Let's get this started. My name is Koji Ikeda. I am one of the software analysts here at Bank of America. I'm thrilled to be hosting a fireside chat with Elastic. We got Ken Exner, Chief Product Officer, and Eric Pringle, Global Vice President of Finance. Thanks so much for being here, guys. Before getting into product, I do want to get technical with you, Ken.
Cool.
I'm thrilled to have you here because I want to talk about the technical side of Elastic. Eric, maybe first question's over to you. You guys reported results last week. It was a busy day on reported earnings that day. Lots of companies reporting. Maybe just give us a high-level overview of what happened in that quarter and, I think most importantly, the guidance methodology as you're thinking about the next fiscal year.
We were really excited about the quarter. We saw CRPO grow 20% and RPO grew 28%. On a constant currency basis, CRPO increased five points growth from the prior quarter, tremendous momentum. It was really a testament to the bookings that we're seeing and the strength in the product that Ken and his team put together, where we're able to really win in the field. I think there have been a lot of people asking, "Oh, well, is any of this inflated? Is there some sort of thing that's going on where you have more discounting or something like that?" The answer is no. This is truly the business performing, we are really happy about that. That was tremendously positive.
One thing that did happen in our quarter is that there was a shift in the mix a little bit more towards cloud. Because there's an upfront rev rec component to self-managed, that took a little bit out of revenue. Think about there being a one-point headwind to revenue based on that, coupled with a little bit of FX. Overall, very positive quarter in terms of the commitments. A record quarter over the last couple of years in terms of the growth and commitments. We're really happy around that. We also gave guidance for FY 2027. We initiated our FY 2027 guidance. The way to think about our FY 2027 guidance, a couple of things. We guided to a full year of 14.5% growth, which is actually acceleration from the Q1 guide.
We think that we're going to see the trajectory of the business accelerate nicely on a quarter-to-quarter basis, which is very exciting, and we're comfortable with the guide based on what our CRPO is and what that means in terms of how much we have to go get that's not already committed. We feel really comfortable about that. In terms of the guidance philosophy, I would just say that it continues to be, we want to put forward a guide that we feel comfortable that we can hit, that we feel confident that the business has plenty to execute against to get to that guidance. We feel good about everything on the guide side.
On that point in the shift mix to Cloud one point headwind, can we dive into that a little bit more?
Is that really around, hey, when you gave that fourth quarter guide, the first time you thought this amount's going to go to cloud, this amount's self-managed, and it turned out a little bit differently? Was it bigger customers that decided to change their allocation between cloud and self-managed?
I'd say it was the former, but with a little mix of the latter. The biggest thing for us was we won this CISA SIEM-as-a-Service deal, where we're providing security SIEM to the U.S. public sector, the civilian agencies there through CISA. We've actually seen a much better uptake of that than we'd even expected. There are certain government customers who historically had been consuming us through self-managed, and some of them shifted over to the cloud when they did deals with us in Q4. That CISA SIEM-as-a-Service deal really drove an increase in the cloud portion of our business, which we were really happy to see. It's all going to come to us as revenue. It's just that the timing of the revenue recognition moved around a little bit.
Eric, I hate to put you on the spot, but FedRAMP, do you know exactly where you are, FedRAMP certification on the cloud side?
I believe that we got,
Certified.
Yeah, we're fully certified FedRAMP High.
Fully certified.
Yeah.
It's up, yeah, as of last month.
Okay.
Yeah. Okay. I guess the key message here.
FedRAMP High.
Yeah.
High.
We were FedRAMP Moderate. Now we got FedRAMP High.
Gotcha. I think the key message here on the guide is look at CRPO as an indication of what's giving you that confidence
Yeah
in the CRPO. Okay.
I think that's the right way to think about it.
Okay.
One other thing besides CRPO, I think it's worth noting because it's something that we've put a lot of effort into across fiscal 2026, is field capacity. Call it two years ago in Q1 2025, there were some execution issues. Since then, Mark Dodds has done a tremendous job of really getting the go-to-market motion on track. Because of the success that we're seeing in the go-to-market and the productivity that we've seen in our field force, we've actually started to invest a lot more in capacity. Over the last 12 months, we've been putting more AEs into the field, and that was part of the reason we saw success in Q4, and that gives us a lot of confidence going into FY27. Just given the capacity and productivity that we need to drive the business, we feel very good about that.
Maybe on the question on go-to-market with Mr. Dodds and really considering you just finished your fourth quarter.
Good time to make any sort of strategy shifts for the next fiscal year. Anything we should be thinking about on any sort of account reallocation or any sort of geographic reallocation or anything on the go-to-market side?
No, nothing in terms of account reallocation, geographic reallocation, nothing in terms of sales compensation. We feel very happy with how our go-to-market functioned across fiscal 2027. You can particularly see that in Q4. Based on the way the team is executing, we don't need to change the go-to-market, we just need more of it, and that's why we're adding capacity to the business.
Got it. Ken.
Koji.
Let's talk Elastic technically.
Yeah.
I do want to start high level first, and so it is a very technical product.
Customers buy you for the technical benefits and the differentiation in there. Maybe just taking a huge step back, what is Elastic and why do customers buy you?
What is Elastic? That's a big question. The company started as an open source search engine, Elasticsearch, which is one of the most popular open source projects of all time. I think it's the most popular Java open source project. It's used by millions of developers. Initially, people used it as a search engine to power search within their applications. People started realizing that you could use it as a development platform. People started using it to build matchmaking sites and ride sharing sites, and using it to build signal intelligence systems and things like that. Couple of the most common use cases were people using it for log analytics and observability to search through logs, to search through metrics. Also people started using it for threat hunting, also searching through logs and security event data, to do security threat hunting.
Over the years, we evolved into sort of three businesses, one around the search business, which these days is really about powering AI-based search. The observability business, which was taking a lot of what people were doing, using us as a log analytics platform and searching through metrics and traces and other things, and making a more out-of-the-box experience there. Finally on the search side, this is probably the newest, but growing incredibly fast. People were using us as a SIEM or as a security analytics platform. We've been expanding beyond that to the adjacent spaces in the security space. Those are the three core businesses. The search, which is these days powering AI search or powering AI applications, not just human usages of search, but also observability and security.
Let's tackle each one of those opportunities one by one.
AI search or search AI, we'll combine those two.
Yeah.
Observability and security. When customers are coming to you, let's start with security.
When customers are coming to you and saying, "Hey, Elastic, help me with my security problem.
Yeah
What is that problem and how do you help solve it?
Well, the core of this is we became very popular for threat hunting. People would use us to search through logs and security event information, looking for that needle in a haystack, trying to figure out if there was an intrusion, trying to figure out what happened, and trying to correlate across different signals across their business. We packaged that up as a SIEM product, and this happened a few years ago. This is the core land motion for us, which is we are the SIEM for most of these SOCs, security operation centers, that they use for that core threat hunting. We also do some of the adjacencies. We expanded into endpoint protection, we expanded into cloud security, we expanded into entity analytics and SOAR. Some of the adjacent spaces.
The core of the product is the SIEM application, which is a security events information system that people use for threat hunting. I will also say that these days everyone in the security space talks about an agentic SOC, which is taking a lot of the activities that these security analysts do and turning it into agentic workflows that allow them to respond more quickly. We've been a leader there at turning this security operation center, which is built around the SIEM, into a fully agentic security operation center. These days people talk about it as using AI to automate the tasks of a security professional. People are creating attacks using AI, so you have to respond with the speed of AI as well.
Same question on the Observability side? What are they coming to you for?
The core of the business was always in logs, which is people were using us as the ELK Stack, as people used to refer to it as. Became the most common solution for logs, continues to be a big part of our business, but we expanded into the adjacencies as well. We expanded into metrics, expanded into tracing, expanded into synthetic monitoring and RUM, a bunch of the different areas. Today we're a complete observability solution. I will say that we've spent a lot of time over the last year trying to become a really great metric solution, and I view this more as an opportunity for us to expand. Even without investing a lot in metrics and infrastructure monitoring, we've had decent pickup and adoption, but logs has always been the core of our business.
I'm very confident about our ability to win and go on the attack in terms of metrics, because we've done a bunch of performance and efficiency work. We're now three times faster than Prometheus-based systems. We're two point five times faster than Prometheus-based systems, two times faster than ClickHouse or more efficient than ClickHouse. We've done a lot of work to be a highly performant and highly efficient metric store, in addition to being a highly performant and efficient log store as well.
Can I add something there? I think it's just good to note for everyone that logs is the primary portion of observability in which we play, and I think that if you look at the market growth metrics and infrastructure monitoring has probably been the fastest growing part of that market.
Yeah.
For us to be able to be much stronger in that market and more competitive, which we relaunched our product in fiscal 2027. At SKO, we got on stage and talked about the tremendous strength in the change that we've made in the product. I think that presents us with a huge opportunity for our Observability business to really be reinvigorated.
Go to the next level with those metrics capabilities. It's something that the field is super excited about, it's something that the team is super excited about, and I think it's a really big opportunity for Elastic in FY 2027.
We also now natively support Prometheus data in PromQL, so if you're using a system like Grafana that is Prometheus-based, you don't have to change anything. You just swap out the back end, and suddenly it's cheaper and faster. You don't have to change any of your dashboards. It's immediately cheaper, immediately faster.
Maybe a good question for you, Ken, thinking you're the Chief Product Officer, and so the strategic move to invest more into metrics infrastructure monitoring, was that a recognition of, "Hey, there's a pretty good opportunity here, we should go for it," or were there customers saying, "We need more because we want to use you guys for this?
It was both. One, it was, as Eric mentioned, metrics or infrastructure monitoring has been the fastest growing part of the observability space. It's not one that we had focused a lot on. Even despite not focusing a lot on this space, we still had customers starting to use us for metrics workloads. This is because we're seeing a bit of consolidation happening. People are wanting to use the same tools for metrics and tracing and observability and logs. Some customers were pushing us because they were wanting to consolidate tools. We also saw it as a big opportunity for us to grow the business. I would say it's both of those.
Ken, when you're out there talking with customers, I think one of the things we often debate with observability and security is that they're two different buyers.
Yeah.
When you're out there talking with customers, are you still talking with two different buyers for your products, or are you beginning to sit at the same table as either one person talking about both or having both parties in the room at the same time?
The most common is it's different, but if it's a business that is trying to save money through consolidation, it tends to go up to the CIO. Oftentimes, for consolidation plays, it's going to be at a more executive level, and that's usually the same buyer. Oftentimes CISOs or the SRE team and the InfoSec team report up to a CIO eventually. They tend to be different buyers, but we're still able to use the accounts to move laterally. One of our most common plays right now is talking to our existing search users and getting them to consider us for security or for observability. They just make the introductions to their CISO, and their CISO often already knows us because their security analysts are already using us in an open source form, and we're able to turn them into a security customer in addition to search.
How does the conversation go with customers that choose you for observability and security traditionally?
Yeah.
The natural progression is how do we use agentic capabilities within observability and security, and you guys can power that.
Yeah.
What does that conversation look like with customers?
In the security space, everyone's talking about the agentic SOC. There's a lot of curiosity about what it means. We're able to actually show it in the product on top of real data, and it's not just this theoretical thing or not just a good demo. Usually, in the security space, what we do is we show it and we actually let them use it. It's gone from this theoretical or this marketing hype to something actually very real. I remember when we introduced Attack Discovery, which was one of the very first uses of agentic AI in security. We introduced this at RSA a year and a half ago.
It blew people's minds because what we were doing is we were processing all the alerts that come in and automatically figuring out which ones were false positives, which ones were real, which ones were correlated, and we're able to map this to the MITRE ATT&CK chain and basically show customers the entire attack path. For a security analyst that sifts through hundreds of these alerts a day trying to figure out which ones to pay attention to or not, we've just taken that work and just done it in a minute and showed them, "This is what you need to care about." When we had these conversations, these analysts would start to cry or they would tear up. They were like, "You've taken all the drudgery away from my work and gotten me to a point where I can actually fight the issues.
I can actually act on these things." When you show them how real this is, it's quite empowering. We were always worried that people might react to it as taking away their work, it was actually quite the opposite. It's the drudgery, it allows them to actually feel more effective because they're buried in what they're doing right now. They're buried in drudgery. The same thing on the SRE side. They're buried in drudgery. They're getting woken up in the middle of the night. They spend the first half hour trying to figure out what the hell is going on, why are they getting paged in the middle of the night. If we can immediately show them, "This is what the issue is, this is what you need to investigate.
These are the potential ways to remediate this," again, they beam because you've taken that drudgery away from them.
I think what's really interesting and attractive for Elastic for the end customers is having a cloud solution and a self-managed solution.
Help me understand why having both options is important.
Yeah.
How you guys think about driving innovation between those two products? Is it parity for the two products? Is one more important than the other, or is one catching up to the other? How do you guys think about innovation between the two?
This is a huge differentiator for us. We are open source. We have a core product that's open source. We have self-managed, meaning you can run it yourself on-prem or on your own AWS or GCP accounts, wherever you want. We have customers that run us on battleships, run us on Humvees. You can run it wherever you want. We also have two different versions of our cloud offering. We have a hosted, which is a single tenant version of our self-managed that we manage for you on all three cloud providers. We have the serverless offering, which is a pure SaaS version, which is on a modern serverless architecture. We offer it multiple ways across all three major CSPs, across more than 60 regions, across gov clouds, FedRAMP Moderate, FedRAMP High. We're working on all five right now.
Lots of different ways to consume the software. I think the other thing that makes us a little bit different is that these are not forks. We try to maintain the core system together, which allows us to always lead with serverless, but eventually make things back into the other ways we offer the software, too. The promise we make our customers is that we will always launch things first in serverless, but we try to always make sure that we make them available for on-prem as well. It's important, especially for our public sector customers, where they may be completely air-gapped, they may be on battleships, as I said. They need to know that they're going to have a great security product, or they're going to have a great observability product, and they're not crippled because of their environment.
Speaking of forks.
Yeah. Sorry, I will say one other thing.
Yeah.
The other reason I think this is important is that it allows us to be where the data is. This is important because think about observability. Each of the cloud providers has their own observability solution. Despite that, we have a very vibrant observability industry that works across these different cloud vendors because it allows them to create a common interface to those different systems, but it can also be where the data is. In terms of our search business, you don't have to move your data into the AWS because that's where your vector database is. You can have your vector database be wherever your data is. There's two advantages to being wherever a customer is. One is you can provide a single interface across wherever their data is, and you can be wherever they are.
If they are in GCP or on-prem, you can have your Elastic cluster or your Elastic deployment there.
Forks.
There is a hyperscaler out there.
with a forked version of you guys.
I don't hear about it that much when I'm talking to customers and partners out there. I'm curious from your seat and when you're out there talking with customers, does that forked version come up at all anymore?
It does if you're an AWS customer.
If you're an AWS customer, they bring it up because, for an AWS customer, it's there, it's easy for them to use it. They try to attack us and their pitch is essentially, it's the same thing. It's the same product. We often have to combat that and explain that it's not. It's a fork, and it's one that's not been maintained very well, and we have to talk about how we are significantly more performant, more efficient. Then they run the benchmarks, and they actually see that we actually are quite a bit cheaper in terms of the total cost because of the investments we continue to make in efficiency, the investments we continue to make in performance. We have to combat that.
I got you.
Yeah. It's usually only with AWS customers.
Elasticsearch has been around for a long time, and when we peel back Elasticsearch, it is based off Apache Lucene.
I've always wanted to ask you, Ken.
Yeah.
How do you guys make it easy, or maybe it's not easy, to switch between different versions of databases, vector databases, solutions, search solutions that at the very, very core are Lucene-based?
Switch between different vector databases?
If someone wanted to come to you, and they're using some other Lucene.
It's not easy. The interfaces are completely different. Lucene is the core search. We are the primary maintainers and contributors.
to Lucene. People often view it as one and the same thing. With us, we're 90%-plus, 95% of Lucene contributors and contributions. We chair Lucene open source project. Others can use that as a core engine in whatever other implementation they have, but the interfaces are different. The implementation is different, so you can't very easily change. If you have a system like MongoDB that also uses, it doesn't mean you can change.
Okay.
I wanted to ask the bull debate on you guys.
Yeah
AI as a demand accelerator. Ken, maybe a question, or Eric, a question for both of you is what are you seeing out there that's giving you the confidence that AI is going to be a long-term driver for you guys and an accelerator of growth for you guys?
I'll start, please.
Yeah, please.
I like to look at it two ways. One is we're a foundational part of the GenAI and Agentic AI tech stack. This is people using us as a retrieval system as part of their context engineering environment. We're also using all of these same tools ourselves to power our observability and security solutions. Both of those are important growth drivers for us. On the search side, as I mentioned, search has become something that doesn't power human interfaces anymore. It's powering agentic experiences. People are using search as a retrieval system for passing data to an LLM or an agent. This begins with us as a vector database, but includes a bunch of different retrieval techniques around that. A bunch of the supporting ways to pass data to an LLM or agent. It's no longer just RAG or prompt engineering.
It's helping people build MCP tools, building skills, different ways of exposing data to an agent or an LLM. We provide a complete context engineering platform that supports various different techniques. Everything from embedding models to different retrieval techniques for very efficiently figuring out how to get data to an agent or an LLM. That is a huge growth driver for us. How do we continue to invest in that and be a core part of the modern agentic AI stack? The other part is we use these things ourselves, and we get to use our own toys. It's allowed us to move very fast in observability and security, and to be leaders there in applying these to those use cases. I mentioned before Attack Discovery. We were actually the very first observability and security company to introduce AI Assistants and copilots.
This is more than three years ago. We've continued providing that leadership, constantly providing the leading capabilities for using agentic workflows within observability and security. It's because we can use our own technologies and we can use our own tools, and that's actually enabled us to move faster than anyone else. This provides growth because suddenly now it's not just selling the platform, it's now we can monetize this through token usage. We can monetize this through workflow executions that we charge for. We can monetize this through conversation turns, different meters that spin based on us using these tools for those agentic and AI experiences.
Yeah. The one thing I'd add is I think that we're seeing tremendous momentum on the AI front in terms of the metrics that we have. We reported 600 of our 100K plus customers are now using us for AI capabilities. That's a pretty big step up from the prior quarter. We talked at the Analyst Day in October around some of the expansion metrics associated with AI and some of the metrics around the traction that we're seeing there. It's not just something that's part of the talk track, it's actually genuinely supporting the business. Across the board, we're really happy with what AI is doing for our business.
I wanted to ask a question on embedding models.
Okay.
This one's going to be Ken.
I spent a lot of time over the last week.
Oh, you've been researching this.
I've been trying to figure out what exactly is an embedding model, and what does it mean for certain companies.
An embedding model, just quick.
Yeah. What is it and why?
Embedding model is how you vectorize data. You take data, whether it's text or image or whatever, and you want to put it into a vector database, you need an embedding model. It's a way to turn text or whatever into vector coordinates that you put into a vector database that allows you to do similarity search. It's a way to turn it into a couple plots of data that you can put in the vector database. It needs to create those coordinates based on an understanding of similarity. It needs to process the underlying data, understand what is similar, and then create these coordinates to plot into vector space.
When people introduced vector databases, initially, it was about text, taking text and trying to create coordinates for a vector database that allows you to say that a cup and a glass are similar concepts, and you can plot those in proximate space in a vector database. People also use it for images and text and stuff. Actually, one of the things people pushed us for very early on, even before generative AI, is people wanted to do image search using Elasticsearch. Companies like Adobe were using us to do image search even before generative AI. We've been doing this for a while.
The thing that's been happening more recently is a race to multimodal embedding models where you can take a PDF document, which might contain tables of data and text and images and stuff, and be able to vectorize that entire thing or take a video or take audio. We recently introduced our jina-embeddings-v5-omni series of Jina AI models, which are multimodal, which allows us to handle any type of data. We're very proud of this because it truly is multimodal. It handles audio, video, images, text, and everything, using vision model techniques. It's state of the art in terms of multimodal. The other thing I'm very proud of is, this one often people overlook, but it's really important if you're actually using these, is that we have some of the most efficient embedding models.
We introduced a new small and nano class of our embedding models, which rank right now in the top 10 of all embedding models. They're super small. Compared to the rest of the top 10 of embedding models, I think it was 14 to 50 times smaller than any of the other models, which directly translates into cost. If you're using this, people are picking these because they're still highly performant in the top 10, but they're a fraction of the cost to run. I geek out over efficiency, and that to me is almost as cool as multimodal.
Interesting.
Yeah.
Sounds like something I need to dig in a little bit more on here.
Yeah.
We are all out of time. Ken, Eric, thank you so much for doing this. This has been a fun conversation.
Thanks for having us, Koji.
Thank you.[crosstalk] Yeah, man. I literally spent like Koji Ikeda. I am one of the software analysts here at Bank of America. Welcome to day three of our tech conference. I am absolutely thrilled to be hosting GitLab for a fireside chat. We have Bill Staples , CEO, and Jessica Ross , CFO, with us today. Thanks so much for joining us.
Absolutely.
Hello, everyone. Thank you.
Hello.
I think you guys just reported results this week. It's been a busy week. I've been running around the tech conference, and so maybe even just to help me, would you recap the first quarter results, kind of the big news coming out of it, and how we're thinking about guidance going forward?
Yeah.
Why don't you kick off with the numbers, and I'll fill in some color.
I know we had a very strong first quarter, very excited. We beat on both revenue and profitability. Revenue was $264 million, growing 23% year-over-year. We delivered 14.2% in NGLI margin, which is a 200 basis point increase year-over-year. Our enterprise business is strong. We are growing seats. We saw strength across all geographies, and we saw some particular strength in PubSec in AMER. We're very excited about that. A couple of other just data points. Our 100K customer cohort grew 18%. We just crossed a big milestone with GitLab Dedicated with $70 million in ARR, and overall, the business is very strong.
Maybe I'll just add a few other data points and color around leading indicators for growth. We shared this quarter we had 30% year-over-year increase in first orders, so new customers coming into the business. That's been a focus for us because we've traditionally been a land and expand business, but have never focused as a company on winning new logos per se. It's always been take what comes. Now, as a billion-dollar revenue company, we've decided to specialize our sales force and have dedicated focus on first orders, as well as a product-led growth motion that we've kind of rebooted, and that's starting to pay off. Customers tend to land small with us, but grow over time. It also demonstrates our competitive position and ability to win in this very dynamic market. Second, we also shared a number of activity metrics in the platform.
We're the beneficiary of all of the AI coding dynamics that are underway with Claude, Cursor, Codex, and multiple other players. While our business model currently doesn't capture that because we've always had a seat-based business model that allows engineers to use whatever coding tools they want to use GitLab to productize that code, we see the platform usage surging. Last quarter, we shared, for example, 60% growth in projects that use our security capabilities. This quarter, we shared nearly 50% increase in year-over-year in code pushes, more code getting pushed into GitLab. Also, significant growth in pipelines.
Those are the things that take code and verify it, secure it, get it ready for deployment, and then push it out. We saw that go from the 20s in the last half of FY 2026, growing month-over-month now to 38% year-over-year growth in Q1. Those are all leading indicators that we're really benefiting from the AI coding work that's going on around the industry. I shared during the quarter five new architectural bets that we're making that set us up not only to deliver more value for customers, but also capture that value over time. One last data point for the quarter, which is it was the first quarter that we've actually had our new agent platform product in market.
This is us taking LLMs and providing agents across the software life cycle to help with all the tasks that engineers have to do to not only write the code, but actually ship the code out to their customers. In our first quarter, we captured more net new ARR than any previous quarter combined across our prior two AI products. It was a very strong start. We also shared an interesting early data point around consumption because this new agent platform is a new pricing model that's a consumption-based model, not our seat-based model. In the first quarter, we saw a $20 million consumption run rate. That's a measure of both committed credits and on-demand credits that were built up both pre-GA and then in our first quarter of business. Felt that was a very strong start.
Nothing that you should depend on in terms of $20 million on the other side. It's a run rate based on one quarter only, so we're not building it into our forecasts, but we thought it was valuable to share it nonetheless.
I guess maybe a couple other things.
There was a lot of goodness in the quarter. We did also highlight, though, that the quarter saw some churn and contraction related to layoffs and also some unique contraction related to M&A. We view those both as temporal, especially the M&A side unique, but without that, the quarter would have been even stronger. I think I know you asked about guidance too, so just maybe stepping back for those newer to the story, when we gave guidance at the beginning of the year, we gave a very wide range, 15% to 17% in terms of revenue growth. There's a lot of moving parts, which we'll talk about as we have this conversation. We really positioned this year as a year of investment and execution. I think the big message this quarter is that is playing out as planned. Our assumptions are playing out.
This is a quarter of tighter execution, which gave us confidence to raise the guide. We've narrowed that to 16% to 17% and really just, again, excited about the year ahead.
Is there a metric that investors should be focusing on as the best leading indicator of the ability to not only achieve the guide but potentially beat it? Is it billings, RPO, the Duo Agent Platform, 20 million metric? What should investors be focusing on as an indicator of health?
I think we really laid out five growth initiatives. I don't think it's one. I think with our business, it's really about us firing on all cylinders. I think that's why we're very intentional about laying out the metrics that Bill talked about. At the end of the day, though, in terms of our guide, we have a ratable revenue model, and that gives us a lot of visibility. We really focus on revenue as our forward metric that you should also be focused on as well.
Okay. A couple of weeks ago, you guys announced that there might be some changes within the organization, and then you fully laid it out on Tuesday of this week. Help us understand the strategy behind it, what you think will be the benefits of it, and then how to think about headcount growth going forward.
A couple weeks ago, I published a letter. If you haven't read it's published on our website, and it's called Act Two. GitLab Act One has been an incredibly successful effort. We're now over $1 billion in revenue, growing at 23% this quarter, and we serve over 50% of the Fortune 100. Some of the largest companies and organizations around the world depend on GitLab. We're incredibly proud of what we've built and what we have. At the same time, you all see it, I'm sure, the way software engineering is happening is changing. It's changing rapidly.
We sit down with our customers all the time, but this quarter in particular, we had several opportunities to have our advisory board, these are some of our largest, most strategic customers, come together in person with us and talk about the future of software engineering, where we believe it's going, and what the opportunities are for GitLab to add value and solve their problems. We came up with five architectural bets that we believe set GitLab up to benefit from the AI structural tailwinds that are happening around coding. These are investments that often relate to the scale of infrastructure that we provide, as well as the capability that we already provide for humans, but now are needed across humans and agents. I can walk through them if you're interested, but the bet starts there.
The Act Two starts there is we realize in order to serve our customers going forward in this agentic era, we need to focus on the scale and the capability of what we do across both humans and agents. That work was laid out. We also, as a management team, this was the first quarter since I became CEO, six quarters ago now, where I've had the entire exec team together. We've gone through some executive changes since I joined. Jessica joined just a few weeks before the quarter began, as did my CTO. As an executive team, we got together, we looked at that strategy, those architectural bets, and we feel very confident and convicted that those are the right things for us to go laser focus on and execute.
We also asked ourselves, though, what else do we need to change to move faster as an organization to capture this opportunity while it's right here in front of us? That led to a discussion around a restructuring and changing both the operational footprint of GitLab as well as the organizational layers and some of the culture dynamics. When we looked at the operating footprint of GitLab really came into its own amidst the COVID era. It was really unique in defining a remote-first async culture that was native to that era. The hiring strategy at one point was hire in any country where there is talent. We ended up with 60 plus different countries where we have employees, and for a company our size, that is pretty unwieldy.
We had a long tail of countries where we had one, two, or three employees, and we've decided to shrink that. We reduced the number of countries by 22, and now we're smaller in that regard. We also looked at our management layers. We had eight layers of management, and we decided in order to streamline communications, prioritization, decision-making, we wanted to shrink that to five. We've done that. Third, we looked at how we execute, how we focus as an organization. Our previous core values were really centered around flexibility. Again, born in that COVID era when everyone was struggling to figure out how to do remote work and how to work from home. We decided that for the agentic era, the number one thing to focus on is speed.
We're shifting from flexibility to speed with quality is our number one operating principle. Second operating principle that we've defined is ownership mindset. We want to truly empower every individual in the organization to think like an owner of the business, to be able to own decisions and execute, versus just take actions and check boxes on tasks. Third, our third operating principle is all about customer outcomes. We want every individual in the organization thinking about the reason we exist is to serve customers and deliver value to them. Everything we do, can we clearly define the customer benefit or customer outcome that that work produces? Those are the three new operating norms that we created, or operating principles. Those are the structural changes we made.
When I introduced these changes to the company, we decided, given the magnitude of opportunity and the magnitude of change that we're introducing, we would explain all of that to our company transparently and also do the restructuring openly. We spent a couple of weeks with all leaders in the hierarchy to work through exactly how the reorganization would work. We offered the opportunity for every employee to decide if they wanted to opt out of all of this change and be part of the restructuring. We felt like that was important because, again, the magnitude of change is great, and this is an opportunity to exit the restructure with an aligned, committed team versus a team that feels like change is being thrust upon them, and then they have to go find something different if they don't like it.
It's been a tough couple of weeks at the company, but I can say I think we're coming out of it much stronger as a result of the approach we took to the restructuring, and a new energy infusing in the company now. Next week, we have a customer event. It's an annual customer event that we do at the start of every major release. It's in London, but it's broadcast here if you'd like to watch it. We're going to be unveiling a bunch of new innovation, including several of the architectural bets that I shared in that Act Two letter, which you're definitely going to be interested in watching if you get a chance. I recommend it.
Maybe just layering on how that works from a financial impact perspective as well. Just to be clear, this restructuring was never intended to be a cost-cutting or margin exercise. This is really about aligning the organization strategically to win in this agentic era. Ultimately, with all the decisions, we ultimately reduced our workforce by about 14%, 350 of our team members. We are recognizing a restructuring charge of about $30 million to $35 million, $19 million in Q2, with the remainder through the rest of the year. We are reinvesting the intent of all of those savings in the architectural bets that Bill has outlined. We're really looking at it in three ways: people, technology, process. This is a big cultural shift for GitLab, and we are asking a lot of our Act Two team members, so we're investing in our people.
Secondly, again, from a technology lens, I think Koji, you were asking about headcount strategy, especially in R&D. This is about making sure we have the right talent to really capitalize on this moment. We're probably going to be growing and investing in R&D. From a process lens, we are looking at every single workflow across the business and reconfiguring that to be AI first. Really excited about those and the opportunity ahead.
With such a big change, one thing I think about is culture, maybe near-term risk, but also long-term benefit. I love the speed with quality. For any technology company, that makes a lot of sense, and especially anybody working in software development, speed with quality is paramount.
As much as you can, how do you think about building culture from here going forward within the company?
Yeah. One of the things that's really interesting is I've been building software for 30 years, and I've been working with software engineering teams. I came up through a product and engineering background, and so I've seen a lot of software and how teams build software. Even within GitLab, it's been interesting to watch how the engineering team has evolved in accepting new ways of building software with agents. Even within GitLab, I see the full spectrum of attitudes, skill sets, and approaches. Right? On the one hand, I have an engineering team that will be shipping next week at [Transcend], who literally writes and delivers more code than the average of the organization by 20 times. 20 times.
They have the luxury of a brand-new service built from scratch in a modern stack using AI tools, and what they've built is phenomenal. I have other teams that are struggling because they have an enormous amount of legacy code that was built over the last 10-plus years. There's technical debt there, and they're using AI. Their usage of even our own platform, Duo Agent Platform, has accelerated them two to four times faster than our historical average. That's awesome to see. I also see others in the engineering organization who feel like AI is a threat, AI is taking over their jobs, reducing the importance of skills that they've spent decades building, and I understand how that can feel.
At the same time, over 30 years, I've watched how the engineering practice has changed so many times that I feel like this is just another evolution of the same thing, right? We've tried to give the coaching, the encouragement, the space to adapt toward these new modern ways of engineering. To your culture question, what we're now saying is, "Look, we've identified what it looks like to be highly successful in agentic engineering. We have several of these teams that are going multiples faster than our historical norms. Let's snap that as the cultural icon.
Let's find ways to train and enable and give tools to everyone to reach that new standard, because that's going to not only benefit you and your careers, and your ability to harness the best technology to build software, but it's going to help us prove our platform, live it first as customer zero, and share with the rest of the world that depend on us how to take advantage of AI tools in this new era. That was what Act Two was about, was taking those bright spots, those early wins that are already within our organization, and setting that as the new standard for everyone. That's how we got to where we're going.
[Transcend] next week. Be excited to tune in. As much as you can tell us, what should we be focusing on? I know there's somewhat of a new pricing model you alluded to on the call, Flex. Tell us as much as you can about Flex, because I know you're going to talk about it a lot next week.
Maybe I'll talk about some of the cool technology.
Yeah
Talk about Flex.
Yeah.
Next week is going to be amazing. We're going to have incredible customers on stage, partners on stage, and do demos of a whole bunch of new innovation that we've been building out over the last quarter. I'm so excited about it. You can expect to see, for example, one of the architectural bets that's really unique to GitLab is the first part of our name is Git. It's the same as GitHub, our competitor, based on an open source layer where code gets stored and version controlled. GitLab is the number one contributor to that project in the world today. I think three of the top five contributors are GitLab employees. We have decided that that scale of infrastructure that was built for humans is not going to meet the needs of agents.
We believe agents are going to push it 100X beyond what it is capable of doing today. We've joined forces with an AI lab, and we are building a new architecture and a new set of capabilities for agents for that infrastructure layer, and we'll be demoing it next week, with our partner, at 100X scale. It is impressive to watch. You'll see both Duo Agent Platform and Cloud Code, other external agents driving volumes of code that is impossible today. That's an exciting one. We're also going to be debuting our new Orbit service. GitLab Orbit is we take all of the data inside GitLab, everything from all of your code, obviously, all of the connections to that code, so the people who wrote it, the changes over time, the plans and bugs against it, the security scans, the builds, everything.
We stitch it together into this graph. We then provide an API for agents to read that context. The reason that's so important is agents thrive on context. The better quality context you give them, the better quality outcomes they produce, and they can do it at lower cost. You'll see just how much quality and cost GitLab Orbit provides next week. There will be several other amazing demos. I don't want to steal the whole show. Those are some of the highlights to look forward to.
Just on Flex pricing, not much to share here, you'll have to tune in. As we're thinking about the future for our customers, it's really about providing optionality. We're really solving for cost, value, and predictability. Flex pricing will be a contract that will allow customers to have both seats and GitLab Credits, so to be able to access our consumption-related products, which we'll be talking about some of those more next week. We are really excited about this offering. We've had early conversations with customers. I think they're very excited about it. Our sales force is excited to sell it. We'll come back with more next week, so please tune in. Follow up with all the fun financial details when we get to Q2.
Bill, I wanted to follow up with you on the AI lab that you mentioned. Both bullish, but I want to be measured here. Anytime a company says AI lab, we could kind of get over our skis. I want to stay measured and I really want to ask around, why would an AI lab want to partner with GitLab, because a common bear thesis out there is an AI lab could just build the entire software development life cycle tool chain themselves.
Yeah. Sometimes non-technical people look at how powerful AI agents are, and LLMs are, and think, "Oh, well, they can build anything," right? Theoretically that's true, but practically speaking, it's a lot harder than it looks. As I mentioned, for this infrastructure layer where we're partnering, we are the number one contributors in the world. There's both domain expertise and historical momentum, and buy-in from the community that they get by working with us. Second, we actually are the Git provider for a number of the AI startups and AI labs. In particular, this one is a customer of GitLab, and it's the respectful and beneficial partnership in action, I think, as we work together, because we're also a consumer of their products and services. Third, I think, as I said before, we support hundreds of thousands of organizations around the world.
We have more than 50% of the Fortune 100 that use GitLab today. They see us also as a distribution channel. When we can support agents at 100x load, that's going to mean they can drive more volumes of tokens through their agents. It benefits them by partnering with us because then their agents running on our infrastructure go way faster and lead to more value for everybody.
I can't believe we made it 26 minutes without talking about Duo Agent Platform or DAP, that you guys always talk about it on the calls. You guys gave a $20 million metric. It's a CRR, consumption run rate?
That's right.
What exactly is in that $20 million? How do we think about it? With any time a company gives a metric, what is the expectation on the frequency of when we might hear this metric?
Yeah. We're very excited about this, but it is an early green shoot. Essentially what a consumption run rate is it takes our committed DAP Credits plus overages over 28 days and annualizes that number as a run rate. We are very excited about it, but again, this is we're a quarter in, it's data that is just a signal of product market fit. It's a signal of we're excited that the product has launched. It's doing very well. Just I think one point of clarification, because on the call, there was a couple of questions about this. It doesn't include any of our prior AI products, Duo Enterprise or Duo, but it does include a small minority of conversions from those products into DAP. I think the big takeaway is that this is additive.
I think the other thing that Ashutosh highlighted at the beginning of the call is that first quarter out, we have higher ARR from Duo Agent Platform compared to either of our prior AI products combined. Again, very excited about it and as the business evolves, we will continue to come back with metrics on a more frequent basis.
Not a quarterly metric, but a milestone.
A milestone metric.
I think that's a great way to think about it.
That's great.
Yeah.
Okay. Duo Agent Platform's been out in the market for some time with your beta customers, and it went live earlier this year, and so how are you going to market with it, and how should we broadly be thinking about adoption, consumption? How does it show up in the model?
We go to market both product-led motions as well as sales-led motions. On the product-led side, we introduced $12 in premium credits for every premium seat and $24 for every ultimate customer seat, which allows engineers that are already in GitLab to begin to experiment and try the platform, without having to have their organization make a commitment. The organization does have to unlock the feature, but once they've unlocked it, there's no monetary commitment required. That is kind of a product-led growth motion. The sales-led growth motion is we have now enabled our Salesforce this quarter, Q1, once again, our first quarter, to go and sell Duo Agent Platform as repo-side, so server-side agentic engineering across the software lifecycle.
That is in contrast to what Cloud, Cursor, Codex do, which are more client side or developer side authoring the code, where we run on the organization or the server side where the code is stored to do all of the actions, not just coding, but all of the security, all of the planning, all of the pipeline remediation on the repo side. It's a complement. Our salesforce often goes in and helps educate the customer on the use cases that we provide that are different and additive to what Cloud or Cursor, whatever their tooling strategy is, and how those two can come together to accelerate the full software engineering lifecycle. Then there was another part of your question I'm missing around.
I forgot, too.
Okay.
In the last 30 seconds here, I wanted to ask you on security. Security offering is very good. Very strong from GitLab. What is top of mind for your customers around software development and security?
Yeah. You probably see in the news a lot of software supply chain attacks going on. Hackers or attackers are using LLMs now to discover and exploit software vulnerabilities in record time. Agents make that easy, too. It is more important than ever that companies put security practices in place, not on production code, but before the code ever ships. Because once it's in production, you're exposed and the hacker's time to find that and exploit it is lower than ever. What GitLab does is we provide the real-time security scanning in the code pipeline before the code gets deployed. That's a really powerful value proposition and always has been, but even more critical today.
Got it. We're all out of time. Bill, Jessica, thank you so much for doing this. We appreciate it. We'll see you soon.
Thanks, Koji. Koji, thank you.[crosstalk]
See you next week.
Thank you so much.
See you next week at [Transend].