Good afternoon, everyone. It's Blair Abernethy, Software Analyst with Rosenblatt. Thanks for joining us. Today, we're happy to have Eric Prengel with us, who's the Global Vice President of Finance for Elastic. We're going to spend the next 45 minutes walking through Elastic's current business and how they're positioning themselves for this rapidly changing AI future that we have entered into in the last couple of years. Welcome, Eric.
Thanks for having me, Blair. Really appreciate you having me and everybody who's joining. Looking forward to the conversation.
Listen, maybe before I ask the first question, I just want to suggest to anyone who might have a question for me. Actually, if you could just email it to me at babernethy@rblt.com, and I will pull up and filter that into our conversation. That would be appreciated. Eric, why don't we just start with at a high level, some context for people that may not have looked at Elastic in the recent past. Just give an overview of sort of where the business is at today, the market, the problems that you guys are addressing, and then a little bit about your background as well.
Why don't I start with the business, and I'll end with my background, and if I forget anything, just double-click on anything, please ask me. Elastic is the world's leading data platform, which is specifically designed to index, store, and analyze massive volumes of messy, unstructured data. That's what Shay Banon originally created it for. What has happened over time is it's been evolved to be used in three core use cases. The first one is search, and historically, Elastic was something that you could build search applications on top of. Search has turned into AI pretty quickly. The AI has kind of been used as a search problem. Now Elastic is increasingly a platform that people are building AI applications on top of. We offer vector databases. We have inference services where you can bring large language models into Elastic.
We have multiple models through GenAI, through a re-ranker model, an embedding model. There's Hybrid Search capabilities that we have that allow people to build AI applications on top of Elastic, and that has been a big driver of growth for our business. It's expanding the TAM and search where people are using Elastic to build applications. That's one of the core pillars of Elastic. The next pillar I'd say is probably Observability, where the ability to index, store, and analyze these massive volumes of data has been applied to first logging, where you can index off log data and parse through it and use it really for Observability. That's where we've got a ton of strength in logging, and we also have metrics and traces, which is APM capabilities at Elastic. Logging is kind of the core functionality, and I'm sure we'll talk about it later.
There's a lot going on with metrics at Elastic that we're super excited about, and that's a new place that we're headed. The third capability is security. It started with SIEM security, the SIEM capabilities, and that has evolved. We also have endpoint functionality and some other stuff, and that's effectively a logging use case where people are ingesting this log data and finding security issues within all the log data and parsing through it. That's a place where AI has been tremendously valuable to Elastic. We've created a bunch of things in the security business where we were first to market. We were first to have an AI Assistant in our business.
We were first to have an agentic SOC, which is this Attack Discovery capability that we have, which sort of automates the parsing through of data and alerts, and triages it down so that the SOC analyst doesn't have to do that manually. That's been a tremendous benefit that we've seen in security, and our security business is just doing really well. Taking a step back, as AI has changed our business in the last three years, it's been pretty massive. For search, AI has expanded the TAM, where we used to have a narrower TAM. With AI, we're now being used to build applications. That's been huge.
With Observability and Security, I think what's happened is AI hasn't necessarily expanded the TAM, because we have AI capabilities built natively into our platform, we've been able to be first to market with a lot of AI functionality, and we've got a roadmap which is really appealing to our customer base around AI. That's helped us become more differentiated. That's the high-level, big-picture Elastic story. I don't know, Blair, do you want me to go into a little bit kind of the update on the quarter? Would that be helpful?
Sure. Actually, given your role as Global Vice President in Finance, absolutely give a snapshot of your last quarter, which was pretty good.
Yeah, maybe I'll give you a little context on what I do. Here at Elastic, I run FP&A. I have strategic finance. I run IR. I have procurement. I have enterprise data. All the business-facing finance functions are kind of where I spend my time, and I partner with Navam on a lot of that part of the business. I do have a pretty good sense of how the business has been performing. We had a really strong Q4 to end the year. We ended the year with CRPO growing 20%, RPO growing 28%. We just had a tremendous amount of long-term commitment that we saw coming from our customers. When I saw that data, I said, "Whoa, this is remarkable. Let's double-click into it." Are we doing more discounting? That's what happened.
I looked into it, and we're not doing more discounting than we have historically. Was it more five-year deals or some outlier seven-year deals that got signed that juiced RPO? No, it's not the case. It's all one-year deals and three-year deals, which is the same duration that our deals have historically been structured to have. Our business was just performing really tremendously well from a commitment perspective, and we're very happy to see the way that the business was performing as we exited the year. That was really positive and a lot of excitement here at Elastic around how our business is doing.
Eric, just for some of those who may not be familiar, last fall, October timeframe, you gave a longer-term or medium-term view on where you guys are going. Maybe if you want to just recap that and your progress towards those goals?
Yeah. We've updated those goals. We've taken those goals even higher than they were before. We had an Analyst Day in October 2025. At that Analyst Day, we walked through what we're seeing in the business around AI, the traction that we're seeing with AI, and as we take a step back and look at our business, the growth that we think that's going to drive. We were comfortable to tell people that we think that we're going to get to 20%+ on our sales-led subscription business. Sales-led subscription is the total subscription revenue, excluding the monthly cloud, which is more of a month-to-month business. We feel very comfortable that we're going to get to that 20% sales-led subscription growth.
We also talked about a Rule of 40 type metric, whereby our Rule of 40 is going to be 40+, and we talked about operating margin of being in excess of 20%, non-GAAP operating margin being in excess of 20% in the intermediate term. The intermediate term for us was FY 2029. We updated that a little bit at the Q4 earnings, where Navam said that we actually now, instead of just saying over 25% for the non-GAAP operating margin, we now expect that we're going to be in the 25% range for non-GAAP operating margin in that timeframe. A lot of positivity and excitement around where the business is going.
A significant amount of your growth in the next few years seems to be coming, or you believe will be coming from AI. Maybe we can shift back to that for a minute. On your core search business, where you guys came from, where you have literally millions of downloads of the open source version, but also thousands of customers, how are you approaching AI in your install base in the search side? What are you providing for them? What's the value that Elastic can deliver and keep yourselves relevant or sticky within that search install base?
We've got a lot of functionality. We've got a vector database that they use to build their application on top of. We've got re-ranker models. We've got embedding models through GenAI. We've also got inference as a service where people can bring an LLM usage, an inference usage from different LLMs into our platform, and they can use all of that as well as through hybrid search to build applications on top of. There's a couple of reasons as to why we really think that we're doing so well. One of the issues is data gravity. People aren't going to put all that data into the LLM. They need to bring the LLM to the data instead of the reverse. Elastic allows them to do that.
Elastic has retrieval capabilities that bring the data that's relevant to a question to the LLM, and we had a blog post recently where an application with Elastic was 70% more efficient in terms of token usage than one that wasn't using Elastic because Elastic is using that retrieval augmented generation, bringing the data that's relevant to the query to the LLM, or bringing the specific details to the LLM and making the LLM much more efficient.
Really using traditional search capabilities and y our rapid, highly scalable indexing, to find the relevant information instead of waste it. Not wasting, but tokens are expensive.
It makes it faster, it makes it more efficient, it makes it more accurate, all at a lower cost. If you're building an application with Elastic, it's tremendously impactful and can reduce the cost, can increase the efficiency, can increase the velocity of the application that you're building. That's the kind of thing that Elastic can do for people who are building applications. The other thing, just to give you a little bit more context, and this is an example that we've used historically, but if inside of Rosenblatt, there was some sort of search thing, and you had an internal LLM to answer questions, like an internal chat-driven interface, and you said, "Hey, I just broke my arm.
I need to go to the doctor. What can I do?" Through Elastic, can provide the underlying capabilities that say, "Hey, this is Blair Abernethy. He lives in this location. He has this medical coverage." They can say, "Blair, you should call this person. You should do this person because they're in network." It can give you all of that because it has context around Blair. Elastic brings that Blair context to the bigger picture LLM. You're able to build applications like that with Elastic.
You guys were talking about that a lot last year. Context engineering. The word was coming up. I guess part of the challenge here is that unstructured data is 80%+ , 85% of the data out there, right? A lot of it's unindexed. Are your customers recognizing this context engineering capability that you guys are bringing?
Absolutely. That's why we're winning these use cases, and that's why we're helping them to be more efficient. We're seeing a ton of customers who need this context. There's a lot of stuff we do. We've got an AI solution which helps. We've got customers in a big document company, and what they do, we allow them to query across their data. We bring the context to them, so it's not just the LLM that has to read every single document. Elastic finds the document, and then the LLM can read the document. Think of if you said, "Hey, I'm looking for contracts that have been signed with big automakers, and I want to know X about them." If you just ask an LLM that, they have to look at every single contract in the database. Think how expensive and time-consuming that is.
With Elastic brings the three contracts that are with automakers, it points them to what they need, and then the LLM can analyze that data and use all the intelligence that LLMs are great. They're super powerful. They've changed the way that we work, but they don't have the ability to parse out which one they should be looking for. Elastic can bring that to the table, make them much more efficient, much faster. You're getting better results at a lower cost. That's kind of how. Customers are wildly excited for that.
Eric, from a go-to-market standpoint, how are you guys taking this to your search installed base? How are you? There's a lot of, as we were talking before the call, there's so much innovation happening, so many changes happening so rapidly. How does Elastic kind of break above the noise a bit and say, "Hey, we can solve some of these issues for you"?
We go to our customers, we understand what issues they're having. We work with them to tell them where we can help them. We've put a lot more capacity into our go-to-market recently. I know that in Q1 of 2025, there were some issues with our go-to-market. We re-segmented the go-to-market, we kind of narrowed the scope of reps who were covering higher value customers and reworked the go-to-market there. There were some issues with that for that first quarter. Since then, the go-to-market's really been humming. We're seeing tremendous success. We've seen a big uptick in productivity. Because of that uptick in productivity, or a strong uptick in productivity that we talked about at the Financial Analyst Day, because of that strength that we've seen in productivity, we put more capacity into the model to really go out and chase the business.
We've been super excited for that. The way we're doing it is we've got some specialists who are search-focused who can give people expertise around AI and bring that to bear. We're going and talking to customers and seeing what they need. I'd say that because of the strength that we have in AI, we've been able to up-level the kinds of conversations that we're having, where we're coming in. Because AI's really a board-level initiative, the most senior levels at the company are engaging with Elastic because of that. Even if they're not necessarily purchasing AI capabilities today, we're seeing customers who are making commitments, and longer-term commitments, with Elastic because of the AI roadmap that we have and because of our ability to partner with them over the long term to really be the AI roadmap that they're excited for.
Maybe we can shift a little over to observability, because the observability solutions and security solutions been important drivers for you in the last five years. Where are you at on observability today? There are a number of competitors in the market. Then maybe talk a little bit about sort of AI within your products or what you're doing to stay relevant in that space.
The biggest thing for us with observability is there are kind of three pillars to observability. There's logging, there's APM or traces, and there's metrics or infrastructure monitoring. Historically, Elastic has been super strong in logging. If you have a big logging use case or a complex logging use case, Elastic and maybe one other vendor are who you call. We are the leader in that space, and we feel super confident about our ability to get into most of the logging opportunities and to win most of the logging opportunities. We've got an incredibly attractive price to value ratio where, or price to technology ratio, price to innovation ratio, where we kind of are best in class there, and we can bring best in class to bear at a reasonable price. With that being said, infrastructure monitoring, same thing.
It's just probably been the fastest growing part of the observability market. It's been growing so fast because a lot of this cloud infrastructure that's been coming online and becoming a bigger and bigger part of the technology ecosystem. We probably haven't had a best-in-class solution in metrics historically. We've got a competitive solution, but it's not best in class, or it hadn't been. We spent a lot of time in FY2026 reworking that solution, making it more compelling, more competitive. We made it meaningfully more efficient by putting a columnar data store on the back end, where we've reduced the footprint of storage for metrics significantly by using a different storage ecosystem than we use for our logging. Because of that, when you benchmark us against our competitors, we're now meaningfully more efficient than them, have a much better price to value ratio.
We think the big opportunity for us in observability, and potentially one of the big opportunities for us across all three solutions, is this metrics opportunity, where we now think that we can compete and win against the leaders in that space in a way that we couldn't previously. It's probably going to manifest itself with us initially rolling out as an add-on play, where we try and sell metrics to customers who are already using us for logging.
Ultimately, we think that we can lead with metrics and go out and win big. It's something, Blair, that a lot of the leaders in the field had been asking for and were excited for. When our team got on stage at our sales kickoff at the beginning of May and told everybody in the field that we were going to have this best-in-class metrics offering and walk through some of the benefits, there was so much excitement. It's been something that our field team has been dying to sell. It was funny, somebody said, "Oh, are you going to have any incentives for the field team to sell metrics?" I almost laughed out loud because they've been wanting metrics so badly. It's like you gave them exactly what they wanted. They don't need incentives.
It's something that they've been wanting to sell forever, and they've been selling against it. Now with this reworked architecture, with the back end, with the efficiencies, it's something that they can sell so much better than they've been able to sell before. Yes, there's AI capability. We have an AI Assistant, we have other capabilities that we have that are AI-centric in Observability. I think what's really going to move the needle is the metrics capabilities that we're bringing to bear, and that's going to be just a complete game changer for our business and for the metrics business overall.
That's GA now?
It's being sold. It's in production. I think if it's not GA, it'll be GA very soon. I think just because I don't want to shortchange other things. We're super excited about metrics, but we also have an AI SRE.
We're going to bring a lot of AI to SRE, and instead of relying on engineers to manually do all this work, it's something where you're going to have AI that can automatically organize these unstructured logs, discover patterns, figure out root causes, and this is going to make things so much more effective, similar to what we do with Attack Discovery in Security, which has been just a game changer. The security business, I can't wait to talk about that, we've seen so much happening positive with the security business. I think that the AI capabilities that we're bringing in for the SRE are going to really change the game for us. Metrics is probably the bigger opportunity even than that AI opportunity, to be honest with you.
You've had a lot of traction with logs in the last couple of years. I guess a lot of those customers are probably coming up for renewal over the next 24 months. That's a perfect opportunity for you to sell metrics.
The field could not be more excited about metrics.
Hopefully we'll see some impact from that in coming quarters. This is not five years out. This is soon.
Here's the thing. We're just bringing it to market in Q1. People have to get enabled on it, they have to go out and sell it. They get the deal signed, it takes time to turn to revenue. If you think about the revenue outlook that we have for FY2027, we're not assuming a massive revenue impact from metrics. I think it's hopefully going to help us win commitments, get a deal signed in the business. I don't think that it's going to be something where we're going to say, "Oh look, a meaningful portion of revenue is specifically attributable to metrics." I think it's FY2028 that the revenue component really starts to have a bigger impact. I think right now it's going to be bookings that's probably going to be the bigger deal.
Got it. Before we go to security, just with metrics, maybe help us to understand how your pricing works for Observability for logs and so forth?
Yeah. Right now the way that we price the solution is generally based on ingest and compute, that's going to continue to be how we think about things. There might be some tweaks around the edges, that's typically how we price things at Elastic. There's going to be, for the Serverless offering, it's slightly different how we price things versus ECE, it's a little bit more based on outcomes versus Well, outcomes. It's not just based on storage and compute. There's other complexities in there, it's typically storage and compute is the best way to think about how we price, it's consumption units. We have consumption-based pricing, that's going to continue to be how the cloud is priced.
Just that it's going to consume less storage because we're now storing it much more efficiently with metrics. It's going to compress the pricing that people see and make us much more able to compete with other vendors.
Right. Just on your Observability install base, maybe help us understand how much of that is sort of Elastic cloud versus on-prem or self-hosted. Is it mostly cloud?
I think first and foremost, it's important to realize that the self-managed is not necessarily on-prem. A lot of people are taking that self-managed business and deploying licenses in their cloud ecosystems. If you think about self-managed versus cloud, it's not legacy is self-managed, modern is cloud. We're seeing some hyper-modern use cases happening in self-managed environments where customers are taking our licenses and deploying them in their AWS, GCP, or Azure instances and running very modern AI use cases, but where they're buying from us in a self-managed way. To your other question, how do we think of I'd say that there's not a big distinction where one solution skews more towards the cloud than others. I'd say U.S. public sector tends to be where we see more self-managed business and large, what's the word I'm looking for?
Highly regulated industries tend to have more self-managed, I don't think that necessarily aligns with a specific solution where I'd say, "Oh, Observability is very self-managed and search is very cloud-centric." I don't think that's the case. Even as you think about search and the motion we've seen towards more and more AI, I don't think even that has been more cloud, for example. We're seeing a lot of customers who are buying self-managed licenses and using those self-managed licenses to deploy AI solutions in their own cloud environments or in their own on-premise instances.
Right. That's part of your value prop, is that you have the flexibility to allow customers to do it any way, to run it any way they want. Let's just shift it to security. Maybe go back and just high level, here's our security offering, where we came from, and then maybe talk about some of this new stuff like the AI s ite reliability engineering and AI Assistant and what progress you've made there on the product side.
I'm very excited too. Logging turned into SIEM because that's based on log data and using that engine to parse through log data from a security perspective and to identify threats and anomalies. That's what the SIEM solution does. We've also added XDR into the business, that came through the Endgame acquisition, where we have endpoints. One of the two $20 million+ deals that we signed in Q2 of FY2026, one of those deals was very XDR-focused, which was a tremendous win for us that we're very excited about. We've also got Agent Builder on top of the security solution.
Agent Builder is our solution that allows you to take the data that's inside of our ecosystem and engage with it in the same way that you would an AI chat interface, where you can just ask questions directly of the data, build different outputs with that data, and do a lot of automation with the data that's in our ecosystem already. You can create agents that are utilizing the data directly with Elastic in a way that we're pretty excited about. Those are two things where we were first to market on, also Attack Discovery is something that's been huge for us from a security perspective.
What Attack Discovery does is it allows you to, without needing a ton of manual work, to parse through the different alerts that you're receiving and figure out which ones we should be spending more time on, which ones are less serious, and just automating that triage to prioritize the alerts to spend time with, is what Attack Discovery does. It allows people to reduce the amount of time that SOC analysts spend doing that manually, it's been a tremendously positive thing. We've seen a lot of traction around it in our business and a lot of excitement. A lot of people are using it, but there's also a lot of people who are excited about, even if they're not ready to use it yet, I think it's part of their roadmap, when they're making their purchasing decision, it's something that comes to bear.
I think that security overall, we've just seen tremendous momentum in. As we think about FY2026 as a whole, how we exited 2026, a lot of the momentum that we're seeing in our business was from security. I think the reason that, it used to be that our reps were saying, "Oh, when we're in a deal, we do well, but it's hard for us to get in deals." I think that Elastic's name in security is getting stronger and stronger. We're getting in more and more deals, when we're in deals, we're really winning them. I ran into one of our three geo leaders at our sales kickoff, I was talking to him for a while, I was talking about the whole business, he was just so excited about what's going on for our security business.
It's just the capabilities that we're bringing, our ability to win when we get in deals because of our technological differentiation. He's like, "Look, my job is to get us in deals. The product team's job is to give me a product that I can sell once I get in deals," the product team has 100% delivered. We're ahead of our competitors on AI. We're excited about our ability to win when we're head-to-head with competitors. Maybe people can discount their product to a point that it's untenable, generally, we're really excited about our ability to get in deals then to win those deals once we're in the deals. Just it was really palpable to hear his excitement when I was with him at our sales kickoff.
If you think about your marketing, your profiles, you've been around for a long time, you've had extremely well-known on the search side of things. How are you raising your profile? Where are you investing to raise your profile on the security side?
I think there's stuff that we're doing for branding. I think there's stuff that we're doing in terms of going to conferences, raising our visibility. We've got search specialists, AEs as a field goal, and SAs as an overlay, where they bring to bear specific domain knowledge and are able to engage in those security conversations and have relationships with the appropriate security buyers in a way that we might not have had a year or two ago before we had that functionality. Just from a security perspective, we've made a lot of investment. I think a lot of it also is word of mouth and the fact that our solutions are winning and that they're being trusted by some of the biggest companies in the world.
The CISA deal, we haven't talked about it yet, but that was a huge deal for us. We signed a $26 million deal with the U.S. federal government through the CISA agency, where they're providing SIEM as a service to the civilian agencies in the U.S. public sector, that just shows a massive trust in Elastic. These are big, chunky deployments that are really important to agencies that are critical to the U.S. government, and they're trusting Elastic to deploy SIEM as a service. Stuff like that kind of commitment from the U.S. government is phenomenal and speaks to the capabilities that our security offering has.
You also, just in the recent months, got FedRAMP High authorization, which is great. Is this helping-
I think a month ago, right, that we got FedRAMP High?
Yeah.
Yeah, very exciting.
That, combined with this CISA deal, is pretty important. How about other non-U.S. federal state level and maybe talk a little more about internationally. How is your profile, how is business trending over there?
It's doing great. The CISA one is just such a standout that I mention it and I'm so excited by it. The traction we saw there was game changing. Internationally, the business continues to do really well with the public sector internationally at the state level, with the Department of War, and the different aspects of the non-civilian U.S. public sector. We've seen a ton of strength. I think that from a public sector perspective, we're very happy with the way the business is performing. The CISA is the bellwether, and it's actually put a pretty big uptick in terms of the cloud component of the public sector business, which isn't typically as much cloud, and CISA's changed that a little bit. Across the board, we're seeing strength in the public sector. There's a lot of excitement there.
Eric, maybe if you can comment from Elastic's perspective on agentic AI adoption within your customer bases. How would you characterize, what are they doing? What have you seen that's working effectively? Where they're leveraging your platform? Maybe just give us some sense of where we are on the customer journey in enterprise.
We talked a little bit about the document search that we're seeing, but there's also an AI native customer who we have on the music generation side. What they're doing is they're using Elasticsearch for vector search embeddings, a lot of detailed model stuff. What that allows them to do is they can index over 1 billion songs and then kind of create this retrieval and matching and generate lyrics to create this, I think we see a lot of as these AI-generated songs. They use Elastic for that. That's really a great use for us. You're also seeing customers using us for agentic at, there's a global supply chain. They're leveraging us for the vector search features and to embed AI into their products. They've got a new agentic product that isn't released yet but is on the way out.
I'd say agentic is very top of mind. It's still in the phase of becoming more prevalent. I think we're going to see more and more that gets deployed in the real world that's agentic over the next 12, 24 months. It's still earlier days for agentic than it is for some of the other AI use cases, is my sense. We're definitely seeing more and more of us with this agentic SOC, with some of the stuff that I just talked about on this music company, then some of the stuff that we're doing in observability, where people can use Agent Builder to engage with their data in a way, then ultimately the goal is for them to be able to create agents that can automate some of the things that needed to be done manually in an agentic capacity.
I think that's the direction that we're going in, that we're seeing more and more customers start to, if they're not deploying it, some are deploying it, even if they're not deploying it, they're starting to work with it. They're starting to think through how they can get value from this agentic approach to AI.
Yeah. Interesting. Your products have traditionally have been pretty technical and for customers, and so it's always hard to or difficult to have the staff, the teams available within an organization to really understand how to get the most out of the platform, out of the components of your platform. Is AI helping customers, helping to make Elastic easier to use?
Oh, absolutely. It makes a huge difference. Migrations off of other solutions onto Elastic are much easier with AI tooling. The AI Assistant enables people to do things on Elastic that used to be very manual and can be totally automated or simplified now. AI is definitely a big tool in terms of how much more efficient it is for people to get up and running on Elastic and to reduce some of the complexity, unquestionably.
Yeah. What about internally? Maybe just bring us up to speed on how Elastic has been leveraging AI within your operations.
In terms of our operations, there's a lot that we're doing in terms of engineering. In R&D, we're heavily utilizing AI coding tools, all the ones you'd think about from OpenAI, from Anthropic, from Google, and that's definitely accelerating the amount of code that we're deploying, that we're writing. We're creating much more efficient ways. In terms of go to market, the field is using some AI modules to automate seller onboarding and to make things a little faster. We're using it for feature testing in engineering, and in terms of in my function, there's a lot that we're doing in AI, in the finance function, where there are things that used to be very manual, and we're experimenting with ways to automate them. I'm not going to get too into detail with that because some of them we're still figuring out.
The kind of stuff in the last three months, I'm not even going to say six months, in the last three months that we've started to work on building in my organization, are going to create meaningful efficiencies for the business over time, we're already starting to see a little bit of it. How much easier it is to build some of these things, there's something that we had an idea for, six months ago, we were talking about buying a solution, we actually built our own pretty efficiently for something that we're going to be using that we need to get our auditors comfortable with. We're pretty excited about it.
Good. In the R&D side, wearing your finance hat, maybe talk a little bit about the cost of token cost, which is everyone sort of grappling with, versus the productivity or the value you're getting out of these things.
I track our token spend on a daily basis.
Okay. You're not worried about it, right?
I have a daily. Yeah, I'm not worried about it because I know it, but I know it because it's important. I don't know what you'd say in that cyclical statement there is, but it's something that we think about, that we track very actively. We want to make sure that engineers are using AI because it's going to give them. You can't create a 10x engineer without AI, but we want to make sure that they're using it efficiently, that they're using the right models, that spend isn't getting out of control, but also that we are getting real spend into our AI usage. I track it on a daily basis. I know how it's being used. We want to make sure that it's being used efficiently and that it's creating value in our organization.
It's very much a cost that we are aware of, cognizant of, and planning around. That we're excited about because the last thing I'd want to do. You have to balance it. This is going to change the way that people work. I don't want people thinking about, "Oh my God, I can't spend this $10 on AI," when they can be so much more efficient with AI than without it. An engineer who's really strong with AI, if they were told, "Hey, you can only use this many dollars of AI," they would effectively stop working once they ran up to that limit. Because it's like, if we were driving from San Francisco to L.A. and we ran out of gas halfway through, we wouldn't get out and walk. We'd figure out a way to get more gas in the car.
I think that that's what AI can be in terms of force multiplier, is the same way a car can move you from here to L.A., from San Francisco, 500 mi, so much faster than a horse can. I think that AI versus manual coding is the same way that no one is going to, in a short amount of time, it's going to be pretty rare that you see people manually coding things, is my belief. That's not an Elastic belief, that's an Eric belief based on conversations that I've had with our team here and some of the things that I've seen that some of the engineers are able to do that is pretty remarkable.
I've been hearing similar comments this week from several different people at the conferences I'm attending. Yeah, we're heading towards a point where coders may not be coding all that much at all.
They still have to understand code.
Yes.
It's going to be critical for them. They're also going to have to understand how to get agents to work for them and how to have these agentic factories that are developing software, where they're giving directions, but they're not. I think one of our developers here said something to me that really resonated. If we were in a car factory and you said, "Oh, this isn't working fast enough." You have all these machines and they're putting the cars together. You wouldn't go down there with a hammer and nails and work alongside the machines. That would be really inefficient. His belief is that at some point pretty soon, we're going to get to a world where that's what coding looks like, where you're setting up the factory for the agents to develop code, but you're not there writing code next to them.
Right. You're setting the strategy and the direction and the feature descriptions, if you will, and the processes, but then let it run.
The Q&A, the testing, and the code review. You're having all of those be done by different agents who have a set of parameters that they're working under and are able to utilize in order to really drive the best results.
Have these coding tools changed your hiring practices or views at all within the engineering group?
I think that across Elastic, we need to be thoughtful around what our organization is going to look like going forward. I think in FY2027 you'll still see us add headcount on a net basis, but it might be in different organizations than we would have without the benefit of some of these AI innovations. The shape of the organization, the places where it grows is going to be different than it would have otherwise, because there will be some new efficiencies that'll be introduced because of AI, and we're going to use AI as many places as we can. For example, the selling function, you still need people talking to people in order to sell software, and I don't think that's going to change in the immediate term.
we'll see headcount continue to flow into the field org, obviously, but there'll be change in terms of how Elastic's growth looks in this fiscal year.
I guess in your SMB side of your business and your monthly business, are you utilizing or applying AI there to help drive up consumption?
I think it's something that there are more opportunities for us to do more with AI in the low-touch part of the business, that's something that we're looking into and exploring, there are different ways that we can get benefit from AI, for sure, in that sort of the self-service business.
Okay, great. Maybe just as we're getting close to the end of our time here, just step back a bit and just sort of remind us, in terms of M&A technologies, how are you looking at the M&A world right now, just given the fact that things are changing so rapidly? Are you more focused on internal development? Because you bought a couple interesting pieces in the last year. Maybe just frame that up for us.
I don't think there's going to be a stepwise change in our M&A strategy in the immediate term. I think that we've had this perspective that if things can pull forward our roadmap, if they can bring innovation to the business that we wouldn't have otherwise, or that would take us much longer, we're open to doing something with M&A. That doesn't preclude us from doing at a certain size or scale or what have you. If it's accretive to the business in terms of the growth and the trajectory of our business and the direction that we've been moving in, which is getting more and more AI-centric, more and more agentic. If there's something in that space that's going to support that, I think we're very open to adding that technology to our business. I kept that short because I know we're short on time.
Appreciate it, Eric. It's great to see the business performing so well in the last couple quarters, and it seems like the opportunity window has been getting bigger and continues to get bigger for Elastic. Thanks for sharing your thoughts and your time with us today.
Thanks for having me, Blair, really appreciate the conversation.
Great.