more work," and we're going to assign it to this particular user. So at the end of the day, what the user actually ends up with is a message like this, where, "Hey, here's what we detected in your environment, and here's everything we did when we detected that threat. We took this host of line. We quarantined some files. We created a case for you. We reached out to whoever is involved in this attack. There might be a few things left for you to do, but we're going to tell you exactly what to do." And this is the future with workflows, which is really exciting for us. Now we saw with Anish -- with Steve, I should say, the power of Agent Builder and the conversations we can have there with agents and also with workflows. So what we've done, as Steve already demonstrated, is we've brought workflows to Agent Builder. Now in the Security world, our users are going to have potentially hundreds of these workflows that they've built, hundreds of automations that are going to run automatically in the background or perhaps on a schedule. They're immediately transferable to Agent Builder. So people can converse with them without having to do any additional work, which is really phenomenal power to be able to give our users. So in this example here, I have my Agent Builder, I have created what we call a threat hunting agent, an agent specifically designed to work with Security data, whether that's structured or unstructured text, but also be able to take some of these actions with workflows. So what I'm going to do is I'm going to ask a very typical question that security analysts might want to ask. I want to ask, can you provide a summary of the top 10 processes run by administrator. Now what this is going to bring to me is full natural language searching of our structured and unstructured data at the same time. So agent Builder is going to identify what it should do. It's going to do that thinking step here. It identified in this case, I'm going to run a query and it found the top 10 processes run by administrator. Now some of these I recognize, but some I don't, especially this one. I don't really recognize this here as an analyst. It's being -- it's really caught my eye something I want to investigate further. So what I'm going to do is I'm going to ask Agent Builder here, look, what is this particular process? And agent Builder is going to identify what it needs to do to give me that answer. It's going to grab what we call a hash, which is a unique identifier for that file. So it identified it should run a query to do that. And then it's going to look up that hash, that unique identifier against what we call a reputation system. Is it going to -- is this file malicious? Have any other vendor seen this particular file? And in this case, said, yes, this is a malicious file. And it broke down this to me in a way that's really easy to understand. And it did that by invoking a workflow to pass that hash onto this reputation service. Now the next thing I might want to do is, okay, we know we have some form of malicious file in my environment. I need to open up an incident. I need to make the people who are on call aware. And I don't want to really leave the screen. I want to keep investigating with Agent Builder, but I also want to start that process. So we're going to ask Agent Builder to go ahead. So we're going to say, look, can you please check who's on call? That's the first thing we want to ask them to do, create a Slack channel for this incident. And then what we want is to also summarize all the findings so far, see how many instructions I'm giving the agent builder here. Add the person who's on call, add the on-call user to the Slack channel and explain what steps to take next, okay? So along those of instructions, typically, I would have to go some other system, find out who's on call, manually go create a Slack channel or go somewhere else to run the workflow, so on and so forth. And in this case, agent builder is going to go ahead and do that for me. So we checked the on-call schedule, which is unstructured text in this case. It found who's on call. It identified that to create a Slack channel needs to call the workflow to do that. It added the on-call user, in this case, is also James, to that Slack channel and lastly, summarize everything that James needs to do. If we go to our Slack here, we'll be able to see all of that. So this channel was just created. You can see 3:40 p.m. James was added and given all the information he needs to continue this investigation. So to recap, we went from a world where our users are manually investigating thousands of alerts. We fixed that with attack discovery. We allowed our users to eradicate the threat using assistant guidance with our XDR platform. And now with Agent Builder, we've brought the full functionality of context engineering to the security user to be able to contain, eradicate and solve security incidents. Santosh, back over to you.

Thanks, James. It's amazing. Let me leave you with a summary of our opportunity in the security space. As you can see behind all the innovations, which you actually just saw, our opportunity here is to grow our SIEM market share. Mind you, we are already doing well in the SIEM space. We are one of the fastest-growing vendors in that space. And our opportunity is to grow that market share further through, again, displacement and augmentation strategies, which I mentioned earlier, so that we can help our customers realize this future of the AI-powered security operations center. An additional opportunity that we have is to grow beyond SIEM, largely into use cases like XDR and CDR by providing that same unified platform. You can bring all your data together into a true platform to detect, investigate and respond across your entire IT real estate. With that, let me actually bring Ken back on stage to summarize.

I'll wrap up. Thank you. Just to wrap up the product section, I think you hopefully see that we have a lot of conviction, that we have a huge opportunity in AI. I'm going to summarize it as two opportunities. One is we have an opportunity to become a fundamental part of the generative AI and agentic AI tech stack. As a vector database, as a context engineering solution, we have an opportunity to be part of that tech stack. I think the other opportunity that I'm super excited about is using those same tools ourselves to disrupt the observability and security space. I think the observability and security space are going to see a lot of change because of AI. There's lots of manual things that happen in these two spaces. There's lots of pattern matching. Things are going to be much better done by machines than by humans. And I think we have an opportunity because we are developing these same tools to lead in that disruption of security and observability. So with that, I'd like to end the product section, we're going to have a break now. We're going to take about 12 minutes. If we could be back in the room at 3:55, we'll go through the go-to-market and finance sections. So see you at 3:55. Thank you. [Break]

Good afternoon. I'm excited to talk to you today about the transformation we've been driving in our go-to-market part of our business and to share with you the momentum we're seeing and the opportunity ahead. Ash mentioned to you that we have the advantage of incumbency. We're trusted by leading organizations around the world across all segments. Our Search AI platforms provide incredible value to customers for search, observability, Security. But I'm telling you, we are just getting started. Now many of you know I came to Elastic 20 months ago as Chief Revenue Officer. And after I arrived, we embarked on a process to evaluate and assess our go-to-market motions, our systems, our processes and our teams. We took a number of actions to get better as an organization. We're seeing results now, and we're refining and making refinements to continuously get better. This is a never-ending process for us. But all of this is designed for us to serve more customers and drive more growth for Elastic. I wanted to share with you some of the improvements we've made in areas we focus to drive our execution. Number one, segmentation and coverage. I'm going to double-click on this in a minute, but I'll tell you that what we found is that we had outgrown our previous model, and we had an opportunity to do a better job of aligning our sales capacity to our largest opportunities. Number two, incentives. We've aligned our sales incentives to drive incremental growth for Elastic and capture the AI opportunity that we have. Number three, operational rigor. We're running the sales organization with a much higher degree of operational rigor. This includes everything from how we forecast, how we generate and track demand, how we progress pipeline through the funnel, how we make sure we have hygiene in our pipeline, how we review deals. All of this is being done at a much greater level of granularity. And we're doing it -- importantly, we're doing it consistently around the globe up and down the organization. Now none of that would be possible without number four, improvements in our systems, our tools and our underlying data. My RevOps team has done a great job here transforming how we run the business. We've also made improvements in generating demand, creating pipeline. We track this every 2 weeks in great detail. Our marketing team is doing a better job than ever. Our sales development team is doing a better job of processing those leads. Our sales team, our partner organization is generating more and more opportunity. And we focused our teams on 3 specific sales plays, which I'll share with you in a minute. The other area that I'll highlight is we've gotten better at hiring. We've reduced the cycle time for hiring. We're now tracking that with the rigor that we do our forecast. We've gotten much better at onboarding and enabling our sellers. We've invested in underlying enablement platform that we didn't have before. We've got more structure, and I'm really happy with the results. Now let me jump in for a minute on segmentation and coverage. So I know this was a topic of conversation 5 quarters ago. And like most tech companies, at a high level, we segment the market between enterprise, commercial and SMB. And SMB for us is our monthly cloud business, our product-led growth. And what we wanted to take a look at after I got here is how are we assigning accounts, what was the logic we were using to sign accounts into enterprise, commercial and SMB? How many accounts did we have assigned in each? And how did we assign sales coverage to those accounts within the segments? And what we found is we had outgrown the processes we were using. We weren't aligned to best practices, and we weren't taking the best advantage of our sales capacity to our greatest opportunities. So we went through a process of evaluating all of our accounts by looking at their total addressable spend, what they spend on the solutions we sell, their propensity to buy Elastic and that took into account a number of variables and their historic spend. And we use that to place accounts in the right place. Now we've evolved this, and I'll share with you at a high level how we segment the market today, still enterprise, commercial, SMB, the top in enterprise. We've subsegmented. A small number of our largest potential customers are in strategic. That's where we have our densest sales coverage. The bulk of our enterprise accounts are in this enterprise block. And what we've done here, particularly in our large markets where we have density, we have reps and entire teams focused on 1 of 2 motions, either expanding with existing customers or landing new logos. We followed that same logic in commercial. You can see the commercial expand, commercial hunter segments. And then we built out what we call commercial general business. We literally moved thousands of accounts out of enterprise, out of commercial or out of the top 2 levels of commercial into general business. Not that these accounts aren't important, but they represent smaller opportunities. We wanted to align our coverage and our cost of that coverage to those opportunities, and we built out an inside sales team at lower cost. And this allowed us to do a couple of things. One, our enterprise and our expanded hunter commercial account executives now have dramatically less number of accounts, so they can focus on those accounts to go deeper in the case of expand. And for hunter and going after new logos, they focus on the accounts that have the greatest opportunity. And as I mentioned, we have territories and we have teams focused on 1 of those 2 motions, so we get really good at it. Before, our accounts had too many -- our account executives had a large number of accounts. They were a mix of installed base and white space accounts they weren't able to be proactive. And what we're seeing now is that we're in opportunities. We're winning deals in places with customers that we've never been before because our reps have more time to focus. In addition to this work, we've made strategic investments in the field and other areas to drive greater customer intimacy, to drive productivity of our account executives and to shorten our sales cycles. The customer architects listed here on the screen, these are our post-sales engineers that work with our customers to get the most value out of Elastic. They help our customers take advantage of the latest features like what you saw demonstrated here today. They also help our customers optimize their implementations to make sure they're running efficiently because we know if our customers are using Elastic efficiently and they're using our latest technology, they're going to be customers for life. We've made additional investments in specialists in the sales organization. For example, 18 months ago, we built out a team of Gen AI specialists to work with our account teams because we saw this opportunity exploding. We charged our sellers with going to their customers and finding out who's building Gen AI applications, what baked their databases are they testing? What were they trying to accomplish? And once they get to those decision makers, we bring in these specialists and having a huge impact. We've also expanded the specialist team for security. We've had for a long time, technical security specialists, but we added sales security specialists to help us get in front of more opportunities, get us into more at bats. You saw the technology that Santosh's team demonstrated. We win when customers evaluate Elastic. We're like -- we want them to go -- we want them to test us. We want to look at other competitors because we win. We're just going after more at that. We've built out a value engineering team that builds ROI models and executive messaging to help our sales teams close large transformational deals, oftentimes helping customers migrate off of legacy incumbents to Elastic. And then on the low end, we have a large number of low dollar renewals. We built out low-cost renewal managers to run a repeatable process for driving our renewal rates up. The other area I want to share with you that we've driven transformation is around our sales plays. We went from a fragmented model to 3 focused sales plays. We treat these like products. They're designed with intent. They're measured with rigor, they're scaled with discipline. Each of them has training for our sellers, content, collateral models to work when we engage with customers. The 3 plays are victor and vectors. This is our Gen AI play. The second is race to displays. This is where we're going after legacy incumbents. And the third is free to paid, just as described. Now this isn't a new motion for Elastic. We've converted free users to paid for a long time, but we've got more structured. And how we do that, we've given our sellers insights into who's using free Elastic, how they're using it. We're giving them tools so they can go to their customers to show the value of going to paid Elastic. Now these transformations that we've driven are driving results. I'm really happy with my team. I'm proud of my leaders, how they've leaned in. And what we're seeing is better performance. We're driving better consistency, predictability. We've had 4 straight quarters of strong sales results, which you've heard on our earnings announcements. And I'll also share with you that we've seen improved productivity. Last year, our productivity per AE per account executive was up high single digits after previously declining. And we saw meaningful improvement in sales efficiency, meaning that the more -- we're getting a better return on our investment in the sales organization. And because we have the right foundation and we now have an investable model, we've been thoughtfully and systematically adding sales capacity to drive our growth for the future. Some other signals of our success. We grew the number of accounts greater than $100,000 ACV by 14% last year, and we grew the average per customer in that cohort. We saw that continue in Q1. And I'm very excited to share what we've seen in growth of our $1 million-plus customers. We grew 27% last year. We continue to grow at that pace in Q1. Now all of that, I'm happy about. I'm excited about what we've accomplished, but I'm really excited about the future. And I'll show you why. Ash mentioned to you that over 50% of the Fortune 500 are paid Elastic customers. But when I widen the aperture a little wider at the Global 2000, it's only 42%. That means we have 58% to go and convert Elastic customers. And I know we can help them. And this is part of why we now have dedicated territories, dedicated teams focusing on getting better at landing to go after new logos. And if you just put that aside and look at our existing customers, what this shows you is that only 19% of them use us for more than one solution. But that 19% represents 75% of our sales-led ARR. We have a massive opportunity to expand in our existing customers. And that's why we have territories and entire teams focused on expand, going deeper with their customers. They have fewer accounts per AE to go deeper, learn about their problems, show them how we can solve their problems, go to the new buying centers to expand. Now I want to show you a customer journey with Elastic. This happens to be a U.S. headquartered retailer that has physical stores around the country, a large web presence, and they started with Elastic a few years ago with observability on self-managed. And you can see that their ARR for us was flat for 2 years, but then they chose us for search in the cloud and their ARR doubled. And shortly after that, we went through our segmentation exercise and the enterprise AE that covers this customer was able to spend more time with them, better understand what they're trying to accomplish, learn about their challenges. And we started helping them create the next-generation e-commerce experience, and they chose us for vector search and our Gen AI capability. They also started using our AI assistant and observability and their ARR doubled. We see opportunity for this to continue to grow as their new e-commerce platform goes into full production. It's an example of customers going from self-managed to cloud, 1 solution to 2 and the potential and opportunity we have when a customer is using us for regular search, keyword search going to vector search. I'll close with this. We've made a lot of improvements. I'm really proud of my team and where we are. We're operating at a high level, but we're continuing to drive improvements in the team. We're now investing in capacity for the future in order to capture the AI opportunity we have at hand. What I can tell you is it's a great time to be at Elastic. Thank you. I will now turn it over to our Chief Financial Officer, Navam.

Welcome, everyone. I'm Navam. Thank you, Mark. Great to see you today. You heard about Agent Builder. You heard about Streams. You heard about workflows. Ton of innovation we're driving at the company. It's truly a dynamic and exciting time to be here. I want to start off by summarizing before I get into the finance stuff, what you heard from my colleagues about the Elastic Advantage. So first and foremost, we are a company with a massive amount of platform innovation. Customers use us where data has gravity, be it on the self-managed side or the cloud side. And we are driving innovation in both. You've heard about that from my colleagues, Ken, Steve and Santosh, about the features and functionality we're driving into both cloud and self-managed platforms. Second, you just heard from Mark, our GTM strategy is gaining momentum. Mark talked about the revamped strategy that we have in place. It's been in place for over a year now. We are adding customers efficiently, and we are expanding them. Third, you heard from Ash about our unparalleled advantage in unstructured data, relevance and context engineering. This is our defensible moat. And because of this advantage, we were made for the Gen AI era, and we're ready to take on this Gen AI opportunity ahead of us. So in my section, I want to talk about how we turn these advantages into what you're all here for, revenue growth, attractive margins and growing free cash flow, all right? So before we do that, I'm going to run through this pretty quickly to baseline. I want to talk about the progress over the last 5 years. We have built a solid base of revenue of over $1.5 billion over the last 12 months of trailing 12 months. This revenue comes from -- and at the same time, we've been increasing our revenue in double digits. This revenue comes from 3 sources. Bottom of the graph is our sales-led subscription revenue. Middle is the monthly cloud business, which is self-serve. And the top of the graph is our services business. If you look at our professional services business, this is in support of our sales-led subscription revenue. We sell it to help our sales-led subscription customers grow. It's about lowering barriers to adoption. It's about shortening time to value, and it supports our sales-led business, right? The monthly cloud business consists mostly of SMB customers. In the past, this grew with high SMB spend. But around 2022, you saw that SMB spend was moderating, and that's the dynamics we're facing today. So over the past few years, we've matured as a company, and we've focused mainly on larger strategic accounts and high propensity commercial accounts, as Mark talked about. And we do -- we serve this segment through a sales-led motion. So this sales-led motion leads to sales-led subscription revenue, and let's take a look at that in more detail. We've maintained a very strong growth rate in sales-led subscription revenue. In 2025, we grew 20% and this sales-led subscription revenue is becoming a bigger and bigger percentage of our total revenue. In 2025, this revenue line reached 81% of our total. This is the segment we spend most of our time and effort on and our investment in. We tell our sales teams and we incentivize our sales teams to go get customers in this segment, either in self-managed or cloud, and we incentivize them to meet our customers where they are. So as you think about our business and as we think about our business, sales-led subscription revenue is the primary barometer of how we measure our success, not just cloud, not just self-managed, sales-led subscription revenue in aggregate, both of them is how we measure success for us. All right. So we delivered these strong revenue lines along with rapidly advancing our operating profit and free cash flow margins. So on the graph on the left, you'll see our non-GAAP operating profit margins. You'll see that we reached breakeven in 2022. Since that point, consistently adding operating profit ending at 15% in 2025. FCF follows a similar trajectory, right? Shown in the chart on the right, we've reached 19% in adjusted free cash flow in 2025. What's here -- what you're seeing here is basically the inherent leverage in our model. We have high product gross margins, and we have leverage on our sales and marketing and G&A line items, and that's causing us to be able to deliver these strong operating profit and strong free cash flow lines. We expect this to continue. With improving product profit margins, with improving free cash flow margins and strong revenue growth, we're continuing to see increases and progress towards the Rule of 40. FY 2023, we had Rule of 40 of 29%, and we've progressively grown that to 36% by FY 2025. We're continuing to make progress there. So Rule of 40 is how we think about balancing how we invest in growth while at the same time, delivering free cash flow. So that's the state of the business over the past 5 years, very strong metrics. A few slides ago, I talked about the importance of sales-led subscription revenue. And these next few slides and sections is about diving deeper into that revenue line item, right? I want to talk about sales-led subscription momentum and how we can support this and make it a durable business. Let's take a look at some data, and Mark mentioned this. Our GTM strategy is anchored on our highest value customers. While at the same time, our product team is delivering continuous innovation, leading the market in search observability and security. And this tech is resonating with these customers and more and more customers are joining our $100,000 customer ranks and $1 million customer ranks. The $100,000 customer rank that you see here on the left accounts for the majority of our sales-led revenue. It's 87% of the total. And these customer counts are consistently increasing. And at the same time, if you look at our average customer value, that's growing over time as well, right? So let's look at how we are able to drive this growing average customer value on the left-hand side. We have a powerful model of landing new customers and expanding them, growing our $100,000 and $1 million ranks. New customers in the beginning drive a smaller amount of ARR, but that ARR compounds over time with expansion. Our platform basically is meant to adapt and scale with customers bringing in data, helping them become more efficient. And this includes some of the largest customers in the world. And the flexibility and scalability of our platform is the key differentiator for these customers and the reason we succeed with these customers. So customer expansion is driven by them bringing in more and more data into our platform, bringing more -- creating more workloads and then upgrading to higher subscription tiers to get premium features and then also adopting multiple solutions as they go deeper into our platform. And all of these dynamics result in a high sales-led net retention rate. In Q1, we saw a sales-led net retention rate of 113% TTM. And this was driven by the strong expansion that I talked about and the stable gross retention that I talked about -- and the stable gross retention. So we've built a durable customer base, and there is great expansion opportunity from both new customers and existing customers. And we have still a long way to go. From the new customer side, when you look at the G2K customer count that Mark showed you this graph before, we still have a lot of white space. 58% of the G2K customer base still remain to become Elastic customers. That's a lot of white space for us to go get. The existing customer side has a lot of opportunity as well. On the left-hand side, I think you've seen this graph before. Mark talked about this. 19% of our customers have more than 1 solution, 2 solutions or 3 solutions, and they contribute to 75% of our sales-led ARR. We see much higher ARR per customer after they adopt multiple solutions. On the right-hand side graph, you'll see that the median for a 3-solution customer is 12x that of a single solution customer. This is our opportunity. As we make inroads with existing customers, again, we -- as I said, we expect workloads to grow and Ken highlighted how we've architected our solution to make things more efficient for our customers and incentivize them to bring more into our system and bring more into our platform and also adopt more and more of our solutions. And this is our repeatable playbook for years to come. As I said, there's a lot more growth left for us. So that's the expansion dynamics of our sales-led subscription revenue. I want to talk about what gives us confidence in the future. And I want to take a deeper look at our customer cohorts. So what are you seeing here? This chart displays our sales-led subscription revenue customer cohorts from FY '13 to FY 2025. Each band and color shade represents a cohort of customers based on when they were first and elastic customer of ours, right? And if we draw a dotted line from the end of FY 2020, you'll get to see that there's a good balance from -- of growth from long-standing customers and newer customers. Customers before 2020 contribute to -- there's almost a 60-40 split, contributed to 61% of our recent growth and customers since 2020 represented 49%. This is a good balanced growth dynamic from our customer cohorts. The cohort data also revealed 3 very important takeaways, which I'll go into, related to the durability of our cohorts, the resiliency of our cohorts and the Gen AI tailwinds that we're beginning to see over the past couple of years. Let's dive a little bit more next into these cohort data and talk about these 3 takeaways. So the first takeaway is that these cohorts show remarkable durability. This is a chart of our most mature cohorts. Even these cohorts are still expanding. Though some of these customers have been with us a while, and normally, you would expect to see more stable ARR rates from these customers, they're similar to our new customers. They're bringing in more data, and they are very active. The FY '13 through '20 customer group -- as a group grew their sales-led ARR by 10% last year. Even these customers continue to be remarkably active just like our new customers. And this expansion durability supports our growth algorithm. The second takeaway is that we see remarkable resiliency from our customer base. You all remember calendar 2022. This was a challenging time for software. I was there as well. Similar to most of the industry, everybody faced budget constraints and everyone was forced to prioritize optimization on their spend. And during that time, our customers were no different. They face the same dynamics. But we worked with our customers to reduce their spend and encourage them to adopt new features of ours like frozen tier storage, for example, which will help them become more efficient. And the aggregate result of all that is a slower expansion rate in that period. But during that period, we also did not see an elevated churn rate. We saw a slower expansion rate, but we didn't see quite an elevated churn rate. Once those headwinds passed, the growth trajectory continued. And I think that was a great data point that demonstrates, one, how resilient our customers are; and two, how important our customers think our platform is, how much value they see in our platform and the amount of innovation we put into our platform, they basically are making our software essential technology in their workflows, and this is great news. The third takeaway relates to the tailwinds we are beginning to see with Gen AI. Now this graph shows the year 1 to year 2 ARR expansion of each cohort. This is the first year expansion of each cohort over the past 6 years. FY 2024 is showing a greater year 1 to year 2 growth than any of the cohorts in the recent past. If you draw that dotted line that you -- or you see the dotted line over there, that represents the '19 through FY '23 cohort average. That's 20%. FY '24 is twice that size, 42%. This is a significant data point because that's the first cohort that has seen a full year of Gen AI impact in their first year growth. In fact, if you look at this FY '24 cohort in a little bit more detail, you'll see that 11% of this cohort adopted some kind of GenAI functionality. But this minority grew more than -- or contributed to more than 60% of the cohort's net expansion rate. Right? This is an excellent outcome. And this is the data point that we have of the accelerated revenue growth we see when customers adopt GenAI use cases. And when you look at it more broadly and you look at our aggregate ARR in FY '25, split it between customers who use Gen AI and split it between customers who do not Gen AI, there is a clear difference in expansion rate that comes up. Our customers have a 6% -- or we see a 6% tailwind from our customers if they are using Gen AI. Gen AI is driving acceleration for us right now. So while we're seeing all these promising results related to Gen AI, it's important to remember, we're still in the very early innings of customer adoption. We've seen strong adoption among our $100,000 customers with 20% -- more than 20% using Gen AI features. And we've made strong progress moving from 4% to 21%. But there's still a fair amount of room for our customers to start using Gen AI and grow into the ranks of the $100,000 category. And even among the customers who are in the $100,000 category, they're still in their infancy in the number of apps that are GenAI enabled, and there's going to be more and more apps that they build, which will then get them further into their journey into Gen AI and create more maturity and more tailwind for us. All right. So now I've shown you a lot of our cohort data, our expansion data, and you've digested how our sales-led customer behavior is. You've seen about -- I've talked about how durable things are and how resilient our customer base is and how we're starting to benefit from the Gen AI functionality -- from the Gen AI tailwinds. Next, and I know this is something you've been looking for. How do you put this all together? How do you think about this from a midterm framework? And let's go there next. So we view sales-led subscription revenue, both self-managed and cloud to be the midterm growth engine of our company. This growth comes from 2 components. The first component is the continued expansion -- sorry, continued execution of our land and expand model. on the core search, observability security use cases on the customer base that we have and the new customers that we're going to add in the G2K. We discussed the durability of our customers. We discussed the white space we have in the remaining customers where we have to get. We expect a 15% plus growth rate by the medium term, not counting the benefits of Gen AI for our sales-led subscription revenue. The second component, and this is still in the early stages, is related to the continued penetration of generative AI among our sales-led customers, both in terms of the number of $100,000 customers we can get as well as the penetration and maturity into those $100,000 customer accounts. We expect a 5% plus tailwind gradually as Gen AI increases among our customer base. So we expect this core execution plus tailwind to result in a 20% plus target growth rate in the medium term. As always, we will operate with operating expense discipline, and we intend the total revenue and adjusted free cash flow margin to result in a 40-plus Rule of 40 target. All right. Let's take a look at this in a little bit more detail. As I mentioned, sales-led subscription revenue target growth rate is 20% plus from those 2 components of the base growth rate and the Gen AI tailwinds. That revenue line, the sales-led subscription revenue line is expected to be 85% to 90% of our total revenue. Both non-GAAP operating margin and adjusted free cash flow is expected to exceed 20% plus, driven by our disciplined OpEx and operating leverage -- disciplined OpEx management and operating leverage. And finally, we expect to maintain net dilution rate below 2.5% as we remain disciplined in adding headcount, while at the same time, being competitive in the talent market. Okay. So when you look at our operating margin improvements in the past and you'll see the leverage that we have, which gives us confidence in reaching this 20%-plus target. FY 2026, I talked to you about was an investment year for us. After those catch-up investments, we expect to decrease sales and marketing and G&A expenses as a percentage of total and get our operating margins above 20% through the combination of our disciplined OpEx on sales and marketing and G&A and also our gross margin improvements. Our product gross margins are already above 80%. You can see that in our subscription revenue line. That's grown, but there's more growth to have -- or there's more increases to have in our subscription margin line. So the combination of subscription margins, which results in higher gross margins and leverage in our sales and marketing and G&A will allow us to get to 20% plus. You can see the progress we've made in sales and marketing and G&A over the past 5 years as well. So we're confident about hitting this 20% plus target. Okay. So now given that we're further along in the year, and you've all digested our medium-term framework now, I'd like to touch on our current fiscal year in the context of aligning to this framework. In the beginning of the fiscal year, we detailed there were certain macro conditions that were emerging. It was very uncertain. We didn't know what the impact of consumption and commitment plans were, what patterns could be. We built some of that into our guidance that we provided in the beginning of the year, and we essentially carried most of that through in the first quarter -- after the first quarter. Since then, now that we're further along in the year and further along in the quarter, we've gained greater visibility into the demand environment. And we feel good about the commitments we're seeing. So there's still headwinds like -- you all read the news, the government shut down, that's still ongoing. But we believe we're better positioned than what we had originally anticipated. So as such, I'm updating my second quarter and full year guidance to the following. You may remember that in Q2, we guided $415 million to $417 million. At the time, we had raised this by -- we were $6 million above consensus and a 14% year-over-year growth rate and '26 was $1.679 billion to $1.689 billion, also a 14% rate with a 16% op margin target. I'm updating this now to a Q2 total revenue target or guidance of $417 million to $419 million, an additional $2 million above the $6 million that we've already increased. And also in FY '26, we're updating the range from $1.697 billion to $1.703 billion, which is a 15% year-over-year growth rate. In addition to this, we expect our non-GAAP operating margins in FY '26 to be 0.25 point higher at 16.25%. Okay. So moving on to discuss our balance sheet. We have a strong balance sheet driven by a growing cash balance that's fed by a growing free cash flow line. With our increasing amounts of cash, we have three primary capital allocation priorities. Our first priority is to continue to invest in the business and our platform to position us to win in GenAI. It's a massive GenAI opportunity ahead of us, and we need to drive durable growth. The second priority, and we've -- this is up because of the GenAI announcement we just did, we will evaluate and pursue acquisitions that further our strategy, similar to the ones that we just did. We'll do so with strict financial discipline. These traditionally have been tech and talent tuck-ins to enhance our search platform or observability and security platform. So this will be our second priority with -- second capital allocation priority. And finally, this is new. We will begin returning capital to shareholders through a share repurchase program in order to partially offset dilution. And we will do ongoing share repurchases unless more attractive acquisition opportunities become available to us. So as part of this acquisition -- capital allocation strategy, I'm pleased to announce that the Board has authorized $0.5 billion for our initial program, and we expect to use more than 50% of the authorized amount in fiscal 2026. And going forward, we expect to return 50% of our free cash flow, as I mentioned, through share repurchases. And again, unless more attractive acquisition opportunities arise that require us to use some more cash. Okay. So to close, we are incredibly excited about the opportunity ahead. We have a business supported by a strong land-and-expand motion. Generative AI presents a dynamic and exciting opportunity for us here at Elastic and our platform was made for this moment. And finally, our model supports a tremendous leverage, which allow us to grow revenue and grow our margins, expanding to rule of 40 and above. Thank you very much for being here. I want to welcome back Eric, back to the stage.

Okay. Thank you, everyone. We're going to have a Q&A session in a second. They're just going to set up the chairs. We're going to get all the people who presented back on stage. [Operator Instructions] With that, I think we've almost got the chairs on stage. I would call the team to come back up, please. These guys work fast. And by the way, [ Claire and Chantelle ] come bring microphones. Go ahead. Ittai, do you want to go first?

Ittai Kidron from Oppenheimer and thanks for the presentation today, very helpful. A clarification for you, Navam and a question for you, Ash -- a clarification for you, Navam. When you talked about the GenAI contribution to your long-term model, 5 points, you said gradually growing into it. Does that mean that in the early years here, it will not be 5%, meaning your targeted growth is under 20%. And in time, will get to 20% just on that? And then Ash, you made a very compelling presentation to the -- you and your entire team around the technology, the differentiation, and I always come away very impressed with the technology. But when I look at the competitive space, whether it be a Datadog or a Dynatrace or the security companies like a CrowdStrike, they are all growing north of 20% for quite some time. So help me understand what is it in the model that is so difficult in translating the technology into reality of dollars. And I understand that there's a big GenAI opportunity ahead. So you can always say, well, talk to me two years from now. But a lot of the advantages have already existed for the last three, four years. What is it that's been missing in making that and how are you addressing that gap -- perceived gap between the capability and the reality of what the numbers are?

Why don't I take the question -- the second question first, and then maybe that will lead nicely for Navam to answer the first. So the -- look, the most important thing to understand is what is the role that customers use us for in an enterprise. And it's always been around unstructured data. Like that's been the primary problem that Elastic was always used to solve. We got into search first, and that was the core use case. Search in the early days just did not have the same market size and opportunity as maybe structured data and the opportunities around it or even observability and security. That led us to go into other areas where unstructured data was a core problem. And so we got into observability, we got into security, but we were methodically building out all of the capabilities that we needed to be a very strong player, be a very compelling player, starting with our core strengths, starting with log analytics for observability and starting with SIEM for security. So we've been on this journey. But keep in mind that when it came to search, that was, in some ways, the smallest part of the business. What has changed for us in every way possible is that unstructured data has become more important than ever before. Our core search business is seeing more interest than we've ever seen in the past. That market, that solution area is today our fastest-growing solution area, and that wasn't the case in the past. That's number one. Second, even when it came to observability and security in the past, although we had the best back-end data store to deal with observability signals, to deal with security signals, we didn't have anything that we could completely and conclusively come forward with and say, this is why we can solve the problem better, faster in a more efficient manner. AI has been that unlock even in observability, even in security. When you see the demos today, if you paid attention to the demos today, what you saw was stuff that matters to security and SRE practitioners that was not possible before and more importantly, that others aren't able to do even today. All of that comes from that really amazing advantage that we have from AI. Now to me, this is definitely an opportunity for us to continue to improve upon our growth rate, accelerate everything that we are doing and get well past the 20% mark. That's the goal. That's the history of how we got here. That's the reason why I'm so excited because effectively, what was always our core strength, Ittai, has now become what the market cares most about.

Yes, let me take that first question, Ittai. I'm glad you asked it. So here's the way you should think about our model. And I hope I gave you enough data on the cohorts and the expansion rates and giving you enough cuts to show that, look, 15% is a baseline. We expect that to be there. Just based on continued core execution on things we have without anything coming from the background and helping us along. That's just the core execution. So that's something we want to be solid about, right? And then beyond that, there's the tailwind of -- so it's 15% plus and then there's 5% plus. AI is just a very dynamic market, so it's going to be progressive as we go into the midterm, which midterm for me is from the end of the year, roughly 3-ish years is the way I think about it. So this year, we've guided to 15% for the full year. Sales led subscription revenue is about -- generally about 2 points higher than that, right? So from there, is the tailwind growing to a 20%-plus target rate which the framework suggests in the midterm period. So that's how we think about sort of a growing tailwind on the sales-led line, which will end in the 20% plus rate in the framework model that I just talked about. But just to be clear, those AI tailwinds are happening now. We're confident about those AI tailwinds continuing. Just the timing, it's a dynamic market. So the timing of exactly quarter-by-quarter mapping it out is a difficult thing to do.

Koji Ikeda from Bank of America. Thanks for doing this, great presentation, guys. I wanted to ask a question may be related to Ittai's question about budget unlock with the customers. It sounds like the generative AI opportunity is tremendous for you guys. Technology is fantastic. I mean you talk with any customer out there. Elastic is just very, very well known in the end market. And so what does it take for the customers to spend more with you guys? Is it just more strategic shots on goal? I mean, Mr. Dodds, this question may be directed mostly towards you about what are you doing within the sales organization to really drive more spend over to you guys?

Yes. Maybe I'll touch upon it and definitely want Mark to elaborate. But in a lot of ways, Koji, as you said, the market opportunity has always been there. Like part of it has been just the opportunity around AI, making search more and more important, which has been a big part of why you see this enthusiasm. You see the cohort data clearly showing how AI is contributing. The other part of this is when we looked at our segmentation model in the past, there was definite inefficiency in the fact that our sellers weren't really specializing. Our sellers were hybrid sellers, if you will. And a lot of the work, a lot of the hard work that we went through at the beginning of FY '25 in terms of the work that Mark did was to make sure that we could go deeper, we could go in a more intentional way into accounts, into enterprise accounts to capture a bigger share of the wallet. These deals take time. But when you're able to convert a customer over from an incumbent SIEM platform and consolidate them into Elastic, those deals tend to be very big. One example was the GSA announcement that we made in the public sector, I think it was a quarter ago.

It was in June.

In June. That is the perfect kind of example of the kinds of opportunities we are now able to go after with the kind of work that Mark's team has done. But Mark?

Yes, I think you said it well. But as I mentioned earlier, we've reduced the number of accounts per AE so that they can go deeper with those customers and cross-sell into new buying centers. And we're seeing that gaining traction and building pipeline and getting us into new business. We're also focusing on net new logos with territories and entire teams focused on that. And then as I mentioned, we're adding more sales capacity. We're getting more at bats to grow our business.

Thanks again for doing the Analyst Day. Mike Cikos with Needham. I appreciate all the information on the cohorts, the financial model. On the 15 points baseline that we're talking to, it would be helpful to get a better understanding of the different -- the use cases that are giving you guys that confidence, right? I know we're bulled up on search. We're seeing it at the conference here, but I think it was probably 5, maybe 6 consecutive quarters where we were talking about search AI budgets are accelerating. And we're now at least a quarter or two since we've gotten that last data point we're seeing the growth rate. So can you just help explain that dynamic? And then the second, more of a tech question here. But with the Elastic inference service, I understand that you guys have the models now being served up on the GPUs, was it on the customer to actually -- it was on them to get the chips and then put the model on the chips? Like what did the customer have to do for that inferencing angle that the EIS offering is now unlocking that you guys are announcing today?

[indiscernible] first and then I'll do.

Why don't I ask Ken to address the second one.

Yes, I'll answer the second question, which is previously, if a customer wanted to use our embedding models or Reranker, they would run them on an ML node within our stack, which would be on a CPU-based architecture. If they wanted to use GPUs, they could, but they would have to direct it towards some other service that they -- either they would run or somewhere else. With the inference service, we are providing a fully managed API-accessible inferencing capability that's running on GPUs. It's initially supporting ELSER and embedding models. We're going to expand that to cover rerankers and the [ GenAI ] models as well. So we'll be expanding the models that we host on the inference service, and it will all be on GPUs.

I'll take that first question you asked. So first of all, I think you've got to think about the core platform growth. There's more and more data coming into the platform. And you've seen the data behind the net retention rates that are 113% and stable. There is data behind each of the cohorts over a long period of time that support continuing that teens growth rate without much tailwinds, right? So that's the first point I'd make that outside of search acceleration, outside of all the unlocks that you're seeing on search. Just the core platform increase is going to be sustained at 15% plus, just based on the net retention rates and the cohort data that we see. In addition to that, just when you think about all the great platform announcements or the search and observability announcements that we made today, that's driving differentiation to allow us to take more market share on that TAM that's still expanding. So observability and security businesses are still very large TAMs and there's still a long way to go there. And I think that our product differentiator now -- differentiation now just grew. So that's what gives us confidence on that 15% plus. It's just driven by that core platform and the amount of data that's coming into the platform and supported by all the cohort data and the expansion data that you just saw, along with all the product announcements that we talked about in security and observability outside of search.

Sanjit?

My congrats on all the great data and from the presentation as well. Ash, I got a really clear view today of where the company is going, how they're going to win, why they're going to win. One of the most popular questions I get from investors is why did growth gets so low to begin with. And Navam, in your presentation, I think, speaks to part of that, which is maybe around some of the optimization activity. There are some go-to-market changes you guys are making, is there anything else that could explain the deceleration in growth that we've seen over the last couple of years? Was there headwinds in your security business or your observability business that you're now working through? Because I think if we put those pieces together, it makes much easier to underwrite the kind of where we're going on a 15% plus basis in GenAI. So any sort of comments there and then I had a product...

Yes, let me address that and then ask Navam to also jump on to it. Fundamentally, I think there was a chart. I can't remember the exact slide number, but there was a chart that, now I'm sure, that broke down the three components within our revenue. So there's sales-led subscription revenue, there is the monthly cloud business and then there is services, right? And if you look carefully at those three components, like they really tell the story, because monthly cloud, which is our SMB business, was a significant significantly larger component and faster growing component of the business if you went back 4, 5 years. And that clearly in the last 3 years has been roughly flat. And that's not something that just we are seeing, but across the board, like we see SMB spending hasn't really come back the way enterprise and other spending has come back. Services, professional services, we have been very clear about that this is not something that we believe is something that we want to grow at the same rate of the business. Like that's not -- it's not the most strategic part of what we are trying to do. We are a platform company. We're a product platform company, and the subscription revenue growth that we are trying to drive through the sales-led motion, that's what we care most about. So services is just an enabler. So what we really have been laser-focused on in the last three years is making sure that we get that growing and thriving and continuing to accelerate. That's what the effort has been in. And that's the reason why we had the -- so apart from it, by solution mix or any other cut, we don't see any issues in terms of competing, winning in the market, continuing to take share. We feel incredibly confident about observability. We feel incredibly confident about security. Matter of fact, we've been beneficiaries when it comes to taking share from incumbents. It is this dynamic of monthly and the rest of the business. And if you take out the monthly, then that's what we are really focused on. Internally, that's the thing that I care about. And over time, that's going to be a bigger and bigger percentage of our revenue and the rest is frankly, not going to be what should matter to investors.

No, I mean nothing more to add. I think the intent of providing those two charts is to give you the data behind it. Ash summarized it clearly, which is the changes in the SMB dynamic, which is partially -- not partially, the main reason for the deceleration that you're seeing in those years. But the second chart, which is showing the durability of our sales and subscription revenue growth rate for multiple years. I mean, talked about multiple quarters in the last earnings call, but you look back, it's been many, many years of strong growth. And what we're saying now is that we maintain that durability 15% plus on the baseline just -- with bread and butter stuff that we're doing now without much GenAI and then the GenAI tailwinds which we're seeing now get us to beyond that, and there's even the potential to breach 20%. The GenAI tailwinds of 5% are just the map we see on the cohorts that we compare, but there's some data that has a higher tailwind than 5%.

Understood. On the security business kind of following the Ittai's question, investors underwriting like market share gains, displacements. I mean, you guys are a product company, security becoming more data-driven, compliant to all that, but are you -- and this probably might be a Mark question. Do you feel like you have the relationships with the security ecosystem to drive what are pretty formidable competitors in the SIEM space in particular. So just get a little -- get your perspective on that.

Yes, I'll comment and maybe Santosh, if you want to add. But what we see in the market is that many customers are ready to and want to replace their legacy security platforms. And they're evaluating Elastic, and they put us through our paces. And we love that because when customers evaluate us in detail and they see the innovation that we're driving, they see attack discovery, auto import we're winning a high percentage of time and we're winning large deals. That's why we have a program around race to displace. But we're finding a lot of success there, and we're excited about that opportunity.

And could you just say your name and firm when you're on the microphone?

Sure. Rob Owens from Piper. Ash, you do have a history in the security universe if we go back almost a decade now at this point.

I'm old.

And as you know, it's never been an efficacy game relative to endpoint. And while you're showing well in the scores, a lot of the independent scoring. Is EDR critical to your success as we think about that convergence of what was XDR, but next-generation SIEM in your view?

In my view, it's going to be another vector for growth, Rob. I think that the great thing about endpoint telemetry is. It's voluminous, and you can't get away from it. You have to have something on your endpoints to make sure that you have that threat vector covered. And exactly for that reason, the most important thing that we solved a few years ago and we got -- solved it right, was to make sure that we made it easy for people to get our endpoints deployed, our agents deployed on their end points. The way we did that is by basically incorporating all of that functionality directly into the same agent that does the SIEM collection, that does the collection of data for SIEM. So you're already deploying that agent. And once you have it, then we go to them and say, "Hey, it's the easy button, just turn it on." Now you're absolutely right that efficacy isn't the only thing that matters, that there is more that's required, like there is people need to put you through their paces. People need to see that you can have the one single dashboard that they can use for doing all their detections and remediation and so on. Having that data-centric approach and being the SIEM gives us the opportunity to start to talk to the SOC and basically say, you're already trusting us for doing all your detections across all threat vectors not just endpoint but across everything because that's what happens in the SIEM. Everything ends up in a SIEM eventually. If you are trusting us for that, why don't you try out the endpoint functionality. So it's been this expand play and all through this time, we have been making our endpoint product better and better. adding not just efficacy capabilities but also management capabilities. How do we easily deploy, how do we do rolling upgrades, all of those things that for other vendors have even caused blue screens of death, as you know. Those things are important to get right, and we've been working hard on them. So over time, I expect this to be a bigger and bigger and more and more interesting area for us for expansion. We're just getting started on it.

Great. Thank you, Howard Ma with Guggenheim Securities, and thanks for a very informative presentation and as well as the balanced profit and growth framework, which is what I want to ask about. So if you look at the top line, going from about 15% today to -- assuming you achieve the aspirational target of 20-plus percent, that's quite positive. But if you look at the free cash flow margin. I know you didn't -- you guys didn't give a discrete guide for this year, but I think it's -- you might have said Navam, on the earnings call, it's like high teens. So going from high teens to 20% is not that much expansion. So I wonder, is that because of conservatism or are you building in investments, specifically on the monthly [ Pago ] side because you have to drive the product-led motion still. And educating your customers that Elastic is the best unified data platform for unstructured data and driving consolidation versus a solid approach today. I would say really, those are the kind of the barriers today. So how much -- the question is how much investments are needed to overcome those potential challenges.

Before Navam answers the question on the free cash flow model and so on. I just want to clarify the inherent leverage in the model. And I want to make sure that people understand that one of the advantages of having a single platform on which we have search, observability, security, all of these solutions built is that our cost of engineering and our cost of building this platform is incredibly efficient. Like we don't have to build three different management consoles. We don't have to build three different ways to manage users and user profiles and so on. There is tremendous leverage that we get by having one single platform. And that applies also to our monthly cloud business. So it is important to understand that our monthly cloud business isn't a expense drag on the business, right? So it is something that tempers the top line because it's been flat. But it doesn't affect our cost profile in any negative way. But let me -- with that, let me just...

Yes. Let me kind of start first on the OpEx side, just to build on what Ash said. I think we broke out the components of what we expect R&D, sales and marketing to G&A to do in the midterm model on the operating expense side, and we expect strong investments into the R&D side, keep that stable. And then the sales and marketing line, I think we've shown the amount of leverage we can drive. Now 2026, we are investing in capacity. I think we're doing a really good job on the productivity improvements. We're seeing all the good things that are happening in the field. We want to double down on that and build some capacity there. So '26 is a build year. But after the build year in '26, you're going to see the progressive improvements in sales and marketing as a percentage of revenue, similar to the G&A side as a percentage of revenue. So we should think of this as a build year and then after that, we're going to go back in the margin trajectory. And remember what I talked about capital allocation. We still want to invest to win the market and make sure that we're positioning ourselves for winning. But at the same time, we're going to be very disciplined, right? On the free cash flow side, the model is to get to 40% plus under the target scenario of 15-plus -- upwards of 15 and upwards of 5. In any scenario, we were going to get to rule of 40 plus. But obviously, the growth components of the 20% plus are those two tailwind plus baseline growth rates. This year, our FCF margin is expected to be roughly the same as last year.

Yes, Tyler Radke from Citi. Thanks for doing this. I thought sort of the framing of Elastic is kind of the leading unstructured data platform definitely resonated across all the different presentations. I guess my question is, as we think about the ways in which AI are changing the way that you can kind of productize that, I'd love to get your thoughts. I mean this agent builder that you demoed seemed pretty compelling. You are seeing a lot of newer vendors almost offer out-of-the-box solutions, whether it's on the search side, the gleams of the world, you're seeing vibe coding products probably leverage a lot of the core elastic functionality. But how do you sort of think about all the trends in the developer space around automation, AI, driving more usage of Elastic over time? What are you doing from a product perspective? And then second question for Navam. You raised guide like 6 weeks after you reported, like what have you seen over the last 6 weeks? I mean, was this just strength on consumption? Was it bookings on the federal side? Any elaboration on that? What's driving that confidence?

Maybe let me just touch upon the first one in terms of monetization. So at the end of the day, our fundamental model for monetization is consumption, as you know, Tyler. And so everything that we do is designed to both give value to our customers with what they are trying to achieve and at the same time, drive consumption on the platform. And that is effectively how we are going to grow. So all the announcements that you heard of today, whether it's the Elastic influence service, the new models from Jina that will become part of that influence service will also be offered. All of those are compute intensive. Our use as a vector database when somebody is building the retrieval system for context engineering to build any kind of agent that is a significant contributor to consumption. Agent builder. I mean the whole -- the way you should think about agent builder is it's going to fast track the process by which somebody actually builds these kinds of end applications, right? That's the key. So yes, we don't have a business UI, but that's because, at the end of the day, the people who work best and fastest with Elastic are developers. And unlike some of these other companies that you mentioned, we have a huge mind share with the developer community, tapping into that developer community, which ends up being a massive community and is building applications that are used by enterprises by every user within the enterprise, we feel it's a much more efficient way and a much faster way to really get penetration. But agent builder, what it lets us do is it fast tracks the approach for an end user to actually build these kinds of applications. And that is key. I don't know if you want to add anything to that?

Just to expand on one thing you said. Our approach has always been to allow people to drop down to the platform and drop down to code and have the flexibility to do whatever they want. And with a lot of the other solutions out there, if you can't do what you want, you're stuck. Like if you want to go customize this, you can't. If it doesn't support in the product, you can't do it. We never keep our developers from being stuck. Like they can always drop down. They can always customize. We try to make it very easy to get started by providing an abstraction. You saw an agent builder like we created an agent automatically by default, we create some tools automatically by default, you can go in, you can customize, you can extend. That's been in our ethos, like something we do. We also approach this from a data point of view. Unlike anyone else -- like we start with the data, and we're trying to give you access to building tools and building agents on top of your data. So we look at this as, you want to chat with your data. You want to build an agent on top of your data. You have private data, how do you expose that to an agent? And how do you expose that to an LLM? That's the point of view we always take. Others tend to start with the hosting platform or something else. We start with the data and try to figure out how to help a customer expose that data and build generative AI applications on top of that data.

Yes. On the guidance side, commitments are strong, and that's what gave us confidence to raise the year. So that's the first thing. And we're further along in the quarter. So we want to give you an update on the quarter as well. And when you think about the government shutdown side, I think there'll be obviously no business conducted in October since there's no one there for the federal government. But overall, the government is going to open up at some point and our products are positioned very well. Once that happens, in fact, it was. We had several good contracts before the government shut down as well. So we're not worried about the U.S. public sector business over the medium term. It's just that October is obviously going to be impacted by the government shutdown, but overall commitments are going very strong. So that's the reason to do the update.

And we're going to do one last question. It's going to be Raimo.

Thanks for squeezing me in. No pressure on the question, quality, I guess. Thank you and thanks from me as well, a great event. Maybe one for Mark as well to get him back on the -- if you look at -- as a sales leader, if you look at the number of customers that only have one product compared to where it should be, that's kind of huge upside, and the question is like why is that number so low? And I don't - I'm sure you guys looked at it. Is that -- is it kind of the wrong customer? Or is there a lot more you can do there? Because that feels like way too low as a kind of ratio.

Yes. It's a great question. We believe there's a lot of upside there. And we looked at how we were covering our customers in the past. First of all, we weren't aligning our sales capacity to the largest opportunities. We had territories that had way too many accounts, a combination of existing accounts and white space accounts. And the sellers, didn't have the time to focus, get deeper with customers and focus on going to the next solution within the customer. That's why we made a lot of the changes that we made, and we're seeing progress on that already. But we see that as tremendous upside going forward.

That was the last question. All I would say is, hopefully, some of you or if not most of you had the opportunity to come earlier during the day and see the complete presentations. But this event, our ElasticON event is always something that we care a lot about because this is one of the greatest and best opportunities for our customers to learn from us, but also from each other. You had the opportunity to participate in the Financial Analyst Day. And hopefully, you were able to get some energy and some ability to talk to customers who are here, get more insights into how they are adopting our platform, hear about some of their success stories with us, hear about why they are excited about what we are doing. We are really, really excited about what the future holds for us. Just in terms of the business overall, the commitments that we are seeing from customers, the demand that we are seeing in the market, the opportunity to take more share as we consolidate onto our platform, the customer -- the needs that customers have around observability, security and so on. And then most importantly, how we can differentiate with AI, how we can capture this wave. I am very confident that AI is going to be the dominant technology for at least the next several decades. And if that's the case, then you have to imagine that more and more applications are going to be built on this LLM based paradigm. We want to be the context engineering platform that every single one of those applications uses. That's our mission. That's our vision, and that's why we are so excited about the future. Thank you again. I believe there are -- there's a cocktail hour for the entire event. You are all very welcome to join, and thank you very much.