Datadog, Inc. Aktie

All right, everybody, let's get this started. My name is Koji Ikeda. I'm one of the software analysts here at Bank of America. Absolutely thrilled, and thank you for joining us for the day 2 keynote lunchtime keynote. To have Datadog CFO, Dave Obstler with us. Thank you so much for doing this.

Thanks for having us. We appreciate it. Thank you.

Thank you. So we have a lot of people in this room. Thanks again for joining us. And I guess there are some people that know Datadog very, very well, but there's also many in this room that might not. And so let's start very, very high level. What is Datadog. What is the core problem Datadog is trying to solve today?

Yes. So Datadog has an observability and security platform that allows the deployment of modern software applications mainly in cloud environments in a safe and effective way -- most of our customers are those that have mission-critical software that's customer-facing. .

So think about video or credit card companies or banks or airlines or hotels, all of them have digital applications that interact with their customer and the Datadog platform is used to monitor and secure the effectiveness and operations of those platforms. Most of this is in real time and Datadog over the years has had an expanding platform to cover more of the surface area in examining what's happening in those applications and allowing them to operate in a good way for the customers.

So your results and fundamentals are accelerating. Clearly, good things are happening. And so why is observability and security becoming more mission-critical software complexity and AI adoption acceleration .

Yes, definitely. AI adoption is a part of it, but essentially, we are observing the development and the production of modern applications, mainly in the cloud. And as they get more and more complex, and as more and more of those applications are moving from legacy technologies to the cloud and be modernized, that is what Datadog does. So what is the effect of AI. One of the things is anytime there's been new technologies of which certainly large language models are one, there has been more of an impetus to modernize the tech stack and therefore, create more applications that are in the cloud and that creates business drivers and has historically created business drivers that have enhanced the data business.

This applies both to non-AI-native companies who are modernizing their tech stack in order to have large language models in their applications as well as a set of infrastructure companies who were called AI natives who are experiencing a demand cycle and have rapid product releases, they're cloud native.

Their whole stack is modernized. And they're using in a very significant way Datadog products to help observe the delivery of those products to their customers.

So 1Q results A little while ago, but still yes, --

Fond memories.

It has been a couple of weeks. But it's a -- I think our note started with low, right? A fantastic results all around accelerating growth. winning new customers, you never thought you would have before. And so let's tackle the first part about just accelerating growth. Looking over the past 6 months, what sort of inflections were you seeing from core observability using out there?

Yes. So this has been going on for -- I think we've been accelerating for 3 or 4 quarters. So we've been communicating this message that -- we have seen a good buying environment that the investments we've made in our platform, we can talk about the number of different products, we're resonating. So we're getting more platform growth -- and we also are having a significant demand cycle in AI native companies.

And this has been building on itself. We've also been investing substantially in our go-to-market over the last year in order to deliver this to our customers. So this has been building on itself. And essentially, in the first quarter, it continued that trend of acceleration in many areas, the non-AI natives, the AI natives, different geographies, enterprise down to SMB and then all those were all contributing factors in producing the first quarter, but the seeds of that started 3 to 4 quarters ago and have been building on itself.

Let's talk a little bit about the multiproducts. You guys give a lot of metrics 2-plus products, I think 4, 6, 8, 10. I think I got that right. So yes. So what are the products that are driving the most multiproduct sign out of those metrics, the 2, 4, 6, 8, 10, which is the one we should be focusing on .

Yes, I mean you should essentially -- the benefit of Datadog is that you can do all of your observation actions in a single platform. So in some ways, it's a single product, the platform. At the epicenter and this has been going on for some time, you have the -- what we used to call the 3 pillars, which are infrastructure or metrics, application monitoring or traces and then logs.

And we've had a very substantial demand cycle from what we call digital experience. And this is taking how an application interacts with customers from the back end all the way out to the mobile device, et cetera. That's called -- that's RAM and that synthetics, et cetera. And that -- those are bigger products for us.

Those have been growing very rapidly, and there has been a consolidation away from both point solutions and do-it-yourself towards our platform. That has been enhanced by some other products that we've put on our platform, including our cloud security products our service management, which is basically interacting with the users to be able to manage cases, et cetera, what we call products that allow you to A/B test on different applications and determine what's most effective.

So we've been adding on additional products on top of that. And then we started to add on what we call AI for data dog and Datadog for AI.

So Datadog for AI is most of our business is created because there are many things that impact an application. What I just mentioned plus databases plus network and now you have LLMs and other things. So we've been working to monitor those, and they are being adopted as well as AI for Datadog, which is how can the platform gets smarter, and service the customers, and these are things like our bits SRE.

And all of those have been developed and are starting to get traction, which is enhancing what had happened over the last 3 or 4 years in the core pillars.

So infrastructure monitoring, APM logs are all big ARR business?

They're all over $1 billion share .

Remind me out of the other products, what sort of metrics you gave on scale. I think there's a bunch that are $10 million plus -- and -- is there the potential for some of those to beach $100 million $250 .

Yes. We've been doing this. We've been giving a lot of metrics. Our Roman synthetics have passed through that, and we gave metrics on that. we gave metrics, I think that security passed the $100 million. What we've been doing is, over time, as we reach these metrics generally around 50 and 100, we have been giving those metrics.

So we had the passing of 100, which was the ones I mentioned, security, then we had a number of other products that were reaching the other -- the 50, which were things like database and network. I'm not sure I have all of this exactly right.

And then we gave metrics. So we have a lot of other products that are passing through 10 and multiples. And so yes, we have a lot of products that have been scaling this. And what we said was there are lots of opportunities. And what we're going to do is we're going to give -- as we reach these milestones, we're going to give those metrics so that everyone can follow along.

And the things that show a lot of promise are Bits SRE. These things like the product analytics we talked about, which is really about how an application is constructed and interacts with clients. These are all smaller products, but the TAM there and other point solutions are much larger than we're at today. And so we're optimistic we can scale those different milestones as well.

Yes. In the quarter 1Q results you guys won a lot of big -- I mean, you guys have many big deals for a while now. So what's going on there with the big enterprises? Is it the go-to-market motion getting better in the enterprise? Is it the enterprises need something more like Datadog? Maybe it's a confluence of both? I mean, help me understand the big deal activity.

Yes, it's definitely a confluence of both. So you have in a customer base that has been around for a long time. So therefore, they're not cloud native. They didn't just get birth. So they have legacy technologies. And they've they have a long way to go. There's somewhat 25%, 30% of workloads in the cloud right now and modernize.

So they are continuing to find use cases and modernizing Datadog's platform is getting bigger and better. So we're consolidating market share onto our platform. And we're getting better about delivering the enterprise service model whether that be channel partners, customer service, technical help the whole ecosystem to be able to deliver.

So all of that has been what we've been investing behind for some time that is bearing fruits. And that's resulted in some large lands. Some we still are largely a land and expand, some very substantial expansions. And when we've given our customer examples, if anybody is curious to go back and look at the scripts, what you'll see is a great combination of what the economy is, you'll see insurance companies, financial service companies, tractor companies, all sorts of different car companies, airlines.

So you'll see how this is evolving in the spread of the business towards cloud native and certainly AI natives, which we'll talk about, but also the cloud nativity within very large traditional enterprises.

Is there any limitation to the type of company or size of company that might not look at Datadog anymore? Or is Datadog available to all types of companies?

Well, so there are some companies that have in their past wanting to want to do this themselves. There are very few. I mean maybe I think Google is a good example of trying to do things themselves. So it's not -- that's not really core to our business, although -- and we'll talk about it. There's examples where we've gotten those types of businesses, and we'll talk about that.

I think the other thing is if we generally are delivering our product through the cloud, so if you require an on-premise solution for regulatory or other reasons that has, by choice, not been where we've concentrated our R&D. We are developing more of those products. So you can see companies which because of either their practices, or the regulation cannot have data leave their premise. That would be a company that is not core to Datadogs end market.

Last quarter, you guys talked about winning some AI labs within some large tech companies. Let's talk about that. So what happened there? Why did they come to you? What were they looking for? And how are you guys hoping to solve that?

Yes. So as background, so I think we -- there's a lot of information we've given a lot of information about how pervasive the AI business has been. And this includes some of the foundation model companies, companies and database companies that are vertical.

So already Datadog has been used pretty pervasively in the monitoring side. So production work environments, inference production. And I think we said over 650, and we gave a lot of statistics on spending over $10 million and 10-plus products. And what we added to that is that 2 larger companies, a hyperscaler and another very large tech company that have model creation within their businesses, foundational models had used Datadog for training as well.

Most of the time, our end market has been for production workloads. But as a couple of things have happened as there is a boundary between training research or training and when it has to go into production that we've now been able to have some customers buy from us.

And in this case, there were some large customers hyperscaler who traditionally does more things on their own, but use Datadog. And what we found is this is an example of the fact that they may not be using Datadog pervasively, but there are use cases where there going to be using Datadog, and that's a great voice of confidence that these companies whose whole businesses this are using Datadog, so it's sort of like a seal of approval.

I mean you mentioned earlier in our conversation that Datadog does well with Inference. And now you're saying training too.

Maybe we're saying we have, but we're not -- we don't believe -- we haven't said the -- we generally don't overpromise. So when we have like a certain amount of training that is spread out we'll tell you, right now, it's more of a sort of centralized. It's good, but we have said most of our revenues we expect to be from production and inference. But we'll see what happens. We'll bring everyone along.

As we think about AI in the future and more enterprises organizations, everybody building their own things, large small language autos for I mean -- how does Datadog think about that opportunity and maybe even going after it a little bit .

We'll prepare itself for that opportunity. I think that for the most part, when you think about production, so we're always going to -- I think we're always -- even if we're in training will always be somewhat proximate to production. So there could be market extensions. But essentially, you use Datadog when it can't go down. .

So if you're training, yes, I mean, if you're training and by definition in the sandbox, you're not putting in production. So you have sort of different impetus. Maybe that will happen, and we certainly are preparing our products. They're the same products. We certainly will have the products.

And we certainly are in touch with customers -- and we certainly will push that if it makes sense for customers, but we just don't know the answer whether that's going to be a core market or a specialist market for us.

So when I think about observability in the most simplistic way. It's data ingestion and analysis. But I know it's much more complicated than that and the architecture is very important in the way to address that. And so maybe help explain to those in the room that are again less familiar with Datadog what is it specifically about the technology architecture that takes this what could be a simple complement concept into a very complicated, something that you have to invest in and difficult to replace.

So 1 of the things before we get to the architecture, 1 of the things is data, Datadog, okay. But it isn't true. There's not one data. We have 1,000-plus integration. When you think about the operation of a cloud application in real time, CPUs, GPUs, databases, lines of code, network, all sorts of things. .

So one of the things that -- and this gets to the architecture, that Datadog did very early as they developed a common architecture to take all that data from all the different parts that could affect and organize it and put it in one place and make it transparent. So you can see that is very hard to do. And that's the reason why point solutions don't really make sense because none of the customers are saying, I want to see what happened to that line of code.

They say, I want to see what happened to the functioning of the application. So you have to see all of it. So that's 1 thing. So then the architecture is that data dog organized that data, knitted it together provided user interfaces and other ways to see it in real time. correlated.

Now this is being enhanced by AI now, which is using large language models to do diagnosis and maybe even 1 day sulfur mediate and produce it all knitted together. And that's hard to do because there are a lot of factors. And many other companies have tried to do this piece mill, and it's all gone back to that integrated data model putting the analytics on top of it, having a simple but not simplistic, meaning everybody can use it.

We don't charge by sea. We benefit when everybody goes into this utility and uses it, and that helps us. And so all of that architecture has been an architecture, which is -- provides the most value to our customer base in analyzing. And it all has to be real time because this isn't like, okay, I produce a marketing collateral, there's an error in it, I can fix said, this is your whole business on the front end.

So that's what's created it. And then all the different pieces, adding on all these different pieces and knitting together have been complex. Right now, we have a competitive advantage and that we have a very large platform. It's scale. It can handle anybody's scale. It basically organizes all this for everybody and it allows you to have a significant platform investment to amortize application investment on top of it, which is a big competitive advantage.

I wanted to dig in on the Datadog AI strategy, but also the data for our AI strategy. So let's get the data first. Right. What is so special about the data that Datadog has. And how long do you think it would be even if it's feasible for a competitor to amass that type of data to be competitive .

Yes, I wouldn't be feasible at a price point that would be competitive. In other words, large language models are by definition large. And what they're doing is they're looking at lots of data. So our strategy in the model side is to offer that, but also then have a set of models that are very specific to observability and security use cases and then to make them because of all the data we have, and then both have a higher functioning model and a cheaper model. .

Because if you're not -- if it's not a generalist model, you don't have to pay the cost of all that stuff that doesn't apply to your use case. So that's what we're doing in the model side. Then there's a whole bunch of different things.

So I'm speaking now about AI for data. We'll get to data for AI, which is all the DNA that goes in. Then there's also the ability to connect to all the software creation on the agent side, whether it be an agent or Marsh or a person, we don't care, whatever is creating and getting towards an application to see what's going on there and then correlate that with everything else that's affecting the application. So all of those are things that we're putting in our platform we're having our user conference in a week, the next week. And there'll be a lot of product releases and our Investor Day also had a very strong articulation of this. And what that will enable us to do is and is already is basically making the platform smarter being able to diagnose very quickly what's going on, being able to make recommendations on what to do about it.

And in some cases, to actually implement those recommendations. And that's the vision. That's what the whole service management vision is in the model. So that's like the investment in AI for data to make the platform enabled to see all those things and use large language models, whether they be the third party or our own to be smart. .

Let's talk about your AI products, the bits AI products Actually, I don't even know how many you said. What do you have? And how should we think about that.

Okay. So heading first into monitoring data flows. So we have LLM observability. So that is the functioning of an LA model Think of it as you have databases, you have code lines of code. So that is where you have LLM in a production model application. And you're using signals to understand is that affecting the performance of the application.

And I think we've said that, that is germane to having LLMs in production. And we have we've had significant growth there, still early days. So we're getting revenues from that. Then we have GPU, which is essentially like an infrastructure product, but instead of CPU, GPU, where you're seeing how the application might or the model may interact with GPUs in delivering.

And that's like similar to our other products where you have to see how the servers or the GPUs are doing -- and then you have that is sort of an example of data dog, monitoring things that affect. So that's data door A. Then you have AI for Datadog, and those products are the bets products, bits is sort of a general name. This is our mascot, our dog. So that's why we are using bits. It's very cute.

And so for different end markets, so SRE would be the systems reliability engineers, et cetera. So that product is out there. There are 2,000 customers using that. I think we have 100,000 or more investigations. And then we also have products in for development and for security that we're rolling out in the pit suite. We also have, as I mentioned, the ability to connect to the cogeneration through our MCP server.

When that will enable that information to get into Datadog and monitor that. And we also have a bunch of other products, agent [indiscernible] et cetera. That is being able to monitor on sort of the cost and management side, how much you're spending on tokens, who's using it. We have cloud cost monitoring and now we're extending that into agentic monitoring. So that's -- that was a long time. I apologize for that. There's a lot of names, but those are some of the product lines that are being put out to market in those areas.

I want to talk about platform consolidation. Good driver of growth for you guys. What is driving platform consolidation today with the large enterprises. And does that theme of what they're doing today for consolidation just continue into the future? Or does consolidation maybe change?

Yes. So you might wonder why this didn't all this happen already? Why isn't -- why didn't everyone just buy everything in Datadog to begin with, and it's already preconsolidated. The reason is, one, Datadog didn't have these products in 10 years ago. .

So there are other products out there and there are contracts that customers have with those products. So we're in an environment that it's happening over time. And why is that happening at all? It has to do with the -- that if you are a practitioner and you need to operate in real time.

You do not want to be context shifting. You don't want to be going into a lot of different data sources. It's slower. It's actually costly, more costly. So as we've developed these products and clients have looked at the utility and they've gotten off of contracts, this has been going on for some time. And I think as the product suite has been getting better and better and better, the components all getting to product parity and beyond.

It's been accelerating. And yes, I think we're very early on in this. There's proliferation, there's a lot of observability point solutions out there. Like I said, all of the decision-making is leading towards consolidation in the single platform. So we think we're pretty early on in that trend.

Do your buyers still think about -- and we'll talk about it in the 3 core applications Infra, APM, log analytics. Do they still think about that as 3 separate products or the customers beginning to come to you and saying, please solve this.

No, they've -- for a long time. We've been selling as credit, so you buy $2 billion of Datadog. You go in and you use all this. No, they've never thought as different products. We're doing that in order to provide transparency to you all. It's one product. They don't care what they're called. They only care about doing their job, systems reliability engineering. So this happened a long time ago. That's what made Datadog and they think of it as the platform.

I want to touch a little bit about security. 10 million-plus business -- it's been out there for 3, 4 years. A couple of years now. So maybe for those that are a little bit unfamiliar with here, you tell us about your security journey, what you're doing on the product side? And maybe more importantly, what are you doing on the go-to-market side? That's very a very good thing.

So first of all, you have to say what security, okay? So -- these are the things we're not in endpoint security. There's some really good companies. We're not in network security. We're not in e-mail security. What we're in is cloud security. Cloud Security has 3 components. It has how the cloud is working, it's called posture management and set up to secure digital applications.

Two is cloud SIM. How are you using logs and other information to investigate what's happening? And 3 is code security. How are you engineering security into the code. And so for -- so not on-premise, not legacy, but for cloud applications, we've built this suite of the 3 products. I would say the 1 that we probably made the most progress on is cloud SIM.

Some of the reasons are it's off of our logs business, and we have an over $1 billion log business. We have a great largest observability logs. And the end market has dynamics where we've been an innovator in it.

So one of our -- so we've had 2 major strategies. One is attach the whole suite to cloud-native emerging companies and 2 is attach cloud SIM to more traditional companies where we have a strong observability logs. So that's been the strategy and it's working. In terms of -- that's the product strategy.

In terms of go-to-market, particularly for enterprises, it's a different go-to-market in that one, you have the influence of a buyer that is not a traditional buyer, the CS and even though they're getting closer together, that is a different buyer than DevOps.

And for some reason, I don't know, I'm too young. But for some reason, there was a distribution -- bottoms-up distribution that facilitated direct in DevOps. But security is a sort of highly central -- has been a highly centralized and if CISO has the [indiscernible] So it's a different buyer who buys through channels.

So what we've been doing is we've been working on selling our direct to our champions, but also investing in product experts or specialty salespeople who are going to try to penetrate CSOs and others and channels where we have a channel program like the other security companies. It's still early. It wasn't our DNA, but we're investing behind it as part of the overall security investment.

One thing that we've heard over and over from customers and partners is I'm the observability buyer that guy is a security buyer. Right. Is that how it's always going to be in your view? Or do you think one day that might convert?;

We think the whole strategy is that it makes no sense -- there's certainly territorialism. There's certainly -- but essentially, it's better -- vol up here, he would say it's idiotic not to design security into applications and to have all this come through.

So dev tech ops, we have seen signs and we believe it will come closer to closer together. A thing that we think will accelerate that is Agentic coding because that's going to speed up everything, and then it's going to require everyone to work together. So even though you're right, there are these separate buying groups.

We're seeing signs of them coming together. And we believe in the future, since we believe that businesses are in the and logical that they will come together, and if we can use the gains in technology from agentic content of detection of penetration, remediation, that will most likely help us in our attempt to scale security.

So in that vein, when you say genetic coating, it's getting pushed out from getting from the software development side and security has to deal with it -- does that feel like the you're beginning to pull in some of the security budget because of where that velocity of...

Progressive companies. We have been for some time, meaning that we have clients where it's all -- it's like I said, it's all knitted together. But you have a whole big world out there. And so it's a process. I don't know if I can say that in the last 6 months, we would see a sea change. We think it's going to -- it's an evolution, and we're -- and we believe it's going to happen. It's going to happen over time.

You guys are highly successful with cloud-native companies and cloud native strategies -- at your Investor Day, you talked about Cloud Prem product that you have -- so can you tell us a little bit about Cloud Prem, what's the strategy there? And how are you leaning into go-to-market with that product?

Well, essentially, we're leaning into it and go to market in the same way, meaning that if a client would find utility or a cost benefit from having data kept in their -- on their own servers. We want to have a product.

And so far, it's been that we have the analytics or we have the log and you keep the data. So we've also have products that allow the data to get in an efficient way of everybody pipelines -- so -- but -- so that's what we're doing. Now we're investing behind it. We have customers that want it, and we've been successful -- it still hasn't been the vast majority of the way our customers want to buy.

But what we're working towards is in difference, meaning that if you want to keep the data, great. If you want to give us the data, great. If you want to have all the functionality over to your side. And so that's taking time to get complete product parity where we have exact functionality.

That's taking time, and that's what Cloud Prem means. We're working on that. That's mainly for the first use case lows.

I got you. I wanted to ask you a question on durability of growth. You guys have great growth trends. And so how do we think about the drivers of the durability of the growth, whether that's cloud migration, platform consolidation and AI and what are you guys seeing that's giving you the confidence that growth continues in the passion it has .

Well, the durability depends on what your time frame is. Okay. So we always have believed and we're seeing that this is a very long time investment cycle. -- because such a high percentage of applications and infrastructure are still on legacy technologies.

So we've always said that if you want to look at this, this is a secular trend. And it can be cyclical, but it's also secular and it's got a lot. I mean you have 70% plus of workloads that aren't in the cloud. So we believe this has very long legs. In terms of is it going to be a straight line, meaning on every month, is it going to have the exact same thing.

We don't know. It's likely if proven you're going to have periods of investment, you may have periods of optimization. It's cloud software, it's consumption cloud software. So we can't predict exactly what the line is going to be in every moment, but we have confidence that it's very durable and it's very long because we're attached to huge market drivers.

Now we also -- if history repeats itself, we found that new technologies, of which AI is happening is a huge accelerant of this conversion. And so again, it's still too early. But if history repeats itself, that is likely to inflect the line upward faster to get to the same place 25, 50 years out.

Yes. Yes, that makes sense. I wanted to ask you a question on how Datadog thinks about product velocity and R&D investment and frankly, your guys' own use of AI within the organization. So you mentioned -- I think we talked about it yesterday, you got about 4,000 developers with an organization. So how do you think about leveraging AI and investment?

Yes. So step back, we've always been a product-led company we've been out investing we're bending over $1 billion in R&D. That's had tremendous benefit, meaning it's allowed us to invest in the platform at scale and the functionality.

We also have our company that has no shortage of things of functionality that we want to roll out. So that's been like epicenter to Datadog. Out invest everyone in R&D. Look at the pipeline of what we want to put in the market at a profitable price and creating value for clients and just look at your R&D resources against that.

That's been what Datadog has done. So what about coatings coding aid agent. So that is essentially another piece of raw material that we are using to try to accelerate the launch of features where -- we're not -- what do you call it, code math, it's agent Maxine, what's the total in [indiscernible] we're not talking [ maxes. ] We've never paid anybody rewarded anybody on the number of lines. We basically think about products. So it's all product, all those software developers, we call product people.

And so essentially, to the extent we can become more efficient in speeding up feature release. It's good for us, and we're finding that. We're basically experimenting around the interplay within that envelope on R&D between tokens and people. And we have a lot of experiments. We have places where we have keep the people. Let's see how fast you are with less tokens.

No. Constrain the people. Let's see how fast you get. And we're doing -- we're pretty nerdy engineering, wonky. So we have all this stuff going on, and we're trying to see what's going to happen. We believe that the componentry of R&D will shift somewhat. We don't know exactly to tokens from people, but we don't know the exact amount.

Have you been able to use AI internally with all that development that you have to help out with your own homegrown solutions in sales and marketing and G&A at all.

So we have AI -- there's 3 or 4 major use cases. One, the product itself. We talked about the models and the products. So those would be components of the product. Two is accelerate product release through software coating tools. 3 would be use AI for productivity of the employees and 4 would be what you're talking about. .

Are we able to use a large language models or AI to improve the productivity and the effectiveness of the different functions. And yes, I mean, there's many, many examples. We're using AI to automate the creation of deals and the processing of deals. To enable our salespeople and our market people to understand propensity to buy and direct our salespeople in places that have a higher chance of success.

So we have all of those -- I'm not going to list all the names, but there's lots of them like that. How do we enable salespeople in product training and things like that? How do we speed up all of the back office functions, whether it be legal, accounting, cash cycle, marketing collateral, all of that.

Yes, we have experiments or projects going on in many areas to try to do that. And what we're really thinking about mainly is how can we convert repeated repetitive administrative tasks. Towards more insights so that we can act faster and inflect the business.

So we have all that going on too. I mean we're not -- again, it's not token. We're not -- basically it's -- you've got to produce something from this, but we have a lot of opportunity. .

And remind us, what is the investment strategy in sales and marketing from -- how do we think about that? .

Yes. The investment strategy in sales and marketing is bottoms up. to be able to cover the high potential customers around the world. It has to do with investment in enterprise, all the way up government and enterprise biggest entities all the way down to start-ups on a lot of different to be able to cover them comprehensively and to be able to get lead gens from them, et cetera. then it has to do with geographic expansion like many U.S. companies, we were most developed and followed where the investment in cloud software was most intense was the U.S.

And we're finding many areas with great payback Brazil, Korea, India, et cetera, where we did not have on-the-ground presence. And we're developing packages of salespeople. Sales engineers, marketing dollars, maybe even data centers to be able to do that. So that's sort of where it is. In addition, the way to reach customers direct and then also channel that we're expanding.

So it's a bottoms-up business plan aimed at that micro level and how we can reach the broadest swath of customers around the world.

I got you. I know we're running up on time here. And so maybe M&A strategy. You guys are not afraid to go out there and acquire things and how are you thinking about your M&A strategy? Any changes in what you've done in the past -- and how do we think about that going forward?

Yes. I think we basically to date, not acquired streams of revenue, but acquiring product capabilities and the people. And we continue to do that. So when you think about building product -- we often build product and then we can enhance that through acquiring a product capability.

And that's the epicenter of what we do. And if there's opportunities, we'll continue to do that. In addition, I think we're open to something that bigger where we do acquire some customers as well, but that will really be dependent on can we integrate it in? And can we accelerate? Does that help us?

And so we're pretty principled on that. One of the things we really insist on is that the R&D team and the product teams stay at Datadog. So that means for certain companies sell move. We don't want that. We make it in the incentive structure. So we only acquire companies where they want to stay.

So that's sort of what we've been doing. It's been very successful. We've created some very big businesses. through that with not that big acquisitions. And I think that's the core with the reservation that we could look bigger to the extent it fits in, in our disciplined way.

I got you. Yes. Last question for you, David, and thanks so much for doing. Yes, you got DASH next week and so -- we're trying to get a little bit out of you of what -- as investors in the room and myself as I look at the schedule, is there anything I should be focusing on just to make sure I don't miss anything big coming on.

Yes, was background, DASH is a place where we make a lot of product releases we do on product strategy, it's for users. And so I think you're going to get -- you get a pretty good road map. And then if you look at who's speaking and everything, I think you'll see is going to have no surprise, a lot of AI content. So some of the things that we will be discussing here the AI for Datadog, data the dog for AI, con, all that well, I think will be fleshed out a bit more at DASH next week.

Got it. Thank you. We're all out of time. Thank you so much, David.

Thank you. Good interview.

I can't even see any of you guys -- view in the middle -- Olivier is obviously a much more interesting and important person than me. But for those of you who don't know me, Peter Weed from Bernstein, I cover software. There's actually 2 of us from Bernstein who cover software that you might know, Mark Moerdler and myself. I do kind of IT infrastructure, dev type software, which is kind of my long-term background have been kind of around it since the late '90s, and you may know Mark, my colleague, he actually was a database entrepreneur back from the same time. So he covers databases and mostly like functional line of business applications.

And we are really fortunate today to have Oli from Datadog join us. Oli is 1 of the founders and the CEO of Datadog, a company that has been really instrumental in category that has really kind of emerged over the last decade that we now call observability, which is all about helping like when you ship a cloud application, you expect it to run. And this is a company that helps ensure that it's both up and it's running at performance before you even put it into production, you can predict how it might behave.

Now I recognize the audience there's probably going to be a variety of background in data and kind of where they are. I think it may be relatively obvious from some of the stock price action recently that things are going pretty well.

Oli, I thought it might be just interesting to kind of reflect on the last kind of 3 to 4 quarters and the acceleration that you've seen and kind of where has that really been coming from?

Yes. So I mean, look, First -- thanks for having me here. We have seen acceleration for the past few quarters. I mean, I won't bore you with the exact numbers. We deal in the filings. But we've seen acceleration. What's the most exciting to us is that it is not a specific customer, specific set of customers, specific side of the business. We've really seen acceleration across the board. And the way we look at the customer base today is we look at both the -- what we call the AI natives, which are largely companies that were started in the last few years built on AI or companies that were selling a little bit before that, that are the -- essentially the large builders and the most important building blocks of they are today.

So we see acceleration in that category, which sort of makes sense. I mean, everybody knows the AI is happening right now. So -- but what's even more interesting is we see an acceleration of the business outside of that. So in the non AI companies. And for us, that's a mix of the older cloud natives, which existed before AI and also all of the larger enterprises, whether they are large enterprises or mid-market, and we've seen acceleration across all of that. So on the AI side, obviously the AI companies are consuming a lot of infrastructure, building a lot of applications, running a lot of applications, and that's what we see on day-to-day basis.

On the rest of the market, we see also more applications, but we also see some growing adoption of AI itself. So we have -- we've disclosed in the last quarter that we've seen very large increases in volumes of our AI-specific products, whether that's the traffic we see to our MCP services or whether that's the traces we get into LLM observability product that really gets traces produced by AI. So we see inflection across all those points.

Oli, I think you've done a really nice job kind of helping people kind of understand like how you get paid and how you kind of ride the cloud wave. Maybe maybe describe like how you think about like where your value comes and like where your growth equation kind of comes from over long periods of time?

So the value comes from helping customers be on top of the complexity they create their applications. So meaning they ship applications, they have to understand what infrastructure is part of that application, how it's running, whether the application itself is running, if it's fast enough, what the users of that application are doing whether the application is creating the right outcomes for the business, whether the application is safe and secure. So all of that, that's what we do for our customers. One way to think about the growing importance of what we do is that, over time, developers have become way more productive.

So when you go back 50 years more than that, developers were hand coding every bit on punch cards and writing every single piece of code that was running-- then after that, there's been higher level languages, better interfaces, compilers, then after that, you've seen open source software. You've seen cloud, you've seen SaaS and internal -- at every single step of the way, you see an increase of productivity. What it means when you get higher productivity is that you produce less or you produce more in less time. which means that you don't really get to [indiscernible] as much to it, you don't understand what's actually going on.

And you see an even bigger acceleration of that today with the coding agents, where engineers can in a few minutes, with open application. They just have no idea what's going on. They probably don't even read the code, but they combine all sorts of different components into an application. So that increase of productivity creates a dramatic increase in complexity, and the problem we saw for our customers is we actually understand that complexity. We manage it for them. And in the future, we want to be even more an automate basically the way the applications run after the [indiscernible].

It's interesting. I think like 1 of the bare cases that has been pervasive around software is like now that we actually have these agents this complexity can now be handled and can't we just go ask whether or not it's Anthropic, Claude or any of these LLMs to just do it for us, how is my system doing? Tell me when it's down. Like, why does Datadog still have a role in a world where we have these AI agents? And maybe it's not today, but maybe 3 years from now, they get smart enough to do this.

So I mean, the 2 things I'll say. First of all, the companies that are building these kinds of models are also using our product to manage their infrastructure and their applications and everything else. So it is a hard job. It is consistently a harder job than writing the application itself. And that is why there's been this race has complexity over time. And everybody is going after it, including the leading companies AI [indiscernible] AI builders all the hyperscalers. So that's 1 thing.

The other thing I will say is that the kind of problems we saw are somewhat different in nature from what these fundamental models or financial models are built for. and operate at very different levels of data volume and real-time requirements. So we have 5 or 6 orders of managing more data than which is typically put in an we need to operate in the real time. So the analogy I would give to understand where we stand with the prime we saw versus what these models do is compared to self-driving cars.

And so mean today, it's working pretty well. You have self-driving cars everywhere. You could, if you wanted take a photo from your dashboard on your car and upload it to Claude and ask what you should do. And it will give you an answer, and it will be a pretty good answer. And we'll tell you actually you should make -- if you turn on the right, and that's why, and this is what's going to happen -- the problem though is that Claude will do that, but it will not drive your car in real time. If it did, it would do it poly, were worse than the algorithm that's or the mall that's running in your car.

And also, it would cost you $10,000 a day to drive your car. So that's a way to understand what these models do relate to what we do in our case, for observability, automating the life cycle of an application, automating the availability, the validation, the security of an application.

I think the other bear narrative like when you get through that, oh my gosh, it's actually a difference between like deterministic programmatic software and kind of probabilistic software, it's like, okay, well, I still have these code suggestion tools. And we've got some open sores like why can't I just like to ask it to build me this bet a programmatic software late, you're obviously not seeing that happen, like what's the disconnect between that narrative in the reality of like why the most innovative companies are turning to use Datadog as opposed to do it themselves.

Yes. I mean it doesn't make economic sense for you to do it yourself typically. And the reason for that is this is -- what we provide gives enormous leverage. So typically, for any dollars customers spend on us, they're going to spend $10 or $20 on their cloud provider. They're going to spend $20 to $100 on their engineering team. with the rise of cutting agents and [indiscernible] in general, maybe some of that $20 to $100 depending on their engineering team is actually going to go to these AI models instead -- so we're talking about a huge amount of leverage.

So when the -- and by the way, we help our customers make the most to optimize their spend on their cloud provider, on their engineering team, on their AI levels on all of that. So we're basically the small part of spend that helps our customers make the most out of the rest and accelerate and make more money, so at the end of the day, it makes no sense when you have them to do that. I'll give you a few more examples that we've had, so more recently. So we mentioned over our last earnings call that we also had a number of hyperscalers static adopting our products in their case for helping with their the development and training of their AI models.

And these are companies that culturally basically don't use any commercial software. They write everything themselves. They use open source, they have to completely homegrown by nature. Even those companies had to come to the realization of that, actually, it didn't make sense for them to do that. They would get better outcomes. They would get faster delivery of what matter the most to them. They would get better economics of it, and that would be better for the business in the long run. We didn't build Datadog to serve a handful of hyperscalers. That's not our business model. A business model is going is to go after every single company out there, have products with very broad appeal.

We think though that this is a great example of these hyperscalers just going for the first time, maybe through what every single other company has to think through, which is, okay, so what do I need to own? What should how do I get the best return on investment? What am I trying to achieve? I need to get growth, I need to get better economics at scale? What's the best way of doing that. I have finite resources, which should I focus on. And I think that's what we're seeing at play here.

So if -- can't be replaced by AI, just asking questions can't be coded, this place still has a tremendous number of start-ups, like I spend a lot of time on Hacker News, for better or worse. I would say, every month, there's somebody who's like celebrating the fact that they're trying to build some new observability platform. And you and I were talking about this a little bit, but beforehand, my history, I'm an old product manager -- years ago at Microsoft in the middleware category. And the 1 thing that has stuck with me this whole time and when I worked at McKinsey and other places, is that I always watch the buyer. And for me, what I noticed is that the greatest chance for a new entrant coming into a market was what the buyer changed, right?

So like you could say like, why does Salesforce exist and replace prior CRMs. Well, because the buyer moved from the CIO to the head of the function. Why is Datadog, I think, an amazing company that replaced prior generations of what we might have called monitoring software. Well, because we got the site reliability engineer and a new set of expectations and requirements when we move cloud delivery and management. If we look at AI, it appears, and I think you've made a good case for when we shed in the past, like the roles might compress again.

There may be several roles that need to get redefined if we're really going to have high velocity shipping security might need to be grouped in as well. And that looks like 1 of those scenarios kind of like the site reliability engineer that collapsed some old roles together and gave you an opening into the market, but I think you guys have been thinking about this almost from day 1 about like why that type of change actually is kind of like what you're almost hoping for because of the upside as opposed to being a threat.

So a couple of things on that. So the first thing I'll say is I mean there's a -- I think you understand when you say there's another new engineer building and observative product every month. I think it's every 2 weeks -- and it's been like that since the very beginning of the -- so I still remember the first fundraising meeting I took in the -- with VCs way back in 2010. And the first word that came out of the mouth of the investor where all monitoring because that's what it was called at the time, monitoring credit market. .

And then it went down here from there. The -- and it's always been like that. And I can't blame people for doing that, and that's what I did. I mean engineer, I started an absorbing company, and that's naturally I think we did approach it a little bit differently though. So our starting point was not, hey, let's build a better product [indiscernible] we're using that, we had it. We're going to build a better one. Or we're using that is too expensive, let's build something else. The starting point for us was, hey, I used to come from development. My cofounder used to come from operations -- technical operations. The 2 teams fought all the time -- and we thought, okay, so , maybe we can bring them together and have 1 platform that bridges the gap between the rules and things like that.

And that vision actually lended itself very, very well towards adding more roles, bringing more people into the mix, expanding the footprint over the lifetime of the company. And that's what we've done. We were actually quite lucky because -- what we didn't understand when we started that was that the collapsing of the role at the time between DevOps, we're at the center of cloud adoption. The whole DevOps movement was born at that time. And that's when you saw those rules become closer. And we saw that pull in the market. but you see even more of that happening today.

I mean you see securities being brought in, you see with the advance of cutting agents, now instead of large deals with specialists, you're going to have smaller teams with people wearing many hats across product, design, engineering, security and many other functions. So I think we're going to see even more on that. The last thing I would mention because you talk about resisting disruption. And we built also the company very deliberately in a way that keeps us in touch and honest when it comes to the low and bleeding edge of the market. So we have more than 30,000 customers. But the bottom half of those 30,000 customers represented about 1% of our revenue. So we do not serve that bottom half for money, it actually cost us money to serve those customers.

We do it because those customers are -- they are small companies, they're individual students. We also have people on the free tier that we only have included in that. And what those people do is they keep us honest with respect to the simplicity of the product. They also help us see what's coming in the market because we see they adopt all of the new stuff way before the large companies do that. So we see that coming. We get many of what we need to build for. And whenever we break things because we try hard to accommodate our largest customers that have very sophisticated needs and that make our products more complex. We see -- actually, we hear from those smaller customers, and we also see in the numbers that really something is not working as it should be working there.

The alternative is what most software companies do, which is they quickly understand that most of the money comes from the higher end of the market. As a result, they focus to care mostly about their large depressed customers. and then they end up being on the path towards future escalation. And before you know it, you end up with products that are what I call enterprise abomination, and we all live with them every day in the office. We know exactly where they are. And I think that that's -- you don't come back from that basically. That's where we become irrelevant over time.

I think we've focused a lot of this conversation is like some of these like bear cases around the people have. And -- maybe we should turn it a little bit to some of the opportunities, right? So you all don't rest. Obviously, I think you spend more in R&D than pretty much the entire rest of the sector put together. So you're shipping a lot of things. And if we look historically at the S curves that the business has gone through, maybe you started with metrics, actually really kind of collaboration and the metrics and then APM and then logs, and these have each kind of grown to being kind of $1 billion type opportunities. There are several things that you've been putting out that you, I think, have been very hopeful that can become some of those next desk curves. -- whether or not it's security, whether or not it's AI monitoring, the GPUs, modeling that you guys have been putting out. when you look at the acceleration that's going on right now, how much can you kind of thank those new things for this versus kind of your existing footprint?

What's interesting is most of the acceleration, like mathematically, it comes from the existing stuff. And when you think of -- like we have the I think we said like 14 of the top 20 AI companies using us, including the very largest of those. Like they use the same traditional products as the rest of our customer base. So by and large. So that gives you a sense of the shape of the market in general. When you look at the observed category, so there is gotten a number that we put every year, but we're #1 there was ability according to Gartner in -- or at least in the closest -- the category had as closer to observability, I forgot the exact name of the category. .

And -- but we only have about 13.6% of that market right now in a market that is growing pretty far -- pretty fast. So it doesn't take a ton of imagination to see where there's a 5x from there just from the core capability, looking at infrastructure applications, end users around those applications, logs and all of that stuff that's that. The new part of the market is everything that relates specifically to AI. Part of that is what we call AI for Datadog, which is who we can do more for our customers and really expand beyond the observatory category. So basically automating the whole for them, not just being in the business of telling humans where things are going, but being in the business of fully running and automating the application, it's promotion to production, it's repairing it when there's an issue, making sure it's secure at any point in time, there's a lot of automation we can do with that.

Another area is what we call [indiscernible] which is observing and understanding all of the AI components our customers are using. And mostly it's fall into 2 categories. One is the AI models our customers are using within their applications or within their agents, which are nondeterministic models and which can't just be tested in development and then validate a ship to production, you have to keep observing them all the time. Like they're like humans, you have to get to know them and you spend time all the time and you keep watching, and you see what's going on and you keep adjusting. So that's 1 big area.

Another area is adapting to the whole new world of agentic coding when developers can produce a lot more applications, a lot faster. But would they still come do faster is getting those applications to production. Now they still need to make sure that those work that they do what they're supposed to be doing, that they provide the right outcomes for their business that they are secure, that they keep working once they've been put into production and when other people are also making changes to the world around it, so that's a huge -- right now, we see a huge increase in the volume of co-change that are being made and applications that are being shipped, and there's a big opportunity for us there, too.

I think there's probably like 2 kind of natural places to take this. Maybe we start with the Datadog for AI. So if we think about some of the things that are going on, they're obviously like there's some very basic uses of AI, but as we start to get to agents look, maybe beyond coding copilots, there isn't like a lot of depth of use of agents. But as we get more and more of that, there are some kind of unique observability problems that come up with essentially monitoring like the behavior of the agents over time. And there are some companies that have come out, things like LangChain and BrainTrust and these types of things that are designed to kind of tackle some of that job, like how do you see Datadog working alongside of or instead of some of these kind of born and AI components that are there to observe kind of the behavior of agents?

Yes. So I think there's 2 extremes to that market. There's 1 side which is everything that's in production. And when you talk about things that are in production, it doesn't make any sense to separate the AI from the non-AI components. For 1 thing, what the agents do is -- so they have a lot of reasoning, but also they use a lot of tools. And the tools are actually apps. So when you monitor the whole thing, you basically end up monitoring like largely apps with a little bit of AI in sprinkle. So when you're on production, it doesn't make any sense to have different tooling, and that's something that we will -- like we develop, we have an element [indiscernible] product for that reason and something that we will want to own in the end.

On the other side of that spectrum, you have what happens in development. So when developers are tinkering, they're playing on their laptop, their testing various things. Historically, that side hasn't been very platform in that in a company, you don't have 1 of them and everybody plugs into it, but whether you've had many of them. And these were more a matter of choice and taste for the individual developers. So the equivalent there could be the IDEs, the developers we're using. I'm old enough to have leave some of the AMAX versus VIAs. So I remember people being very opinionated about the editors they use and things like that. And whereas absorbability, you have only 1 production application. So like everything has to plug into 1 environment to 1 thing in the end and that's platform in nature.

So what's not completely clear to us today is how much of that is they've clearly developed a purely local non-platform lots of it of usage in any company, some homegrown, some and source, et cetera. And what part is in of basically back there, we have to go to own fully the production side of things.

So when you kind of look at that opportunity going forward, I think perhaps 1 of the challenges is it's very dynamic, like how we want to do all of this stuff is somewhat of a work in progress. How do you think about your own investment in kind of being part of the frontier there, so you're in the right place at the right time when things start to solidify and become a little bit more common across organizations.

Yes. So I mean we spent a ton of time like watching our customers and what is they do and how they work. And so we have the luxury of catering to every single layer of the stack in the customer. So most of our business is with large enterprises. But we also serve a lot of start-ups in AI, whether they are super large-scale model makers or the companies 1 level below that are largely AI native and building on AI. So when you look at the largest, it's interesting to see how they work but it might not be completely transferable to the rest of the market because those companies have unlimited access to inference for 1 thing. And also for a number of them like almost vertical wall of demand in front of them.

So they are not in the situation that both of the companies are in. So they might make different choices and that might the way they operate might not carry across. When you look at the tier below that, though, like the companies that are, I mean, still very successful going fast in AI, but are still buying most of their influence from somebody else and have to make some hard prioritization goal in terms of what would they work on and not and have more relatable growth for the rest of the market. I think from there, there's a lot we see and we can learn in terms of where the world is going.

We see the trends pick up like a few quarters before the rest of the world might and we get some interesting insights in terms of how our products are being used and we need to be able to support those companies as they do that.

And I feel dumb. I feel like I should have almost even asked the question in a slightly different way because I realize for the audience, they might not fully appreciate your product-led approach to the organization, which you're kind of describing some of the outputs of product-led is something that I have been kind of in love with ever since like I got enamored with Atlassian flywheel approach background in 2010, and I tried to apply it to several businesses like building it out of Dropbox and things like this. like maybe help people understand what is special about that product-led approach to the organization with the growth function and with product management being so tight with discovering these opportunities and delivering and why that allows you to stay so innovative?

Yes. So the whole business is built around bottom-up product adoption. So we -- our product is typically adopted by the record files and then the people who actually do the work. We build around a unified platform, which is very important, meaning every single new product will build as part of that platform. Whenever we do M&A and we acquire company we do platform. So everything is part of that tightly integrated in 1 single platform. We charge per usage. So the whole business model is usage. I mean there's a few tiny parts of the revenue that are based. That's because we compete in categories that are purely based. But the vast majority of our revenue is completely usage-based, which also is interesting because right now, it allows us to I mean basically, we don't have any transformation to undergo to get into an AI edge where the notion of seed gets disrupted and you have to charge for outcomes for value per something.

We're already usage base, so we can very easily adapt our model. Everything around the adoption of our product is -- everything around the business is geared towards the adoption of the product. So when we land the customer, we're already deployed typically and we just see the usage start growing from that time. What this gives us as a business is a great way to understand which products to build and not build for our customers and what is valuable or not. Again, we use age based. We [indiscernible] about having that usage or having new products being attached to new SKU, meaning that we get very clear signal about what's valuable and what's not in our products. which allows us to keep developing a lot of the right things.

So that's -- and if you follow the business, were knowing the industry for very quickly expanding the product footprint very quickly, improving the existing products and be very good at growing new lines of business. And we have a lot of tasks in our earnings around the adoption of those businesses.

The other things we get from having this product-wide approach to the adoption and bottom-up adoption, is that we have a very efficient go-to-market. So we spend significantly less as a percentage of the top line on go-to-market, which then the typical enterprise business is which allows us to invest about 30% of the top line on the R&D, which then feeds the flywheel of building more product and building more differentiation and being relevant for the long term.

Maybe the asterisks on that now has been that you are actually starting to build some enterprise sales capability. like what drove that evolution where you keep kind of the product-led core, but you realized that some enterprise overlay was important. And how has that kind of worked in...

Enterprise has been around for a while, like we built that team many years ago. So the -- all that's baked in. What's important to understand is that it's enterprise but bottom up. So it's never the case or maybe I can happen once. But it's never the case that you call up the CIO and you're super impressive in your golf game and you end up with a deal that gets done, that's not will happen. .

In our case, we start with the teams, the teams adopters. And then we make sure that we connect between the teams that are adopting us and the leadership in those enterprises, so that they understand what is their buying and what the ROI is and things like that. And so that we can enter growing and longer-term agreements with those companies. But even with the large enterprises, we mostly land very small. -- the ASPs when we land are in the, call it, mid-5-figure to low 6 figures every year. which is very small for this kind of enterprise deals. And then we grow those customers for the very long term. And we announced on the earnings call, we typically have very interesting land deals or consolidation deals we make in the 6, 7, 8 figures annualized. But most of our deals start more and they grow and they -- we tend to have very, very efficient sales, thanks to that.

The 1 thing that we're doing today that is a little bit different is the new sales motion we're adding is that we're adding some overlays for selling security very specifically to the [indiscernible] because that's not the most we had before. And we find that it's a little bit different -- different enough to warrant a slightly different team to handle that. But that's specific to security to a CISO in the largest enterprises.

And by the way, maybe talk about that a little bit like why is security such a natural part of what you're doing? And obviously, you have security vendors that are also making some acquisitions into what looks like competitors in your space. Like how do you see that playing out between like your role in cybersecurity and what some cybersecurity vendors are doing?

Yes. I mean, look, the end of the day, the thesis we had when we started the company that you should bring Devon ops together also extends to security. It doesn't make sense for security to be separate. -- the teams would work well together, they should speak to same language, achieve the same role. And we see the same -- we see this accelerated by the adoption of AI, where you'll have the same person basically having to carry to wear many hats in care about security as well. So we think that, that bottom of adoption from the developers and the ops people is going to reach into security. And is, in the end, going to be -- it's going to become much more of a bottom-up motion than a upon motion in terms of how that adoption.

From a technical perspective, most of the signals you need to secure an application, you get observability. You have everything that happens in the production environment, you have everything that happens on the way to production in the core, in the testing environment. You have the behavior of the end users, you have the behavior you have the developers. You know exactly who's changing what and when, so all of that is the information that you need to fill into the security models, and we're uniquely positioned to get that as well.

So we went down the path right now of like some of the expansion opportunities organization and the commercial motion and why that's really helped you be successful. But I think you pointed out there's like the other side, which is AI for Datadog. And while it's still relatively early, there's kind of several investments that you're making there, everything from the its AI agent to some of the new modeling stuff with Toto that you guys are doing. Maybe help the audience kind of understand some of those things that actually can add incremental value, reduce the human time, add more predictability that you guys can do?

Yes. I mean, the high-level goal there is to automate right, and to do more than observed and to really automate -- so 1 thing a lot of our customers go through is so they use us to observe and monitor the applications and something [indiscernible] 30 a.m. and we work them up and then they start Zoom call with 20 people on it, that last 3 hours from 3 a.m. to 6:00 a.m. The site is down, business is upset. I mean you can imagine -- Yes, very costly, very disruptive, not anyone's favorite part of the job. So there's a huge opportunity to automate there.

And it used to be fairly hard do that. But with the new technologies in AI are really opening doors that were just not quite available before. So what we do today is we have products under operations side and on the security side, they do that automation. We have a bit, that's a name. So sorry, agent BI security agent. And what they do is they run investigations for you. So for example, in the case of the outage I mentioned earlier, instead of having like a 3-hour Zoom call with 20 people on it, 4 minutes in at an analysis that says, "Oh, by the way, I find out what was broken. This is what this is what it is. These are the 3 people who know how to fix that. This is the fix I propose, not sure we do it.

And I mean that's already life changing for those customers. And typically, this is the way they like they see they turn this on, maybe they forgot the tuning on and then the gain this incident. And then they get on to that long zoom call, and then after the Zoom call, they look at their notification and they were asked, "Oh my god, 4 minutes into it, you had it already. So -- and that's the light of moment from them and they start that. We have a similar agent on the security side that does security investigations. And there, the focus is mostly to cut the noise. So the teams that do security response have tons and tons and tons of investigations to run. 95% of them are more even are just benign.

And we -- a big part of the job there is to make sure to get to the super quickly, so they can focus on other things. And so we do that as well. So that's what we do today. If you squint, you could say, hey, some of that you could also do from you use cloud or and you give them MCP tools to acquire your data and maybe they can come to the to the similar reasoning and come to a similar conclusion. The next level of what we're building though is about having that intelligence directly inside the data plane at with all of our real-time data and to drive the automation itself, so models that are specialized that can run on very large volumes of data at a very attractive cost and with very low latency, so we can fully automate the operations.

There are some first outputs of that. You can see, we published 2 versions of a time series model like a financial time series model, which is called TOTO and you can look for it online. This model is very interesting because it is a model that is -- that was trained on observatory data almost exclusively, but that generalizes very well to other types of time series. So it can actually -- like it's state-of-the-art across domains, not just for viability. And so it's actually really good at what they're forecasting even though it was trained on mostly application of the ability data.

What's also exciting about it is that it's a model that scales, meaning that you can train a larger model from the same data set with the same architecture and even the same hyper parameters and you end up with high performance. And that's not something that has ever been achieved in time series models before. We have been achieving very famously in language models, starting like the most visible case of GP2, and we know what happened next. I think there's -- we start to see the same opportunity with these models. So now we're busy basically extending those models beyond time series, when I say time series. -- are mostly timestamps and values. We're extending that towards including the rest of observed data, whether that's logs and traces and application topology and network data and user data and all of that stuff, so we can really fully model and be predictive on our customers' infrastructure. And the idea there is if we build that properly, we can automate a lot of our customers' operations.

And I think the point being that like an LLM is designed to kind of represent human language. This is a completely it's a semi structured set of data that you would love to have a model like an LLM, but now this is a machine learning model optimized for that. As a result, its performance is much better than you can ever get out of LLM.

Yes. And also, these are -- like these are very different types of LLM are very bad time series for example. And so these are -- the architectures are even though the models are transformer based, so a lot of deep learning, transforms a lot of it is similar, but the architecture of the mall is actually fairly different.

Yes. Well, LLMs don't understand ordinality. So like you need a model that has that as a structure of it for this to work in different ways. We can obviously go much deeper on some of these things. But before we run out of time, I think it's also like important to perhaps reflect on some of the deployment model flexibility that you've been providing customer I mean it's almost like exhausting when you think of like the level of innovation you guys have been able to put out. I think that you could probably help us reflect on like from a buyer standpoint, 1 of their frustrations when they're forced to have a single deployment model that may not be ideal, whether or not it's for data residency or credits they already have. So maybe help us understand some of what you're doing with bring your own cloud and some of these models.

Yes. I mean -- and just to zoom out, customers in general, they face just an explosion of the amount of data and applications and everything, right? So -- and so there's -- in absorbability, there's a perpetual quest for efficiency, and making sure that would you spend on measuring auditing, rating your application remains predictable and within range of what you were expecting. For that, there are 3 core mechanisms. One is you need to have a feedback loop that lets you understand what you're consuming and what's valuable and what's not and what you need to do more or less of. And that's something we built big part of the product is making sure that the right people have the feedback loop at the people who care and who capture their information, see exactly how much value they're getting out of it and how much is costing them. So that's 1 part.

Another part is to be to get ever smarter on how much of a sample you need to understand the data. And that goes back to all the smart building the models, et cetera. etcetera. And the third part is to keep innovating on how you can, how efficient you can be at storing and acquiring and managing the data and all the various topologies you can employ to make that work for various customers. So on that last one, One thing we started doing last year, and we see quite a bit of demand for and we're accelerating on is, we are doing some we call bring you on cloud options for our customers to deploy our product. meaning that they can store the data on infrastructure that they manage themselves even though we still run and update the application for them.

And the reasons for that are -- so we see cost at large scale is 1 of them actually have petabytes of data, that's something that you probably won't. Other reasons would be data resiliency lows, and we see the world in general is fracturing into smaller zones as opposed to one big go. So we expect a lot more of that in the future. Even though today, it's still a native for most customers, we expect that to be more front of mind, maybe 1, 2, 3, 5 years. It's hard to tell when exactly. And the last reason might be that customers have -- maybe they have a large data center, they already built and they want to use. Maybe they have a large commitment with a specific cloud provider, and they want to use the what they've committed already for that. Like there's all sorts of reasons why customers might want to control more of the deployment there.

Historically, we had focused on being purely SaaS and there were a few reasons for that. One is, I mentioned earlier that we thrive on being product-led and product adoption, meaning it's very important for us to be able to iterate very quickly. and also to understand what customers are using and not using and what's valuable and valuable. .

So we never offered an on-prem version of our software early on for that reason. Like we thought it would be a huge tax on innovation. And also it would make us a lot more a lot number in terms of who we build product. And we've seen that play out. Like most companies that start having a product that has some success in the enterprise, get offers, they can't refuse early on of having buildings on-prem versions of their product. And that's a great idea in the short term and a horrible idea in the long term because those companies end up slowing down a -- the way we built this technology now is bring you on cloud, allows us to have the best of both worlds because we still manage and run and update those applications and meter those applications. But at the same time, we can run a data plan fully within control of our customers and on their infrastructure.

And between this and some of the stuff you guys have done with FedRAMP perhaps this even leads to some of the opportunity you could see the scale around because like if I look at many of the other software companies I cover, somewhere between 7% and 15% of their revenue come from federal. And I think you guys are -- if David's comments earlier, we're accurate closer to like 1%. So you could see even just on that 1 customer category, the type of revenue opportunity that it opens. We only have a couple of minutes left. You talk to a lot of investors, be talking a lot of these different events. What do you think the most underappreciated aspect of Datadog's forward-looking opportunity is?

I mean, there will be 2 halves to it. 1 is the world is exploding where they are right now. And this means so much more stuff so much more complexity, so much more infrastructure. And we see that play out with the large dialytic today, but that's going to come to the rest of the market. And so that's a huge opportunity for us. If there's only 1 category that remains, and I know everybody here has been debating the software ever going to be worth anything ever again. Well, I mean the answer Genies yes. But the -- if there's only 1 category that is left in the world, it's observability because the 1 job that remains for us humans is to control the machine and understand what it's doing, understand whether it's delivering the right outcomes, how much it's costing and whether the intent of the -- of what we wanted to do is actually what would get performing in the end.

So I think that's a huge opportunity for us. There's a lot to be there. It's a challenging market because it's changing really fast. So there's a -- that muscle we built on product innovation, I think, is going to be brought to bear, but that's -- we see that as a huge opportunity. The other half, the more boring half is that all of that is driving the need for standard observability. Those large AI companies are consuming large amounts of infrastructure and applications in the same way the other companies are -- and as I said earlier, like we have less than 14% of that market, and that market is growing and is going to grow faster, I think, now as the -- as you see this huge CapEx investments in infrastructure and general and it's not use GPUs anymore. It used CPUs and everything else as these agents need to use tools.

And so I think we -- that part of the business, the boring the existing part of the pan, that's not new form factors, your plication or anything. That part has another 5 or 10 x into it, and that's very exciting.

Well, you heard it here first. The only business that will remain after the robots take over everything else is observability in Datadog. So make sure that you're ready to be an SRE in your future. Oli, thank you so much. I really appreciate you taking the time here and sharing with the audience your experience and kind of where Datadog is going.

All right. Thank you.

Welcome, everybody. Thank you for joining. It's a great pleasure to be here with Olivier Pomel, CEO and Co-Founder of Datadog. Olivier, thank you for joining us.

Great to be here.

I think we'll start off on a great note. This time last year, you were on stage with us. We highlighted that you were one of only 4 enterprise software companies growing mid-20s plus. We said something very rare was happening at Datadog a year later and looking back, I think maybe we understated that a little bit. You're now growing over 30% at a $4 billion scale. So can you help us in layman terms understand the problem that you're solving out there? Why it's so critical for customers and what do you think the phenomena that are fueling your growth at scale?

Yes. So I mean, look, we do observability and security. So we sell to engineers and product teams and our customers. We help them understand whether the software, the services that they're shipping are actually working. They're working as appropriate for their customers, if they are fast enough and if those products deliver the right business value for them and for their customers. So that's fundamentally what we do. We serve every type of company from the 10-year startup, the newest AI company all the way to the largest and the oldest enterprises and with pretty much everything in between.

The reason why we see demand, I think there's 2 aspects. One of them is we're in still fairly early in a super cycle of digitalization and cloud migration. So we started the company 15 years ago. It was right at the beginning of the cloud migration. That's still ongoing. And that's still a big driver of our business and still something that's going to keep going for many, many years. And then the second aspect is, I'm sure it's not lost on anybody in this room that everything is changing with AI. There's a lot more that is being built that is being shipped in software. There's a lot more interactions that are being automated. And all of that creates new kinds of complexities and new kinds of surfaces that and also, frankly, quite a bit more infrastructure. And all of that needs to be managed, monitored, observed, secured, and that's what we do.

Yes. Yes. I was preparing for this. I want to say you guys made it easy to come with some of these questions because you had such a good Q1. Yes, I think one of the best prints we've had this earnings season, 32% revenue growth rate, as we mentioned, that's $4 billion scale, accelerating for the fourth quarter in a row. Largest sequential add in a while, a very, very healthy raise, really just a lot of strength across the board. I'd love to kind of decompose that a little bit. Where are you seeing those pockets of strength? I think it's more than just AI, right? Maybe what surprised you? And what do you see kind of persisting going forward?

Yes. So look, the -- as we said on the call, I think on the earnings call, we saw acceleration across every single part of our business. We saw acceleration with the brand-new AI-native companies, whether they're small or very big. We saw also acceleration with the rest of our business, which is even more interesting. So the non-AI part, all the companies that were around before AI started taking over. I think the drivers are a little bit different there. I mean, on the AI side, obviously, we see that part of the ecosystem blowing up, like the AI is getting into production, some use cases just coding are very real and scaling very fast.

And this is feeding a number of large model companies but also all sorts of application companies that are being built around that. So I think the drivers are fairly clear there. What's even more interesting for us is that the bulk of our business, the rest of our business that is -- that existed before AI became a thing. That business is accelerating as well. And part of that is those companies have to -- they understand they need to modernize and modernize faster so they can be ready for AI. Part of it is just that we're still very early in cloud migration. There's a number we like quoting, which is that I think Gartner has this report every year on the market share in ITOM and a number of other fields. And we're the leader in observability, we're the #1 there, but we still have only 13.6% of the market according to them.

So this tells you how early it is in the market for us, how much opportunity there is ahead of us, even factoring out all of the new developments with AI, all of the new explosion of demand we can see there. And so we think we're still early in a super cycle there.

Yes. Yes. And definitely, some threads will have to pull on in a little bit. But one topic that's been out there for the entire time we've covered you all is this concept of customers doing it themselves, using open source now or building it. And it's been persistent, even though none of the data points really supported that much. I think your retention is stellar. But this last quarter, you guys started talking about getting the hyperscalers as customers, right? And if there was anybody who could do it, they do have it, right? They have these tools within their own ecosystem. So if we think about it that way, like if even the hyperscalers concluded, they need to buy from you rather than use what they have. I mean, was that surprising to you at all? And what do you think that says about the value proposition that you have and even the moat that you've built?

So I mean I wouldn't call it surprising, but it's definitely a very interesting proof point because the way we see it is the reasons to build it yourself are usually mostly cultural. That's because you want to build yourself, you want to have your teams do it or somebody on the team wants it so bad and nobody else around them wants to prevent them from doing that. So that's cultural. Typically, you don't get nearly as good of a result when you do that. For one thing, it takes a lot of time and a lot of focus that you should spend somewhere else. So typically, when you try and build it yourself, you end up with a solution to -- in 2 years from the -- to the problem you had last year. And that's typically not great.

You also end up -- justify what you might think when you go into it, much worse economics, and that's not a fantastic solution long term. That's something that many of the hyperscalers, again, are happy to live with because for cultural reasons, they want to in-source everything. Now what we see happening now is there's an extremely competitive situation right now around the development of AI and I think it really focuses the mind for many companies. When they realize, "Hey, wait, actually, instead of waiting 2 years for this, we can have it now, and it's going to run better and cheaper in the end." So what are we doing? What should we be doing there? So again, our business model in the end is not to serve the hyperscalers. There's not enough of them. They are too fickle also for cultural reasons, as I mentioned earlier.

But for us, it's a great proof point. Even the companies that have virtually unlimited access to top talent and a strong cultural bias for building use our product. It means that it really doesn't make sense for anybody else to build their own.

Yes. Yes. I think that it's an excellent ironman argument for what you guys have been differentiated. And I just had a conversation with a partner where he made a very interesting point that I hadn't thought about, which we're asking about why coding all that. And their comment was AI has accelerated the pace of code being generated, and it's increased the competition because everybody can do it out there. And so the effect that it has is the opposite is now if you don't use something that's really trustable and powerful like Datadog, you might actually be hindering your development process, and that's your bread and butter. So in fact, the effect is that it makes people more reliant on something like Datadog. Do you see that dynamic?

Yes, definitely. And look, most of the -- actually, pretty much all of the top 8 or 10 coding companies or -- coding companies, whether you're talking about the models people use in production or the large companies or the products that are built more for the consumers, all of those companies use Datadog behind the scenes. And so that tells you about the very specific need there is there. Look, at the end of the day, we see there's so much more stuff that is being built, it's being produced so much faster that, by definition, the folks who produce or that have no idea how it actually works.

That's the least understood thing about productivity is that the more you increase productivity, the more complexity you create because folks manipulate way more things in way less time and as a result, doesn't go through their brains and they don't understand what's going on so they need help to actually understand what happened, to actually make sure it works properly, to make sure it delivers what it's supposed to deliver for the business in the end to make sure it keeps working, when everything keeps changing around it to make sure it's secure, and that's what we do.

Yes. I think a great point to kind of get into the AI-native cohort that you guys have 22 AI native spending more than $1 million and 5 spending more than $10 million. And a lot of the focus tends to go on some of the larger ones. But I think the impressive part is how diverse even that has got foundation models, codegen, Vertical AI. So when you look across your book, where in the value chain do you kind of see the strongest opportunity? And what is the differentiation you have for those customers?

So I mean, those customers have like a high pressure to deliver a lot and deliver very fast. And look, the core of what we do is what they all start with. So we cover everything from end to end from the bits that go through the CPU, the network, the GPUs all the way up to the end users, how they're using a product, whether they are coming back, whether they're completing what they're supposed to be doing, how much business value they generate for you in the end. And we cover absolutely everything in between in a way that's fully integrated. If you go to any of our competitors' website, they all say they do it, the reality is it's actually really, really hard and really differentiated to do it well, and that's why those companies all use us.

This is -- when you think of the investments they are making, whether it's on their engineering teams, their research teams, their GPU fleet, all of that goes to waste if you do not build -- understand how to build the right thing and then ship an experience that works for your end users.

Yes. Yes. The need to perform is the highest for them. I think one of the most striking comments that you had on the Q1 call was the change in posture with regards to training. I think the quote was last year, we said training was not really a market for us yet, and we're actually starting to see it become a market. So that's -- it's a big step function change, I think, in people's minds. When did you start to see this happen? Was it -- maybe you don't like the word surprise, but was is it surprising to you guys? And what do you kind of see about that opportunity looking forward?

Yes. So this one was actually a bit of a surprise, yes. So it made intuitive sense to us before that training might become a market and because we saw -- look, a while ago, training was mostly pre-training. It only made sense for 5 to 10 companies in the world to do it, it was large scale. It was completely homegrown not a great fit for building a product typically. But we saw that the technology changed quite a bit. So models went from being mostly pre-trained to being largely post-trained. The post-training was becoming increasingly specialized to different types of verticals. The stacks that are used for post-training also are becoming richer and richer. So now when you post-train a model, you're going to run all of those different environments, basically, you're going to run all the applications you can run, simulate behavior in those applications, capture what comes out.

So you end up running way more complex tasks for doing that. And then we saw -- instead of having 5 to 10 companies doing that now there were 50 to 100. So intuitively, it made sense to us that something might be more interesting there. We were surprised, though, to see a number of different customers, including multiple hyperscalers come to us for training at about the same time and which tells us there's something happening and there's potentially the emergence of a new market there. Again, too early to call it because the technology is changing fast as the markets are changing fast. But if we get into a situation where we go from 50 to 100 of these companies to 500 to 1,000 to maybe more, then that becomes a really interesting market and there's a real problem to solve, and we can do it.

The other thing that's interesting, if you look at the evolution of the technology is we're probably going towards models that can learn on an ongoing basis. So instead of training a model, whether that's pre-training, post-training, if you keep improving your model with online evaluations and online improvement, then that becomes really very much ongoing production concern and something that you can repeat in many, many, many different companies over there. So we are hopeful, interested in that -- it's not the core -- it's still small compared to the inference business compared to everything else that happens in the stack that our customers are running. But it's an interesting new green shoot, I would say.

Yes. Can you -- do you have a sense you said they kind of all came to you at the same time. Do you have a sense of what the trigger was from their perspective? Was it a lot of this stuff going to post training? Is that just something that makes more sense for you guys that don't have solutions for it like, what was the catalyst?

I think the -- a lot of these companies are on a bit of a similar clock. So everybody woke up to ChatGPT at some point and then there were a number of internal efforts started in a number of different places. Some of these efforts worked, some of these don't. Yet you have these cycles that companies live through and when something didn't work exactly the way you want, initially, then you try and reset and do things differently. I think part of it is that part of it is companies being somewhat aligned on that on the same because they all compete in the same market.

Yes, yes. Okay. So let's think about the fact that blossoming out of AI, right? Where we're seeing this move, like you said, beyond just a handful of companies. I think there's been a lot of indications from people across the stack in the software space talking about that hinting at it. I think maybe that's underappreciated by a lot of people. Like I said, a lot of focus just goes to those kind of very well-known LLM companies. But could you provide your thought on that? You said it might democratize quite a bit more, right? So both on the training and inferencing side, whichever one you going to dig into, like how are you seeing that democratization happen? Are we literally in the very first inning of that? How do you see that curve growing?

I think we're still super early. I think the focus for most companies or most of users of AI today is to make sure it works. So everybody is getting from -- step one is, let me make it work once and then step 2 is, okay, so now let me deploy that at scale across my company across similar use cases and things like that. I think we're still very much in that phase. We are not at all in the phase of, okay. So now what else can I build on that or how do I rationalize it? Or how do I optimize, that's for later.

That later might come sooner if we see the explosion of the AI company's revenue continue at the rate it is right now, that money is coming from somewhere. And it's -- there's probably going to be a push for rationalization sooner rather than later. But the mode right now is still very much, let me get it to work. Our mental model for what the market looks like in the end is that it's similar to what you see in the overall cloud infrastructure or even the database market. Like database market you have a number of options up there. You can have -- you can buy a closed-source database that you're going to run yourself. You can buy closed-source cloud databases that you don't run yourself that are completely black boxes or turnkey and if somebody else is running them for you.

You can use open source one that you run on infrastructure somebody else provides for you. You can run the open source on your own infrastructure. You can build your own databases and all of those actually coexist like there's reasons for all of those to exist and customers typically are going to mix and match a number of those. They're going to have different cloud providers they're going to have different database providers, you're going to have a bit of everything in there. My guess is we're looking at a market that looks like that for AI inference.

Yes. I want to get to non-AI cohort accelerating, which I think is, again, a big part of the story, but one last one a couple of years ago, we kind of gave you this analogy, at least that was our thinking, which training is like a bottle rocket and inference can be like a steady compounder to get the time you kind of agreed with that framing. We touched on it a little bit, but do you think that maybe that framework has changed a little bit where the training might be a little bit more of a steady compounder on its own as well?

So it's inching closer to being an ongoing recurring thing but it's still a little bit one-off, like what we see still you have these large training runs or these smaller training runs, but there's still easily run like you do a run, then you do another one and then maybe nothing happens for a few days, then you do another one. And these also tend to be still somewhat like custom coded, like you don't have like a standard way of doing it that every single company out there is using. So it's a big improvement from where it was a couple of years ago where again, only a handful of companies were doing it. It was extremely hand-coded, absolutely not production minded in general like it was kept up basically by people babysitting those training jobs night and day.

And being like very large pretraining runs and then nothing after that. But we're still not yet at the point where it's an ongoing, always on every day of the week, live with customer data kind of operation. I think we still don't know whether the market is going there. We think it might be, but that's for the future.

Yes. Got it. So moving to the non-AI cohort. So the core customers they've been accelerating, I think, every quarter for a little while now. As you've said, as we've said many times, your story is about a lot of customers and a lot of things going in the right direction. But it has been accelerating. So I'm sure the pieces are kind of similar, but what's changed? I mean, a year ago to now, what is going on in this core drivers that's pushing that a little bit higher?

Yes. I mean, look, so we do have some proof points that those customers are starting to adopt some AI in production. It's still small relative to the size of those customers and still a small driver of our growth there. Like we see traffic, for example, in our MCPs and traffic also to our LLM observability product. We see a very strong growth. I think we released some numbers in the earnings call on that. But that's still a small amount, but just give us an idea that these customers are moving there. But the bulk of it for these customers is that they are still modernizing, moving to the cloud. We get into more of them because we have successfully built up sales capacity.

We go to market in more regions and more segments. We get great return on investment on that. And then we've been expanding the product -- set of products. We can sell in the categories we sell into. And we have enough of those products that are reaching product market fit, getting to inflection points in their growth and where we basically see great adoption and consolidation from customers in our product. So I would call it the boring side of the business. It's not AI revolutionizing everything. But it's a predictably high return on investment, very buildable part of the business where we keep building those products, it makes sense to our customers, and we keep investing in the sales capacity because we are still early in what is a very large market opportunity.

Yes. Yes. I wanted to touch on that bit, SRE. Even before you guys have launched that, I talked to a few people and they're pretty excited about it, saw a lot of value in it. I think you guys have a 100,000 investigations since launch, 1,000 customers. Really going well. I'd love to hear what you see on that front in terms of customer utilization. And one thing we do here is that the more autonomous, the technology, the more powerful it is, customers do have a bit of a challenge of how do you integrate that into the workflow, because you kind of have to change how you run your business. So is that something you see as a real hurdle? Or are there customers out there who are kind of really pushing the edge there?

I do. So we see a ton of pull for it, and it gives us a lot of areas to develop in the product. So one thing we keep hearing from customers is I want it to go faster into auto resolution. So initially, we were worried that if it does too much, it creates a trust issue and it's hard for customers to control. But with AI taking hold pretty much in every single part of our customer's businesses, I think that train is leaving the station now and folks are getting comfortable with automation. And so they are pushing for more and more end-to-end automation with it. So maybe -- okay, it's fine. You told me what was the issue? Why it was there? Who introduced it, how to fix it. Now give me a button to fix it or maybe even better, fix it for me and then tell me about it. So we're working on that.

A second area of pull we hear from customers is, okay, this is great. This works really, really well. But I wanted to work across my other systems. So it should work services within Datadog, but also I'm using some other logging system. I'm using some other security tools, I'm using all these different things. I have a lot of open source, can you work across all of that. So we're also pushing so that Datadog actually connects to all those bits and investigate across all of those different systems. Another area we're investing in is getting more proactive and predictive. So we don't have to wait until you run into an incident to investigate, maybe solve it or prevent it even and so there's quite a bit of R&D that goes into that.

So we're developing our own models for that. We've released actually 10 days ago or the second version of our time series foundation model called TOTO and you can look it up. There's a blog post and some results that we've published there. This model is open-weights, but we're also being adopted by the community as well. And what's really exciting there is -- so it's a time series model. It's a general purpose, even though it was trained almost exclusively on observability data, it performs extremely well on everything else. Actually, it's state-of-the-art across all time series use cases, not just observability. And it is a competitive field. Every single large company has time series models. So it's very exciting for that reason. It's also very exciting because it's a first time series model that shows scaling, meaning we can train it with more data and train larger models for longer and we get better performance.

It used to be that it didn't work for time series model. Like we all know that what started the current AI revolution we're living through is the fact that we saw large language models scale, like we saw, and I think it was started with GPT-2, we saw that you could throw more GPUs and more data and you get better results. I think we're getting to that point with time series models. So it's very exciting for us because that's the path for us to get from, okay, we run an investigation after you had an incident to we're going to be predictive. We're going to understand what's going to happen next in your systems, and we can run AI directly within our data plane without having to get outside of Datadog, which is very exciting.

So that change in kind of being able to feed more data that could really kind of bend the curve on the capabilities.

That's what we're working on. Yes. That's exciting. And again, it's research. So -- but if research is good enough that we can open with it, we can open it up, and we see a large amount of adoption from it.

And what do you think that would look like once you start being able to do that, I think, is it charging for like issue that you've proactively resolved? Or how would you think about that?

I mean right now, we charge for investigations for it. Long term, we don't actually know what the model is going to be in part because the broader market is still figuring out how to package intelligence. And so it's not clear yet what customers will relate to the most there. And so I guess we'll see. The short of it though is, for us, it doesn't really matter because we have a usage-based business model in general.

And so it doesn't matter whether we have a new dimension in usage that relates to in this particular type of investigations or whether that gets attached to other parts of usage we have, whether it's on the data volume we process, the number of events, the footprint our customers have in the cloud, like there's a number of different ways to look at it. So for us, it's not a big deal either way.

Yes. Let's talk a little bit about your R&D, your headcount and kind of how you're handling that. You guys are obviously kind of very forward-leaning. We had a discussion last night at dinner about how you guys are kind of thinking about putting productivity and R&D through people and then through code, tokens, however you want to think about it. Can you just frame that discussion and how you guys have set it up?

Yes. So I mean, the short of it is we are currently -- like if you think of what drives or limits our growth. We're limited and driven on 2 sides. One is the sales capacity we have and we're still early there compared to the number of markets and segments and customers, we need to be talking to. So we still have to grow that capacity. That one is still largely human-driven like the buyers are humans maybe at least for the foreseeable future, the humans will be buying. We'll see if that changes at some point, but that means the sales capacity is largely driven by humans.

And we're still growing that as fast as we can. We get great return on investment on that. We're also driven and limited by the number of products we have in relevant categories with the right amount of functionality and quality. And that is driven by our investments in engineering and R&D. Historically, that's been mostly about human labor and how many people are in the team. Now maybe there's a bit of a mix shift maybe you will have the same overall R&D investment, which right now is around 30% of the top line, but some more of it will go to tokens or GPUs and less of it will go to labor, I think it's unclear, but we know we'll keep investing. And we also know that right now, we're also -- we keep hiring. We think we can still scale. We need to still need to scale, and there's still a lot more we need to be for our customers and a lot more demand out there.

When you say that you're growing as fast as you can in terms of the sales and marketing side, is the limiting factor, just your ability to hire people?

Well, when you talk about sales and marketing and sales capacity in general, it's -- you can't just think of it in terms of a big pool of labor. You need to think of it in terms of the right people with the right territories in the right segments, everywhere around the world. And so it's way more of a bottom-up kind of a thing than a top down. And so -- and that's actually really challenging to do well and at scale.

Yes. Zooming out a bit at the end here. Everybody's kind of seen the -- all the models. There's an LLM du jour, it seems like ChatGPT, Gemini, Claude, so on and so forth. Do you see that leapfrogging effect? Do you expect that to continue. What is your view on how that's evolving?

So it's a super competitive market right now, which is fascinating because these are products that, on one hand, look like commodities because you can hotswap them pretty easily and from a far, it's hard to tell which one is better from the consumer. But at the same time, it's an incredibly competitive market where improvement is very rapid. Similarly to what we've seen with the cloud like 10 years ago, like there was a fear at some point that Amazon was going to run with everything, and they would compete with everyone in every single field. We've had a little bit of the same and it didn't turn out that way, right? I mean it's a very healthy multi-vendor, very innovative marketplace where you have these large-scale vendors that provide things that kind of are largely commodities with some differentiation, but not all that much.

And it's a very dynamic marketplace. My guess is we're going to see the same happen with the AI models. I think we're well on our way to that already, there is multiple vendors, they compete. There's a lot of innovation. There's a range of frontier paid versions. There's a range of open source versions that are lagging behind, but not crazily behind. And so we -- my guess is that we're not going to be in that situation either. What's interesting, though, is that for customers and users of those models, it's really hard to understand how well they work, how they compare, whether they still work the same way they used to, whether something has changed, whether something would be more appropriate for them.

And we think it's a big opportunity for us and for observability in general, like our job among many other things, is to tell them, "Hey, that thing you're using, it's actually not performing as you thought it was or it changed or something else changing is better or this is where you can mix and match them and I think it creates a long-term opportunity for us." The same way we've had an opportunity with the various kinds of clouds being mixed and matched by our customers.

Yes. Another vector of growth. I think at our 2024 TMC conference, you had this comment where you said, it doesn't take a lot of imagination to see how we can get to 5 to 10x the size we are today. I think you were about $2.5 billion run rate then and now you're at $4 billion and the comment back then were like, listen, the ITOM market is growing at a healthy rate, and we're winners in that market taking share, so we're going to grow even faster. We've talked about AI in a bunch of different ways today. Do you think that's transforming that TAM that you have? I mean does that change this concept of how big you can get?

So I mean, on the first part the part about the lack of imagination, I can -- we can still grow the same way without imagination. We're still in a similar position. I think I remember that time -- I remember the Gartner number we quoted at that time was around -- had around 10% of the market. Now we have around 13.6%. And so the math that gets you to the 5x is pretty much the same as it was at the time. And that's without entering massive new categories or without huge tailwinds in AI.

Now the thing that's new now is this explosion of AI and we think it opens up a number of other opportunities. In particular, we can do so much more and we should do so much more in automation, which allows us to deliver a lot more value to our customers. And we think observability was only ever one half of a solution so yes, okay, you've observed, but now what would you do? And I think there's a lot more we can do to automate. Yes. So that's the exciting part.

Yes. We're coming down to the last few minutes here. We'd love to ask this question, which is when you're hopefully back here in 12 months, what do you think that the audience is going to know or see come to fruition that you're kind of seeing right now? Like what's going to surprise that you're kind of already seen in the pipeline and not discussed as much as it should be.

I mean, look, the -- I don't know if it's going to be truly surprising to anybody here, but the amount of stuff being built and the way the development cycles are collapsing is creating so much opportunity for us. Just on one hand because of the sheer volume of stuff that's coming up, but also on the other hand, because the value is moving from the act of writing code to everything that comes before and after. So what should I work on? How do I know it's working? How do I know it's safe. How do I know it's actually -- my users actually find value in it. All of that creates such an amount of -- such amounts of opportunity that, again, I don't think it's always understood by the market, especially some days where [indiscernible] these trends, definitely, is understood at those times. But I think it's -- for us, it's a huge driver of short and long-term growth.

Yes. And we've had a lot of such days between now and the start of the year. We've got just a few seconds left. Anything on security, I mean, it's one of those areas that's growing very fast in the background. Where do you see that going?

So it's exciting. I think there's 2 areas in particular where we find great satisfaction over the past couple of quarters. One is code security. I mean that whole field is upended by coding model. And so there's so much more to be done there, and we find a great amount of demand for it. So that's exciting. The second area, we find a lot of success in these -- our cloud SIEM product. And with resonates really well with the market there is a combination of 2 things.

One is it's a really top of the line, event management, storage, log management system that lets you stream data from anywhere to anywhere, storage in all sorts of extremely efficient way, curate very well. And that's because it comes from our observability product. So it's already way above what a pure-play security company would do, for example. And we combine that with the surprisingly good best AI security assistant. I say surprisingly because we were surprised by it, and we find that it constantly rewards customers when we show them that. And it's fairly unique. You don't see anybody in the market that has this combination of the super good data back end with the super flexible with the AI on top of that. Most companies try and focus either on one or the 2. And so that resonates very well in the market.

Yes, full solution. Thank you so much for your time and your insights. Really appreciate it.

All right. Thank you very much.

Good day, and thank you for standing by. Welcome to the First Quarter 2026 Datadog Earnings Conference Call. [Operator Instructions] Please be advised that today's conference is being recorded.

I would now like to turn the conference over to Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead.

Thank you, Lisa. Good morning, and thank you all for joining us to review Datadog's first quarter 2026 financial results, which we announced in our press release issued this morning. Joining me on the call today are Olivier Pomel, Datadog's Co-Founder and CEO; and David Obstler, Datadog's CFO.

During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the second quarter and the fiscal year 2026 and related notes and assumptions, our product capabilities and our ability to capitalize on market opportunities. The words anticipate, believe, continue, estimate, expect, intend, will and similar expressions are intended to identify forward-looking statements and similar indications of future expectations. These statements reflect our views today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially.

For a discussion of the material risks and other important factors that could affect our actual results, please refer to our Form 10-K for the year ended December 31, 2025. Additional information will be made available in our upcoming Form 10-Q for the fiscal quarter ending March 31, 2026, and other filings with the SEC. This information is also available on the Investor Relations section of our website, along with a replay of this call. We will discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial measures in the tables in our earnings release, which is available at investors.datadoghq.com.

With that, I'd like to turn the call over to Olivier.

Thanks, Yuka, and thank you all for joining us to go over a very strong start to 2026. Let me begin with this quarter's business drivers. I'm very pleased to say that our teams executed very well and delivered revenue growth of 32% year-over-year, accelerating from 29% last quarter and 25% in the year ago quarter. We showed broad-based acceleration of revenue growth across cohorts, including both our AI and non-AI customers. Our AI native customers cohort continue to grow and diversify rapidly, both in the number of customers we serve and the scale of those customers. In this quarter, including new land deals with 2 of the world's biggest AI research teams, helping them improve and optimize their training workflows. I'll talk more about that in a bit.

Even more impressive was the growth in our non-AI customers. Non-AI customer revenue growth accelerated again this quarter to mid-20s percent year-over-year up from 23% last quarter and 19% in the year ago quarter. We think this is a sign of strong continued cloud migration, greater adoption of our products, and customers of all kinds accelerating their use of AI. Finally, churn has remained low with gross revenue retention stable in the mid- to high 90s, highlighting the mission-critical nature of our platform for our customers.

Regarding our Q1 financial performance and key metrics. Revenue was $1.01 billion, an increase of 32% year-over-year and above the high end of our guidance range. We ended Q1 with about 33,200 customers, up from about 30,500 a year ago. We also ended with about 4,550 customers with an ARR of $100,000 or more, up from about 3,770 a year ago. These customers generated about 90% of our ARR. And we generated free cash flow of $289 million with a free cash flow margin of 29%.

Turning to product adoption. Our platform strategy continues to resonate in the market. For example, 56% of our customers now use 4 or more products, up from 51% a year ago. 35% of our customers use 6 or more products, up from 28% a year ago and 20% of our customers use 8 or more products, up from 13% a year ago. So we're landing more customers and delivering value across more products. And our business continues to grow. Our total ARR now exceeds $4 billion, and our quarterly revenue exceeded $1 billion for the first time in Q1.

This is a big achievement for all of us at Datadog and is a product of years of investment in building and innovating for our customers. But we are still just getting started. Of our 26 products, 5 are over $100 million in ARR, another 3 are between $50 million and $100 million ARR. We're working hard to build and deliver further growth in those products. And this leaves 18 other products, which are earlier in their life cycles. We believe each has the potential to grow to more than $100 million over time.

Moving on to R&D. Our engineers enabled with the latest AI coding tools are building rapidly to help our customers confidently and securely deploy their applications. So let me speak to a few of our product launches this quarter. Let's start with AI. As a reminder, we're talking about our AI efforts in 2 buckets: AI for Datadog and Datadog for AI.

So first, AI for Datadog. These are AI products and capabilities that make the Datadog platform better and more useful for our customers. In March, we launched our MCP server for general availability. With MCP server, developers access live production data to debug their applications directly in their AI coding agent or IDE.

We delivered Bits AI Security Agent, which autonomously triages Datadog Cloud SIEM signals, conduct in-depth investigations of potential threats and delivers actionable recommendations. We've seen Bits AI Security Agent reduce investigations that could take hours to as little as 30 seconds. We also shipped Bits Assistant now in preview, which helps customers search and act across Datadog using natural language prompts.

Moving on to Datadog for AI. This includes Datadog capabilities that deliver end-to-end observability and security across the AI stack. We launched GPU monitoring, enabling teams to understand GPU fleet utilization, workload efficiency, thermal and power behavior and interconnect performance. This drives higher GPU ROI and operational reliability.

Our customers continue to move forward with their AI activities, and we can see that in their usage of the Datadog platform. We now have over 6,500 customers sending data for one or more of our AI integrations. Though this is only 20% of total customers, they represent about 80% of our ARR.

And our customers' usage of AI within Datadog platform continues to grow rapidly. Bits AI SRE agent investigations have more than doubled from December to March. The number of spans sent to our LLM observability product nearly tripled quarter-over-quarter. The number of Datadog MCP server tool calls quadrupled quarter-over-quarter and the number of Bits Assistant messages increased by a factor of 12 in that period.

While we are aggressively building with and for AI, we also continue to expand the Datadog platform to deliver against our customers' increasingly complex needs. To speak to a few of these efforts, last month, we launched experiments for general availability. Experiments work hand-in-hand with our feature flagging product and combine best-in-class statistical methods with real-time observability guardrails so companies can test for impact, choose among alternatives quickly and ship with confidence.

In addition, our customers now benefit from APM recommendations. By analyzing telemetry data from application performance monitoring, real user monitoring, profiler and database monitoring, APM recommendations automatically identify performance and reliability issues and most importantly, explain how to fix them.

And we announced our plans to launch our next data center in the U.K. We see a large opportunity to serve our British customers as cloud adoption accelerates in regulated industries. Last but not least, we are pleased to have received FedRAMP High certification from the U.S. federal government. With this certification, we can now move forward with federal agency customers that require FedRAMP High to handle sensitive workloads.

Meanwhile, we continue to expand our product offerings, go-to-market teams and channel partnerships for public sector customers, both in the U.S. and internationally. So our teams were hard at work again, and we're looking forward to sharing many new products and future announcements at our DASH user conference on June 9 and 10 in New York City.

Now let's move on to sales and marketing and highlight some of the deals we closed this quarter. First, we landed 2 large deals, a 7-figure and an 8-figure annualized deals with the AI research divisions at 2 of the world's largest technology companies. These organizations are building and training the most advanced AI models in the world. It is critical for them to reduce engineering friction and increase training velocity, but fragmented internal and open source tooling made it harder to identify and solve issues and reduce engineering and research productivity.

By using Datadog, both companies are accelerating their pace of innovation on their hyperscale AI training workloads. And this includes optimizing their workflows using GPU monitoring on large parallel GPU grids.

Next, we signed a 7-figure annualized expansion for an 8-figure annualized deal with a leading online recruiting platform. This customer is centralizing on Datadog to reduce complexity, drive developer velocity and improve efficiency. With this expansion, they will replace a stand-alone tool with Datadog LLM observability to correlate LLM signals with APM and user experience data. This customer will grow to 16 Datadog products, including Datadog MCP server.

Next, we signed a 7-figure annualized expansion for an 8-figure annualized deal with a Fortune 500 bank. With this expansion, this customer will migrate their remaining log data into Datadog, fully replacing their legacy log vendor. Most notably, our Flex Logs give them granular control over costs while meeting strict compliance requirements. This customer uses 10 Datadog products, including Bits AI Security Agent to accelerate incident response with AI.

Next, we signed a 7-figure annualized expansion with a leading global hedge fund. This customer operates thousands of on-prem hosts and network devices. At that scale, their open source monitoring stack has become operationally unsustainable, impacting portfolio managers and investment analysts.

With this expansion, they will replace their entire on-prem observability layer with Datadog infrastructure monitoring and network device monitoring and will have unified visibility across their cloud and on-prem environments. This customer will expand to 11 Datadog products.

Next, we landed a 6-figure annualized deal with a Fortune 500 insurance company. This company's fragmented observability stack led to long outages with incidents spotted first by their customers instead of their tooling. By using Datadog and consolidating 3 legacy APM tools, they expect to move from reactive responses to proactive incident detection. They will adopt 10 Datadog products to start, including all 3 pillars and LLM observability.

Next, we signed a 7-figure annualized expansion with one of the world's largest travel groups in APAC. This customer was using Datadog on one business unit, but in 2 others, they were juggling multiple tools and lacked actionable insights. By consolidating 6 legacy open source and cloud monitoring tools, the customer saved money and improve platform resiliency and performance. This multiyear commitment positions Datadog as their strategic observability provider.

And finally, we landed a 6-figure annualized deal with a leading Latin American fintech company. This customer serves tens of millions of users across critical financial flows. Their rapid growth outpaced their fragmented front-end monitoring setup and outages expose them to financial, operational and reputational risks.

By adopting our digital experience monitoring suite, including RUM, Synthetics and product analytics, they now have full visibility of user activity with the cost control they also previously lacked. This customer will start with 5 Datadog products. And that's it for our wins. Congratulations again to our entire go-to-market organization for a great Q1.

Before I turn it over to David for a financial review, I want to say a few words on our longer-term outlook. We are pleased with the way we started 2026 as we support our customers' inflection in AI usage and application development and as they lean into our AI innovations, including Bits AI Security Agent, Bits AI Security Analyst, Bits Assistant, Datadog MCP server, GPU monitoring and many more.

There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. But we now have an additional secular growth driver with AI as we help our customers deliver more value with this transformative new technology. Now more than ever, we feel ideally positioned to help customers of every size and every industry as well as all types of users, whether humans or AI agents, so they can transform, innovate and drive value through AI and cloud adoption.

And with that, I will turn it over to our CFO, David.

Thanks, Olivier. This was a very strong quarter for Datadog. Our Q1 revenue was $1.01 billion, up 32% year-over-year. Our 6% quarter-over-quarter revenue growth is the highest for Q1 since 2022, and our $53 million quarter-over-quarter revenue added is the highest ever for Q1. That included the strongest quarter of sequential usage growth from existing customers since the first quarter of 2022.

We also delivered an all-time record for sequential ARR added to the quarter. ARR growth accelerated in each month of Q1, and we see a continuation of these healthy growth trends in April. We also achieved strong new logo bookings. New logo annualized bookings set a new all-time record by a significant margin and more than doubled versus a year ago quarter. These included wins in observability and included some of our newer products like security, data observability and Flex Logs. And our new logo average land size also set a record and more than doubled year-over-year as we continue to land larger deals.

Revenue growth accelerated with our broad base of customers, excluding the AI natives, to mid-20s percent year-over-year, up from 23% last quarter and 19% in the year ago quarter. We saw robust growth across our customer base with broad-based strength across customer size, spending bands and industries.

Meanwhile, our AI native customer growth continues to significantly outpace the rest of the business. This group continues to diversify and grow, including 22 customers spending more than $1 million annually and 5 spending more than $10 million annually. This group includes the leading companies in foundational models, code-gen tools and vertical-specific AI solutions.

Next, regarding our retention metrics. Our trailing 12-month net revenue retention percentage was in the low 120%, up from about 120% last quarter. And our trailing 12-month gross retention percentage remains in the mid- to high 90s.

Now moving on to our financial results. Billings were $1.03 billion, up 37% year-over-year. And remaining performance obligations, or RPO, was $3.48 billion, up 51% year-over-year, with current RPO growing in the mid-40s percent year-over-year. RPO duration increased year-over-year as the mix of multiyear deals increased in Q1. As a reminder, we continue to believe revenue is a better indicator of our business trends than billings and RPO given their variability.

Now let's review some of the key income statement results. Unless otherwise noted, all metrics are non-GAAP, and we have provided a reconciliation of GAAP to non-GAAP financials in our earnings release. First, Q1 gross profit was $807 million with a gross margin of 80.2%. This compares to a gross margin of 81.4% last quarter and 80.3% in the year ago quarter. As we've discussed in the past, our gross margin varies from quarter-to-quarter with investments into innovations for our customers, offset by efficiency efforts.

Our Q1 OpEx grew 31% year-over-year versus 29% last quarter and 29% in the year ago quarter. As a reminder, we continue to grow our investments to pursue our long-term growth opportunities, and this OpEx growth is an indication of our execution of our hiring plans. Q1 operating income was $223 million for a 22% operating margin compared to 24% last quarter and 22% in the year ago quarter.

Turning to the balance sheet and cash flow statements. We ended the quarter with $4.8 billion in cash, cash equivalents and marketable securities. Our cash flow from operations was $335 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $289 million and free cash flow margin was 29%.

And now for our outlook for the second quarter and for the fiscal year 2026. First, our guidance philosophy overall remains unchanged. As a reminder, we base our guidance on trends observed in recent months and apply conservatism on these growth trends. In addition, as with last quarter, we are applying a higher degree of conservatism to our largest customer.

So for the second quarter, we expect revenues to be in the range of $1.07 billion to $1.08 billion, which represents a 29% to 31% year-over-year growth. This guidance implies sequential revenue growth of $64 million to $74 million or 6% to 7% due to the strong growth of revenue in Q1 and into April. Non-GAAP operating income is expected to be in the range of $225 million to $235 million, which implies an operating margin of 21% to 22%.

As a reminder, in Q2, we will be holding our DASH user conference, which we estimate to cost about $15 million and which we have reflected in our operating income guidance. Non-GAAP net income per share is expected to be $0.57 to $0.59 per share based on approximately 369 million weighted average diluted shares outstanding.

And for fiscal 2026, we expect revenues to be in the range of $4.3 billion to $4.34 billion, which represents 25% to 27% year-over-year growth. Non-GAAP operating income is expected to be in the range of $940 million to $980 million, which implies an operating margin of 22% to 23%. And non-GAAP net income per share is expected to be in the range of $2.36 to $2.44 per share based on approximately 372 million weighted average diluted shares outstanding.

Finally, some additional notes on the guidance. We expect net interest and other income for fiscal 2026 to be approximately $170 million. We expect cash taxes for 2026 to be approximately $30 million to $40 million. We continue to apply a 21% non-GAAP tax rate for 2026 and going forward. And we expect capital expenditures and capitalized software together to be 4% to 5% of revenue in fiscal 2026.

To summarize, we are very pleased with our execution in Q1. We are well positioned to help our existing and prospective customers with their cloud migration, digital transformation and AI adoption journeys. And I want to thank Datadogs worldwide for their efforts.

With that, we'll open the call for questions. Operator, let's begin the Q&A. Thanks.

[Operator Instructions] Our first question today is coming from the line of Mark Murphy of JPMorgan.

Congratulations on an amazing performance. Olivier, is there any way to conceptualize the growth in the sheer raw volume of code that's being produced in the world today due to adoption of code generators such as Claude Code and Codex and Cursor because they seem to be developing the capability to take on full projects. And some of the charts are showing these capabilities are just exponentially exploding upward in a straight line. I'm wondering how much of that code is going into production and therefore, driving activity for Datadog?

Well, we definitely think and see that there's many more applications being created. There's going to be way more complexity in production. We see some of that happening already today. Some of those new applications are getting into production. They're finding users. We see some signs of that at every layer of our platform. We quoted a few stats on the increasing data volumes we see in our AI products. That's definitely a reflection of that. So we see an inflection point there in consumption from customers. We see a move to production that is very real, and we see that across both AI native and non-AI companies.

Okay. And just a quick related follow-up. If we click down one layer, and I'm wondering how you might view the increasing heterogeneity of the environment at the silicon level, because the -- when you look across Amazon, with Trainium and Graviton, Google with TPUs, Microsoft has launched the Maia Silicon. It looks like that is starting to explode. And our understanding is that trying to monitor the mixed environment is a lot more difficult than if you just have a uniform fleet of Intel and AMD chips. And we keep hearing all the traditional monitoring tools, they really fail on the custom silicon and Datadog handles it well. The -- and then all this new telemetry, including high-bandwidth memory and that type of thing. Can you speak to whether that trend is giving you some tailwind?

Yes. I mean, look, the broader market that's interesting here is training, the training used to be something only 2 or 3 companies were doing or maybe 4 or 5 at a large scale. And it looks like training actually might democratize quite a bit more, and many companies will train models on a regular basis. So it becomes more of a viable category for service providers like us basically.

I think the heterogeneity of the silicon is definitely a trend that plays in our favor there. The more heterogeneous, the more you need someone else to make sense of everything for you and tie it all together and also tie it all with the non-GPU aspects and the rest of the infrastructure and the applications and the users and the developers like basically everything we do for living.

There's only -- when you think of who is actually -- who actually has heterogeneous environments today, that is still a very small number of companies, Google, barely just started selling their TPUs to the outside. So I think it's still a small number of companies that are there, but we see a growing opportunity there.

Interestingly, last year, when we reported earnings, we said we're mostly interested in inference workloads and training is not really a market for us yet. Now we actually see training becoming a market. We started landing customers that are actually hyperscalers that have a whole host of homegrown technologies and that are using us specifically in their super intelligence labs to help monitor their workloads, accelerate the training runs, monitor the GPUs also. So we see that as a point of validation that there's going to be a great market for us.

That's amazing. I think there's a whole new dimension if you can move from inferencing into the training side. And I caught the reference in the prepared remarks of how you landed a couple of those very large labs. So congrats on everything.

And our next question will be coming from the line of Sanjit Singh of Morgan Stanley.

I want to start it off with David. And this guide to start the year is probably the best we've seen in several years, David, and you laid out the underlying assumptions quite well. I just wanted to do a sanity gut check just on the sort of overall backdrop -- macro backdrop. We do have some geopolitical tensions and those types of things when we think about. Your Middle East business and any impact from like in your e-commerce or retail business where there may be some consumer discretionary impacts. I just want to get like how you're thinking about those parts of the business? And then I had a follow-up for Oli.

Yes. We had a very strong quarter across the board. We had multi-industry, multi-geography type of quarter and SMB was very strong. And that -- the source of our guidance and our raises are at the core, that type of performance. We haven't seen any particular effect in the consumer businesses or e-commerce businesses yet. We basically have a continuation of trends in those businesses, travels and things like that, that are very similar to the other industries. So we haven't seen it yet. We obviously watch it and look at analytics, but we haven't seen it. In terms of our overall guidance, the trends that we have in organic, we discount across the board, and I think we mentioned our particular treatment of our largest customer.

That's very clear. And then, Olivier, for you, I think when we talk to investors about the debate in this category longer term, is just what does this -- what does the category look like when agents are doing the triaging, investigating versus human engineers and human SREs. And so what is your sort of vision of that -- how that evolves for Datadog, both from a product standpoint and an experience standpoint from a UI perspective. But also like is there going to be new modalities in terms of pricing when agents are consuming the Datadog platform to a higher degree than engineers do today?

Yes. Look, I think one thing I'd say is it's hard to tell where we're going to be in 4, 5 years. If you had told me 2 years ago that most engineers would go back to coding in the console, I wouldn't have believed you. And yet, that's one of the winning modalities today.

Look, as far as we're concerned, we don't care whether most of the usage is humans, most of the usage is agents. Our business model lends itself to it pretty well, like we're usage-based, and it doesn't really matter where the usage is coming from, from that perspective.

The way we see trends up right now is we see both a stratospheric increase of agent usage. So we have a ton of usage on our MCP server. We see customers trying to automate a lot with their own agents, using our agents, using a combination of those. But we also see an increase of usage of the web interfaces by humans. So right now, the two work hand-in-hand, and we keep developing and pushing on both fronts.

Next question is coming from the line of Raimo Lenschow of Barclays.

Congrats from me as well. One for Olivier and one for David. Olivier, if I listen to the -- to you in your prepared remarks, there's a lot of like consolidation that people try to do open source tooling and then realize they kind of needed to come to you and come back. On the other hand, in the industry, we still have a lot of like noise around that level. How do you see it in real life? To me, it seems a little bit like observability is just very hot and then there's different categories where you use certain vendors and some open source tooling. Can you speak what you see in real life there?

I mean in real life, most companies have open source in some capacity somewhere. But when it comes to having a platform that unifies everything, takes care of everything, does more of the problem solving for you, that's typically what customers use us. And the motion we see pretty much everywhere is customers have 4, 6, 7, 15, 25 different things and different pockets in the organizations and different business units, and it's a huge mess, and they come to us so they can unify all that.

They get better results because all of the data is in one place. The workflows can be automated from end-to-end. You can get end-to-end visibility, you don't have blind spots. And also they save money because they don't have all these pockets of inefficiency everywhere. So it's a win for everyone.

The thing that's also interesting, in particular this quarter is that we also landed some large parts of hyperscalers. And hyperscalers typically have a culture of building everything themselves, and they certainly have the balance sheet and the human capital to support some of that build-out. Like if there was ever a set of companies for whom it makes sense to do it themselves, that would be those companies. And yet, we see that they have the same issues. When it comes to going as fast as they can and being as efficient as they can with their resources, like they come to us to replace some of the things that we were using before.

Two things, two metrics to look at that to make the points, Oli, you're making. If you look at our platform adoption and you see both the growth of the different categories and the extension of the categories out to lots of products that shows you that the consolidation on the Datadog platform has continued and is a very strong trend. And part of that is the movement of solutions, as Oli mentioned, that are both open source, but also the competitive point solutions onto the platform. That's been a significant driver of the revenue growth for some time now, and that continued certainly in Q1.

Okay. Perfect. And then, David, for you, the last year, and -- we did a lot of investments around go-to-market, especially on sales capacity. If you think about now the non-AI category doing better, how much of that is like people doing like the cloud migrations again, so that's like an industry trend? And how much of that is like you guys actually being broader positioned?

Yes. Well it's a number of things, including one is the expansion of the platform, the consolidation the successful ramping of sales capacity, which is -- while not jeopardizing productivity, which has resulted in increasing ARR and a good environment as well. And I think that's what we said last time. There are a number of factors. And certainly, what we're proving out here is the investments we've made in go-to-market and are continuing are paying off and were the right decision. Oli, anything there?

Yes. And look, we -- at the end of the day, there's clearly some market tailwinds with the adoption of AI. And -- but also, we are outperforming all of our competitors at scale, and we're taking share. And that relates to the structure of our platform, the way we expand with new products, the way these products are maturing and starting to win in their respective categories and the way we've successfully grown the sales capacity.

Certainly, the AI investment trend has helped, but what we're trying to do is separate that. So -- and AI investment is probably helping the overall as well. But when you really take that out, you still -- you see a very pronounced acceleration here, and that has to do with the factors that I mentioned and Oli talked about.

Our next question is coming from the line of Gabriela Borges of Goldman Sachs.

Olivier, I find your comments on training versus inference so interesting. Maybe just crystallize for us, why do you think the training opportunity is happening now or inflecting now? And then either for yourself or David, how do we think about the attach rate on training versus inference of observability? If there is a way to benchmark observability spend as a percentage of inference spend, does that number change given the new data that you're seeing on the training side as well?

So on the training side, training was very new a couple of years ago. It was something that was only done by very few companies, and it was, in a way, very artisanal. Like, it was not a production workload. It was something that researchers were building and that was very one-off and homegrown in ways. And now it's turning into production. It's turning into something that many more companies are doing. It's scaling by orders of magnitude.

And it's becoming something that has to be on all the time, reliable and every minute you lose is -- or rather every failure you have in your training runs is a week you give away to the competition. And so as a result, it becomes way more interesting as a market for us. And we see some signs of that. Again, we didn't have a lot of it. We didn't see a lot of it last year. Now all of a sudden, we're starting to see quite a bit of activity there and demand, and we have success landing with large customers with those products.

Yes. I think going back to the metrics that Oli talked about in terms of attach, we said that 6,500 customers are using our integrations and that's 20% of the customers and 80% of the ARR. So there is attach. I think it's earlier days for the training. That looks like it will be a contributor. But I think we -- that's early, and I would sort of look at the larger attachment at this point as the evidence of inference, but also some training.

Our next question is coming from the line of Karl Keirstead of UBS.

Okay. Great. I wanted to start, Olivier and David and Yuka in congratulating all of you and the team on reaching that $1 billion milestone, well done. David, maybe the question is for you and to hone in specifically on the 2Q guide. Even if you put up a modest beat on that guide, it's going to be by order of magnitude, the largest sequential dollar add, I think, in the company's history. And I just wanted to unpack what's giving you that confidence. And in particular, is there anything interesting to call out, David, in terms of the ramp of a couple of the larger research labs, one of which renewed with you guys in the fourth quarter, another one just landed. I presume they're ramping nicely in 2Q, but would love any color.

Yes. Let me unpack this in a couple of ways. As you know, we're a recurring revenue model. So the biggest indication of in the near term of the next quarter is the ARR growth in the previous quarter, and we said we had a record. So essentially, at the bedrock of this is sort of the run forward of ARR that we've already signed. The ARR add was very broad-based and was not very concentrated. So -- whereas we pointed out some very significant adds I would say that the first quarter and that ARR add was really diversified and from lots of different places.

So the -- and I think Oli will come in here. But the confidence that we have is, you're right, we essentially take what we already have. We discount the growth trends that we've seen. And that produces what you exactly said, which is whatever your assumptions are on beat, a very impressive sequential really due to what happened in Q1 and the rate of business accumulation by Datadog. Oli, do you want to add?

Yes. I mean, I basically want to double down on what David just said. The adds were broad-based. I mean, look -- and when you look at why do we have a great Q1, we also landed great customers in Q4. We had talked about it a quarter ago. But even if you take out the customer we land in Q4 that added the most revenue in Q1, we still had a record quarter in terms of ARR adds. So this is really broad-based. And we landed a few more customers in Q1 that don't contribute any revenue yet, but we expect to be big contributors in the future. So when you put all that together, we feel very confident about Q2, hence, the numbers you've seen.

And our next question will be coming from the line of Fatima Boolani of Citi.

Oli, I wanted to double back on a question that was asked earlier with respect to telemetry volumes, essentially going parabolic and you are accessing brand-new demand vectors in the foray into training and monitoring and observing training model environments inside some of the world's largest frontier labs. And so I wanted to ask you about the structural changes to the capital intensity of the business. I mean your CapEx levels are still pretty respectable and pretty muted. So I wanted to get a better understanding of what sort of extrinsic or intrinsic engineering efforts you're undertaking to keep a very efficient CapEx envelope in spite of the fact that it seems like that would increase because of the torrent of telemetry you're seeing on the platform.

And then as a related matter, we've seen a rise of sovereign data and data residency requirements kind of ramp as AI models move into the territory of national security and things like that. So just wondering if you can kind of talk to some of the engineering horsepower internally that you're leveraging to be able to keep a really tight command on capital intensity and frankly, your gross margins?

Yes. I mean, look, so the investments we're making right now, we run most of our workloads on cloud, meaning you'll see all of that in OpEx, not in CapEx. So we have low CapEx. If it changes, we'll tell you, like if for some reason, we decide to make different kinds of investments and some of it more upfront, some of it more CapEx, we'll tell you, but that's not the case today.

We are definitely ramping up our investments, in particular, in R&D and in the scale of the models we train ourselves and things like that. Right now, there's no -- nothing that you can actually see in the numbers that move any needle, but if that changes, also we'll tell you. We don't expect any change to our model. So that's on the CapEx side. We're very different businesses in that way from the AI lab.

On the subject of data residency and sovereignty of AI and things like that. We definitely see more push for that, more demand for that in the customer base. And for us, that means investments in 2 areas. One is in deploying into more geographies and having more certifications to sell to the public sector and to the highest levels of the public sector. So we mentioned today data center in the U.K., for example, and our FedRAMP High certification. We are not stopping there. In terms of the certification, we're going after with the federal government. So that's an area of investment.

Another area of investment is our bring your own cloud products and -- where we can actually run on our customers' infrastructure. And so we announced that. We released some products there, and we have heavy investment in that area so we can support customers that want to operate in a slightly separate way from the rest of our customer base.

And our next question is coming from the line of Kirk Materne of Evercore.

Congrats on a nice start. Oli, I was wondering if you could just give some thoughts on the idea of sort of security for agents. I think one of the big issues in terms of getting agents into production is sort of the security aspect of that. And how do you see Datadog plugging into that opportunity?

And then just a quick one for David. Congrats on the FedRAMP reaching that milestone. Are your partner relationships in place to take advantage of this? I realize it will be a long-term opportunity, but just kind of curious how well established you are down there to start seeing some maybe bookings in that area.

Yes. So on the security of agents, we interface with that in 2 ways. So first, there's the agents we build ourselves because we are building a lot of automation inside of our product for our customers and agents that automatically identify but also resolve issues without you having to do anything. And there, a lot of it has to do with understanding what permissions to apply, what kind of guardrails to apply, what kind of -- how to interface with the humans and how to make that trustworthy and visible in the right way.

And so that's pretty much the whole product surface is to [indiscernible] data. The automation itself actually kind of works already. So you should expect to hear more about that at our conference. This is definitely one big area of investment for us.

On the security aspects of our engagements, look, we -- our belief in security is that you need to integrate -- you can't just have point solutions that look at one lever of the whole security posture. You need to look at everything altogether. And that's one of the areas that we are also covering with our security efforts. So that's part of the whole platform, actually.

On the FedRAMP, we've been working on both the different certifications. But at the same time, we've been investing in the go-to-market function, both in terms of reps and channel partners for a number of years. Certainly, there's more investment to be done, but we invested ahead of the certifications because in this sector, building pipeline, et cetera, takes time. And certainly, the channel partner relationships are a very important part of this, and we have been investing, but also have more investment to do.

Our next question is coming from the line of Patrick Colville of Scotiabank.

Echoing the congrats of my peers. I guess, Olivier and David, you guys are very deliberate in your messaging on the prepared remarks. And I just -- I guess I want to double check the kind of wording of one of the comments. I think, David, you said a higher degree of conservatism to the largest customers. I guess, did I hear that right? And then does the higher degree of conservatism reference versus the other customer cohorts? Or does it reference versus your guidance philosophy in prior quarters vis-a-vis this customer?

It's both. It's the same guidance we used, and we're being very explicit. For all the business, except for the largest customers, we've always taken the drivers and discounted them. We -- for this particular customer, we took a higher degree of conservatism than the other part of the customer base and discounted it more. And we were, I think, in the remarks, and you interpret it correct, very explicit and you're correct.

I wouldn't give that much weight to the very specific way. We're deliberate, but not all that deliberate. And similarly, both David and I have a raspy voice today, but there's no other meaning.

But I will remind everybody that we did not change. So it's the question also, I think you asked, is did we change or is this a different methodology of both the overall and the large customer than the guidance the last quarter or the previous? The answer is no. It's the same methodology and -- that we've had. So no change, but that has been what we've always been doing.

Okay. And Olivier, can I ask about your comments about the hyperscalers because I thought that was particularly interesting. And the reason why is I don't think you called them out previously before, and they are so prevalent in the modern tech stack. To your point, they could do this themselves. So I guess how are they using Datadog? Is it for more kind of traditional observability? Or is it for these newer areas like GPU monitoring that Datadog has performed so well of late?

Well, it's both actually. When you look in general at the large AI customers, they use Datadog the way other companies are largely with a fairly broad set of our products to cover the full surface of observability. What's new is we now have a product for GPU monitoring. It's a very new product. And we see the hyperscalers that are coming to us for training workloads in particular, being very interested in that.

So again, it's too early in the product life cycle and the customer life cycle for these specific customers to call definitive victory there, but we see that as a very encouraging sign of where the market might go in the future because we think this might be a bellwether of what the next 10, 100, 500 companies that are going to start training workloads are going to want to do. We have some signs that go beyond the customers we signed this quarter that point that way too.

And our next question is coming from the line of Peter Weed of Bernstein Research.

I'll echo others on the momentum. Great to see. One of, I think, the great successes you talked about was landing a couple of the AI labs for the hyperscalers. Although I think on the other hand, you've talked in the past around hyperscalers are typically building observability in-house. What is it really about the AI workloads that are making it more attractive for them to use Datadog? And what might give you confidence that Datadog might be more persistent with them in these types of workloads and that's kind of a signal for maybe how other customers might use Datadog around AI differentiated from things that they might be able to bring in-house other places?

The same reason all of our customers use us. It's high stakes, high complexity and not core, they have to be what's differentiated. They can't afford to be late, and it's a really hard job to do that. So that's what we build our whole business on. And it's also very true for -- at the highest level for the largest companies.

Yes. No, I was just going to say -- but I guess the point is you've emphasized that those largest customers have been able to go in-house on some other things. Is there something unique about AI that prevents them from doing that here?

Well, I think the urgency of their development efforts focuses the mind. That's what I would put it. I would say it forced you to figure out what's core and what's not core and what's the -- who you want to get to the -- what you need to do to maximize your chances of success. And again, it is the same thinking all of our customers have all the time.

I think the equation for hyperscalers has often been fairly different because they have, let's call it, unlimited access to staffing and they could sort of set their own time horizons for the developments they wanted to make. I think the situation is a little bit different with the AI race, maybe.

And our next question is coming from the line of Gregg Moskowitz of Mizuho.

And I'll add my congratulations on a terrific quarter. Just one for me. Oli, I know it's not GA yet, but curious if you have any early feedback on your new cloud prem offering. As you noted earlier, providing the ability for Datadog to run on customer infrastructure. Could this be another -- yet another, I should say, incremental growth opportunity for Datadog? What are your expectations for this?

Well, definitely, we think -- I think there was a question earlier on data residency and living in customers' environments. We definitely see a great opportunity there. There is a chance that a good portion of the market leans this way in the future. Today, it's not the largest part of the market, but we definitely see a potential for that. So we're investing heavily in that sort of our product.

We are starting to see some interesting customer traction there. So we think this can be another growth lever definitely. We also think that it can help us getting into some extremely large-scale workload where customers would not have considered a SaaS offering before where we can be in the running. So that's very exciting.

All right. And I think that was our last question. So I want to thank you all for attending the call. And I'll remind you that we have our conference in just a bit more than a month, and I hope to see many of you there. So thank you all.

This concludes today's program. You may all disconnect.

All right. Good morning. Day 2 morning session of the Morgan Stanley TMT Conference. We are super thrilled to have the Chief Financial Officer from Datadog, David Obstler. David, welcome back to the TMT conference. I think you guys have been here every single year that you've been public.

Yes. Thanks for having us back.

Awesome. Yes, we're going to -- lots of talk about. Business is doing well. We've going to talk about, of course, AI and all sorts of other topics. But before I get there for important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures.

So with that, let's kick off the conversation. If we look at this year, you're coming off another strong year business accelerated from a growth perspective to 28% to $3.4 billion in revenue, delivering operating margins of 22% and you're now serving 32,000-plus customers. We're at an interesting time in the market, and particularly with software, and then there's a kind of a return to first principles thinking and assessing how software providers create value. So with that context in terms of getting back to basics, David, what are the core problems that Datadog helps customers solve today? And what problems will Datadog help solve going forward?

Yes. Good question. So Datadog helps companies migrate their mission-critical applications, usually customer-facing to the cloud or manage them in the cloud. Our legacy has been -- and observability, which has really been around infrastructure, APM, logs and digital experience. And as we've expanded the product set over the years, we tend to handle more and more of the problems of clients always in a single pane of glass, heavily integrated, our customers in DevOps and SRE can come in, turn it on and see what the environment. And we've been moving the platform from observability to areas like security, towards the front end, towards digital applications and digital experience, now product analytics, to service management and workflows and injecting AI in the platform, both the platform itself and the monitoring, all to try to move from observability to recommendations and an action, which has been the core value proposition of Datadog from day 1.

Awesome. Let's talk about some of the business trends that they mentioned. One of the things that stands out for me is just the core business sort of reaccelerating. It's accelerated for 2 quarters in a row. You grew 18% in Q2, 20% in Q3, accelerated again in Q4. What are the factors driving the reacceleration? And how durable does this rejuvenation in growth feel to you in the context of the current demand environment?

Yes, there have been a number of factors. This is the business in companies that we're not calling AI natives. And first of all, we're very, very early in the migration of applications into the cloud and in the modernization of applications and infrastructure. And what we've seen is a good buying environment, meaning corporations down the SMB have turned back towards moving applications into the cloud. That is likely we said and we believe to be accelerated and complemented over time by the re-platforming that's going to be caused by increased complexity because of AI. So we have a good buying environment.

The second thing is that we've expanded our platform substantially over time. So we have a lot of new products, a lot of new products that are getting to and achieving scale. And so we have a broader value proposition to sell. I would say the third thing is that we've been winning market share and consolidating. So remember, the value proposition is single pane of glass, a platform. And we're finding that we've been able to consolidate that market. There's some really good evidence like the acceleration of the APM product. That means we're innovating, but also taking market share.

And I think the last thing in our hands is that we've expanded our go-to-market capabilities. We successfully expanded our quota capacity. We've been able to do that while maintaining productivity. That includes new geographies, governments. I think we're getting better and better about enterprise selling and the go to market motions. So that's something very much in our hands, and we've been investing for the last couple of years, and that has been paying dividends, helping to accelerate that business.

Yes. That point around consolidation is, I think, an important one because you have to have the product portfolio to do that, right? And there's a lot of players in this space, that probably only a handful that can actually do get to -- go to a customer and say, we can consolidate 10 or a dozen of these capabilities onto the Datadog platform?

I think we've showed a very interesting statistic on our Investor Day that despite the fact we've been at this for a while, only half of our customers are using all 3 pillars. And once a customer standardizes on Datadog, their spend accelerates. So we've done a lot. As you say, we have been consolidating and we do have that product set, but there's much more to go.

And just maybe stick on that topic I was going to ask this question a little bit later. But on the customers, roughly half of the business that's not on all 3 pillars, what do you think will be the unlock to get them to adopt more of the platform?

I think you have to think back to history. Datadog's first product was infrastructure. And there were APM log products, digital experience, database monitoring out there. So I think the 2 things, and we're seeing this over and over, that are unlocking, are, one is the frictionless adoption in the platform that exposes these products to clients and then time because there are installed bases, they're champions, et cetera. So the value that's being seen from having everything in a single pane of glass is quite substantial, but it takes some time to replace those other legacy products or legacy customer bases. So we're seeing that. I think we said over the years that in our larger deals, somewhere plus or minus half of those largest deals have consolidation in it.

We've been talking about the core business trends and the forces driving that growth. You guys are also doing incredibly well with the AI natives themselves. You have 70% of the top 20 AI native. You got 19 of them spending in excess of $1 million annually. And I think roughly about 650 AI-native customers overall. So what has the company been doing to penetrate this segment of the market? And how do you think this cohort performs going into fiscal year '26?

Yes, great question. This is an ideal customer base for us like cloud native. They don't have legacy infrastructure apps. They are modern cloud companies and Datadog, as you know, designed its product to be optimal and to be a product that is -- handles many of the needs of this type of customer. And these customers are growing very fast. So they're tending to land and adopt the Datadog products very quickly. They don't have something else they used before. So they're landing. So I think we have a natural product fit.

As you can say, we've been comprehensively winning. And what we're trying to do, it's similar to other fast-growth companies. We're trying to work with them in terms of both landing and expanding, expanding the product set, having very good account management and technical management with them, we've evolved over the years to have lots of different departments at Datadog that help a client understand. And what we're finding is they're deciding that it makes so much more sense given their huge investment in their own products to buy Datadog's. So I would say that we're winning. And we're also -- all the type of developer marketing and types of things we do hit very well with that constituency, and we're continuing with that.

On the last earnings call, you announced an 8-figure land with another leading model provider. There's a debate in the market that these types of customers want to build their own infrastructure and tooling. And so what were the reasons that this customer chose to go with Datadog after its observability needs?

There's a debate in the market, but the evidence is quite the opposite. In fact, when you look at how Datadog has grown and the market share it has taken. The predominant decision has been to use the Datadog platform rather than build it yourself. One, those companies have a lot to do. Two, when you look at the total cost of ownership, in terms of development and the platform and the cloud, it's efficient to use a Datadog and you get the best of breed.

So I think that's a good example of a customer, that early on, experimented and trying to do it themselves. But I think we said it's obviously one of the larger companies in the space and along with the other large companies in the space, they've decided to use Datadog. That is completely antithetical to, I would say, the concern out in the market. But when you really look at it and you look at Datadog's gross retention, which very upper 90s, and we showed this in the Investor Day, what you'll see is it's very much fringe cases that decide to do it themselves.

And the weight is really on the other side of using Datadog. We also have customers that do both at the same time, that maybe experiment with that and come back. So I think the weight of the market has been to buy the Datadog platform for lots of reasons, including efficiency, efficacy, return on investment costs, et cetera.

Awesome. Let's talk a little bit about, you guys had reported your Q4 results and guided for 2026 the other week. And so I just wanted to review some of the assumptions around there. You guided for 19% revenue growth at the midpoint, excluding your largest customer, you think the remainder of the business will grow in excess of 20%. Given that the core business grew 23% in Q4, what gives you the confidence that growth excluding your largest AI customer will prove durable at current levels?

Yes. Great question. So we really -- so there's 2 different questions. What do we guide to and what are we seeing. And I think we said and showed that the business had been accelerating. We made a comment when we did our earnings that we saw that follow into this year. And generally, the business is predictable in the shorter term when you look at the sort of the trends. So the reason why we see it is we see a very good end market, we see a great adoption of our products.

We see us landing more logos and larger logos. We can talk about all this. So all of those trends are compound on themselves and give us the confidence. Now -- then we put conservatism on it. We take those growth trends, and we discount them in order to provide the cushion in our guidance. But pretty much that type of accelerating performance translating into guidance is due to what we're seeing repeating the 3 or 4 factors that I mentioned upfront.

Yes. It's been a methodology that you've got in place for years when it comes to the guidance. So let's talk -- you brought up the point on new logo lands and the deal sizes getting better. I mean one of the things that I found interesting about the Analyst Day is that the size of the enterprise land deals really stepped up in 2025 versus prior years. Can you speak to the size of these enterprise land deals and the force that's driving the big increase in the overall land size?

Yes, definitely. I think we're -- our product suite is broader. That means we are landing and expanding faster and more products. So we have -- we're still land and expand. We're still get in there very long. We showed in the Investor Day, very long-growing cohorts. But what we're seeing is we're seeing -- and this has to do with consolidation, replacement, quite a number of lands that are larger and are more comprehensive and our service model in terms of how to deal with those customers, how to sell through channels, how to help the business owner and the CIO make that migration, has improved, which would result in more acceleration. So those are all the things that product side and execution side that have created that broader enterprise selling.

Yes. Very impressive to see. So when this year started, I had investors reach to me like Sanjit, congratulations, you don't cover seat-based models, right? You cover the part of software that's insulated from an AI risk and those types of things. I have to say in the last couple of weeks, everything is getting questioned, including the names that I cover as well as across software and across sectors. And so I want to spend a couple of minutes talking through the debate in terms of defensibility against potential AI disintermediation.

And there's a couple of different angles that I'd love to get your perspective on. One question I get is how does the value proposition of the Datadog platform change when agents are doing the investigating and triaging of incidents versus human DevOps or cyber liability engineers. Is there an observability solution that has a dashboard that is being used as interface, is relevant in an Agenetic world.

That's a big question. There are lots of things, but I want to say something on front. They're seat-based, I think, which is an important thing, but there's also the word infrastructure. So when you're infrastructure and you see that's related to seat-based, we're monetizing based on the agents or the containers or the servers, we're also monetizing based on how it's used. But what are we doing? We're monitoring infrastructure. And what we're finding, and we've seen this through time is as the technology evolves, you need, and I would argue, we would argue increased need to have visibility into the infrastructure. So that's one thing that's very important.

Another thing that's quite important is we deliver and connect in a variety of ways already. We don't care if you're coming in through a desktop, through a wireless, through open-source, through OTel. I think as you look at our Investor Day slides, you'll see that we're investing a lot of money in making sure we can both cover and come in through the information around agents. So the delivery. And then I would add that when you look at the value that's delivered, you have the access, but you also have the integrations and all of the data brought together in an increasingly complex world.

You have the organization of that and then we're calling it service management or closing the loop. You have what to do about that. And when it comes to foundational models or access to data, we're either integrating or -- and I think we demonstrated at our Investor Day, our own foundational models to make sure that we're investing so that if critical capabilities involve, what's the most cost-efficient and best foundational model using the vast data, we have it. So I think that there's lots of reasons seat-based is important. So congratulations you're covering, but there's lots of other things under the hood that you have to look at to when I think if you're evaluating defensibility and frankly, the increased value add in your products.

Yes, that's an important point. And so the other angle on sort of the risk to observability players, including Datadog is the potential for customers to combine open-source tooling and manage their metrics, traces and logs, combine that with agents from like the model providers to reach over the data and execute the incident response. What's the company's argument for why this line of thinking is off pace?

We -- I think that we would view -- when you think we had the same discussion about OTel. The key is not -- it's -- yes, having access to all the data and Datadog through its MCP server, through its LLM monitoring, through all of this, through its integrations is always going to have and is investing significantly in having access to all the data. And then it's always been the case, whether it's OTel or direct integrations, et cetera, that what -- the magic is what happens after that.

So I think -- and we talk -- we can talk about AI for Datadog and Datadog for AI because there's a whole another set of things about AI-ness of the platform. But in terms of access to the data, I think if you look at our investor presentation, we're doing it, then, okay, why come to Datadog, why continue? And we believe that platforms like Datadog has to be completely AI-native, integrating with agents, but using agents within Datadog, and that's what we're calling our SRE Bits, our Security Bits, et cetera. That means that we are investing and leading the way in the agents in the platform to provide value, do diagnostics and eventually self remediate. So yes, it's important. This is an important DNA. And I think that given how much we're investing in R&D, and looking forward, all credit to Ali and the R&D team, I think we're doing it with and to ourselves.

Let's talk a little bit about the AI for Datadog. So just to add up on the point that you made and particularly around the models that you guys are developing. How much of a competitive advantage are the AI models Datadog has built with a huge data sets that you have access to when thinking about competition versus other AI natives of the research. Maybe said another way, will Datadog release more capable agents, more quickly in this domain versus the research labs and other competition due to a data and AI model advantage.

We do believe that will be the case. We don't know for sure, but we believe that when you think about efficacy and cost that having models that are based on the huge amount of data we have about this problem and trained on these data sets will be part of the equation that delivers most value and at a good price. Because when you're talking about generalist models foundational models, they are essentially using a vast amount of data. We don't have as much data as they have, but we have data that's more on point.

And we're spending our R&D dollars training that. I guess this might be one of the reasons why all of those AI native companies are using Datadog because what they're finding is the platform and the AI nature of the platform is a better solution for observing these workloads, securing these workloads and creating action than their more foundational generalist models.

Can we talk a little bit about the momentum that you're seeing with Bits AI, SRE agents. What's been the customer feedback with regards to its accuracy, reliability and speed in terms of finding the root cause to incidents.

Yes. It's been great. Look at the analyst presentation for some truly impressive quotes. We have just put it in GA. We have 1,000-plus customers using it. We have ARR where it's being paid for. We have a pricing model on the site. So the initial reception has been very strong. And of course, like all of Datadog's products, we launch them, we get feedback, and we continue to improve that. So I think we're going to -- we're still early stages, but getting great feedback in terms of the fact that we're on the right track. Customers are finding value and we're getting the feedback to be able to handle more and more use cases.

And I'm saying we're going to do that. The other one is that -- the other Bits products around security and around development, I would say they're a little earlier in this cycle, but we plan to do the exact same thing, which is have co-development partners, figure out the uses, get them in GA and then learn. So it's part of a whole portfolio, as you mentioned, in the AI of the platform.

Let's return back to the other aspects of the AI debate when it comes to this category and Datadog. So let's just go to be sometimes expressing the view that observability is just data ingestion into a time series database, we use some event streaming, and it's a dashboard. And so I think the implication that they're getting at here is that observability can be replicated with the help of AI and coding agents. I've heard you guys in the past mentioned that 50% of your engineers work on the core platform itself. And so I'd love to -- if you could expand upon the scale and sophistication of the platform as well as the services that the core platform provides. My guess is that you guys have spent over $1 billion building the core platform. So I'd love to get your perspective on why Datadog will be hard to replicate.

Well, a lot more than that because we're spending over $1 billion a year, and we said 50%. And I think there are a number of things. One, scale, how to handle the data. When you think about -- is it -- when you think about ingesting infrastructure data and creating metrics, No, our product is so much broader when you consider that it has that aspect, but then it has front-to-back application monitoring, customer behavior, databases, data observability pipeline, logging, service management, all sorts of things, all correlated. So I think one is all of those things that are orchestrated and all the integrations.

Two is it's been a huge competitive advantage in that with the platform and how we've designed it, we can build this additional functionality. That's at the core of our speed, much faster, much more cheaply and in a more orchestrated way than competitors. And if agents are important in doing that, they'll be in our platform. So I think the platform itself has been -- it's kind of a 2-way thing, a circle. It's enabled us to have all this functionality. And it's also enabled us to integrate new functionality in it, which I think will position us very, very well as the world gets even more complex and agents start to.

I mean there's all sorts of evidence that we have now in terms of the number of calls out to MCP servers accelerating dramatically between the third and fourth quarter. The spans that are sent from LLMs 10x over the last 6 months. So all of this is proving true that the platform is a contributory factor in having us continue to be even maybe more so in the future at the center of all of this.

You guys built multiple billion-dollar-plus business, like your Logs business. I think some of those businesses were built with relatively few engineers because they're building on top of a platform.

There's no question. The return on investment and the ability to get through, I think we said infrastructure had gotten to $1.6 billion, logs over $1 billion, APM and digital experience over $1 billion. And there's lots of other examples we've been given. And I think a lot of it has to do with the basic fact that Datadog started as a core data and infrastructure company which has created this competitive advantage and the ability to develop very quickly.

Awesome. So let's talk about some of the opportunities to unlock growth further across the business. Let's get an update on the security business. Security seemed like it shifted to a higher gear in 2025. And one of the -- going back to the Investor Day, one of the data points that you guys put out there was 70% of your $1 million dollar customers are using one or more security products. But in terms of the ARR, it's still relatively modest. So what are the initiatives to improve the security adoption with your larger spending customers?

Great question. We launched security initially to work with our platform in DevSecOps and more progressive customers, more cloud-native customers and I would say, more limited functionality than we've arrived to. And so the change, the thing that's inflected here is that both with the maturity, and I'll talk about Cloud SIEM as an example, the maturity of the product, the architecture of the product to use are very entrenched and excellent enterprise logs business and expand the uses of logs outside of observability logs.

The redesign of the front end and the addition of channels and services -- service partners on top has allowed us to begin to attach Cloud SIEM in particularly, but also more broadly onto some of our sophisticated large enterprise customers. And so that's what we started to do last year, and that is starting to take up the ARR per customer and move it into, I would say, larger use cases, more traditional use cases, et cetera.

So it's a combination of product maturity, particularly in Cloud SIEM, using our very good installed base in logs and the go-to-market. We also have a good market environment with some things that have changed in some of the other competitors, where we've been able to, like we did in Cloud Logs, focused on the Cloud SIEM work functionality and start to penetrate some very significant customers.

Guys like me have been asking for several years when it comes to the security strategy? Like are you guys need to have -- are you guys going to -- Datadog going to build a specialist security sales force. It sounds like you guys finally made that decision to pull the trigger on that. So the question is like why now in terms of deploying that specialist security sales team? And what do you hope the impact will be in sort of year one of the deployment?

First of all, I think that we acknowledge, security is a different go-to-market motion. It's more channel led, it's more centralized. And so I think the reason that we didn't do it upfront is in order to win the hearts and minds of channel partners and to have the right motion. You have to have a competitive product, equal or better in functionality. It probably doesn't make much sense to have all that distribution if you're not going to be at that level.

So I think it was a step function in that we had to get the product, in the right shape and Cloud SIEM got there and et cetera. So I think it couldn't have happened. We're early on in it. I think the progress we've made already has been mainly due to our existing go-to-market motions and our cross-selling into enterprise logs customers. And we're just getting those -- both the channel and the specialist salespeople potted in. We don't know the answer. But I think it's going to help accelerate by having quota-bearing either channel partners or salespeople who do nothing else. So it's early, and we'll report on it, but we're optimistic that it will help accelerate.

I guess the signal here is that you guys are confident now, more confident in the actual product capabilities. And because you're more confident now you feel more motivated to engage the ecosystem in the channel.

To win the channel partners and to get them recommending. You have to have a number of things going on, including the product. So I think what we said is we're ready for that, and that's why we did it last year and are going to invest behind it.

We talked a little bit earlier about AI and how it's embedded within the Datadog platform. Let's go the opposite way Datadog in terms of helping customers with their AI initiatives. LLM observability is a product I think you guys had out for a little over a year, maybe a little bit longer than that. Do you feel that this product has found product market fit? And what are some of the recent patterns on usage? .

Yes. Definitely. So yes. But this is essentially dependent upon our customers having LLMs that are in their production environments. And so we are setting it up and starting to see inflection in that. I think we have the right product. And the amount of use of integrations and the span sent to us have accelerated substantially. I think I wrote down a couple of metrics in that. We have over 1,000 users and our spans sent to us in the last 6 months have expanded by 10x.

I think that's evidence of our clients starting to integrate LLMs into their own production applications. And we have the right product and fit. So I think we're early on, but we're getting a lot of good usage and the acceleration rate of the use of that -- those integrations and the data they're sending us is quite pronounced recently.

Yes, it's great insight. Another area, one that I'm particularly interested in, because we sort of live in a GPU economy. You've talked about a product for GPU monitoring that's in preview now. So why decision to enter this category? What types of customers do you think will be most interested in this offering? And what do you think pricing will look like?

Yes, we've had it. We've had the ability, but I think we're -- we understand that the GPU is larger than the CPU and required. So I think this will be as our clients, broadly speaking, not just the AI natives, but as enterprise customers or cloud native start to create their own models and integrate it into their own production environments, they'll be using more GPUs. And just like all the other product areas, we want to be there to be able to monitor it.

So right now, we have some use. What we want to do is increase the functionality, optimize the pricing, the same way we do always. And so to be there for our broad client base as they use more GPUs. Don't forget, most of the GPUs have been fairly centralized right now. And the infrastructure providers and the model. But we don't think that will be the state of play of the market down the road.

Expect some broadening. That makes a lot of sense. I wanted to wrap up the conversation just on some of the consolidation that's going on in the market. So you've seen some large security vendors acquire into observability. You've seen data platform providers acquire into observability. So the questions here is, can -- the assets have been acquired can they pull off those consolidation deals that you and maybe a handful of others can do in the market? Or are they -- what's your assessment of the breadth of that data capability.

Well, this is not brand new. This has been going on for a while. Since we've been public, we discussed it. There have been a number of companies in security or in automation, in IT automation they've tried to do this. And they basically have not succeeded. So I think it depends on what you're acquiring. So far, most of what has been required are point solutions that don't offer the observability platform. So I think, number one, in order to compete against the company that has the breadth of product and the single pane of glass.

The market has not been kind to point solutions. So they have to do that. They don't have that. And then just like our challenge in go-to-market and security, they would have a challenge in sort of the bottoms-up selling into DevOps. So I think it's a tall order. And most of the companies that have been acquired, our point solutions that may have use cases, but really haven't been competitive in the broad obseverability platform.

Maybe one last I want you to comment on, as with respect to the competitive element. What do you think will make Datadog the winner versus the security players who also have their agents deployed in the customer environment just like Datadog does.

Yes. I think it's essentially having the data and the integrations and the user interface that are optimized for those use cases that are -- we've learned, we've just talked about are different than security. And so I think it's the same thing on the other side, whether it be agents or whether it be developers or whatever the combination, these raw materials have to be integrated with the data. And so I think it's going to be a lift for those others to do it. It's certainly -- they've been there trying to do it for 20 years. It hasn't happened. Who knows about the future, but it is a difficult lift.

Well, we'll leave it there. David, you -- asked a lot of product questions, less financial questions. So kudos on that. Thank you for giving us the update on Datadog. Really appreciate it.

Thanks for having us. Appreciate it. Thank you.

Good afternoon everyone. My name is Yuka Broderick, and I lead Investor Relations here at Datadog. Welcome to all of you here in the theater and everyone joining us online for our 2026 Investor Day.

Before we dive in, just a few reminders. During this presentation, we will make forward-looking statements, including statements related to our strategy, product development, market opportunity and financial goals. These statements reflect our views today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially. For a discussion of material risks, please refer to our Form 10-Q for the quarter ended September 30, 2025, and other filings that we may make with the SEC, including our Form 10-K for fiscal 2025.

We will also discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial measures in the appendix to this presentation, which is available at investors.datadoghq.com.

All right. Let me briefly run through our agenda for today. In the first half, we will focus on our strategy, platform and product innovation. You will hear from our Co-Founder, CEO, Olivier Pomel; and CTO, Alexis Le-Quoc, followed by product leaders, Yrieix Garnier, Tim Knutson, Michael Whetten; and our Chief Product Officer, Yanbing Li. We will follow that with the Q&A with that group. In the second half, we will discuss our go-to-market, how we deliver value to customers and our financial performance. Our presenters will be CRO, Sean Walters; COO, Adam Blitzer; and CFO, David Obstler. That group will be joined by Olivier.

With that, let me turn it over to Olivier to kick things off.

Thank you, Yuka. All right. Hi, everyone. My name is Olivier Pomel. I'm the co-founder and CEO here at Datadog and welcome to the Investor Day. So you're going to hear from several of our leaders today, and I know they are quite excited to present. And so as we discuss what we're doing here at Datadog, our goal is to show you why even after 16 years of building the company, we believe that we're still only just getting started. We're barely scratching the surface of the opportunity. So I'll kick us off with a quick recap of the problem we solve today, how our platform has expanded over time, what we're up to with AI, and I'll give you a sense of the broad direction we're taking over the long term.

All right. All right. So let's start with cloud migration and digital transformation. So you have here the usual beautiful chart [indiscernible], which shows a sustained rate of migration over the past few years that is expected to continue for the foreseeable future. And it is worth noting that Gartner expects spend on public cloud to exceed $1 trillion by 2027. And even with that, it would still add up to only 16% of global tech spend.

So why is this happening? Because as a company, any kind of company, you absolutely have to, you have to interact with your customers online. You have to differentiate from the competition through innovation. You have to run into the cloud to get agility, short time to value and efficiency. And to be honest, you also have to lean into the best tech so you can hire and retain the great engineers. And in the end, this modernization leads to better business outcomes. All of this was true over the past decade, and we expect it to be even more pronounced in the age of AI. As you cannot adopt AI, if you are not digital and in the cloud.

So our customers want to build their applications in the cloud as fast as possible using the latest technology. And this is a slide I've been using for a while now, and that is still true. And it illustrates the explosion of complexity. We've all witnessed [indiscernible] innovation over the past 20 years. So I won't spend too much time on it because we've gone [indiscernible] before. But you can note that we added a few things in there such as on the chart on the top right, which is the scale and compute units. You see that now we've added the GPU fleets with many [indiscernible] being deployed all the time. And the way you read those charts is you multiply each of them. And so what you end up with is truly an explosion of complexity.

Now AI is a large, rapidly growing and exciting [indiscernible] spending. And I think we all noticed. But to put things in perspective, we have a few Gartner numbers. So the market opportunity really is very large. And for us, it is the next big thing that's going to bring both better business value to customers and additional leverage internally in our business. But it's also going to compound the complexity our customers face to take advantage of it. And so we added a couple more charts in there on the right side.

You'll see one on the top right that illustrates the number and also read the scale of models that are available in [indiscernible] phase. And on the bottom right, we have a chart that shows the increase of developer productivity. And we've all seen the shocking ascent of coning agents over the past few months. But really, this is only the continuation of a much broader trend that has been going on for decades.

When we started the -- way back when you had to write all the code yourself in low-level languages. And then you could use higher-level languages. So you became 10x as productive. Then the Internet came along and you could learn about everything so much faster, so you get another order of magnitude and productivity. Then we saw the assent of the open source software in the cloud. So you could use [indiscernible] components, so a full end-to-end services and then you get another order of magnitude of productivity. And today, with the rise of coding agents, I think we're starting at the next 1 or 2 other productivity right now.

So this is the prime result. To put it simply, Datadog exists to solve this enormous problem of complexity for our customers. We connect to all of their software components. We scale with all the infrastructure, compute units they deploy. We also scale with the services they create and deliver. We understand the way infrastructure and applications are changing, and we connect separate teams to each other across different functions.

Now what's interesting is that none of this problem are going away in the age of AI. In fact, the complexity is even greater because AI, all those things to be built faster. There's a lot more of everything. And the stakes are much higher as agents start to act on their own [indiscernible] humans. Our response to win this race against complexity is to invest, to invest in innovation and to invest on behalf of our customers.

Over the years, we've invested about 30% of revenues into R&D. In 2025, we invested over $1 billion in R&D and ending the year with about 4,000 engineers. We believe that we are investing several times more than our largest peers. We are also adopting AI in R&D and [indiscernible] products, which gives us unparalleled leverage and velocity in our space. That investment has led to an expansion of our platform over time and our successful entry into new categories to solve more problems for our customers. We delivered [indiscernible] on the unified platform. that break down silos among what used to be disconnected teams and data sets.

Now all those categories you saw on the previous slide, are part of the critical user flows that make up the activities of our customers' businesses. And what we see here on this slide is one way you can model on the customer side, the continuum of problem areas that take customers from tech innovation all the way to realizing business value. So you have writing code all the way to the left, and understanding where the value is and running the business all the way on the right.

So we started right in the middle in observability. Of course, we didn't have all the functionality listed here from day one. We added a lot of it over time as we kept going deeper and kept covering all the new technologies our customers were adopting. And this includes new innovations to help our customers observe their AI stack as you can see on the highlights there. More recently, we've added capabilities around the data layer bringing observability across the entire data life cycle from injection to transformation and all the way to downstream using reports, applications and AI models. And the market for data observability is picking up quite meaningfully as data is critical to developing and adopting AI. Data quality monitoring, in particular, resonates with customers.

Looking to the right, we have been building a great business in digital experience. We are now expanding further into user and report analytics, where we are seeing rapid customer adoption. And we are improving value for customers with our [ Synthetics and RUM ] products that can scale very cost-effectively to extremely large consumer user bases. And what we found when getting all of this into the head of customers, is that by broadening our scope and removing painful integration points, we see the value of our platform go up dramatically in surrounding areas.

Now looking left on the developer side. The developer landscape is evolving very quickly with [indiscernible] tools and rapid deployments. And so the market for tools that have developers build, deploy, debug and iterate, is expanding very meaningfully. So we've been bringing more value to developers with capabilities such as feature flags, the Datadog MCP server and the [indiscernible] agent. And we have much more to do in the software delivery space. You will hear later about this topic from Michael.

Security is a concern that spans from end-to-end across a spectrum of development, production and user interaction. And our suite of product delivers against that need. This is includes helping find venerabilities in development, identifying and eliminating threats in production, and securing sensitive data around live user activity. And we are building AI capabilities to move faster and [indiscernible] security problems. You will hear more from Tim on this area.

We've been building our cloud service management products as well. What this involves is going beyond helping our customers understand and secure their systems and into helping them coordinate people and teams to manage, communicate, organize, take action and more and more automate response. So we are building on our momentum in this space, including the successful launches in 2025 of our own core product and [indiscernible] SRE agent. And Yanbing will share more about that.

So if we look at the platform altogether, we are delivering an end-to-end suite of capabilities that help our customers build faster, deploy confidently, fix problem rapidly and deliver better business outcomes. And we are breaking down silos across operations and DevOps teams, data engineers, product designers, developers, security teams, incident responders, FinOps teams and business users. We have much more to do in each of these areas, but we've made meaningful progress over the years and are seeing broad customer adoption, which shows that we are delivering value.

As proof of the value, we're delivering across multiple categories, as I discussed on the earnings call a couple of days ago, we now have $1.6 billion of ARR in infrastructure monitoring another $1 billion of ARR each in both log management and the end-to-end APM and [indiscernible] suite. The fact that we have real balance across the 3 pillars of observability as well as meaningful scale in each one shows that Datadog is unique within the industry in establishing true platform value for customers. Even though you've heard a lot of competitors say they have 3 pillars capabilities, their business typically remains driven by just one of these pillars, which dramatically reduces the value, they're able to deliver against customers explosion of complexity.

All right. So I want to talk a little bit now about our AI build-out and break it down into 2 buckets. First, we're building AI for Datadog. We're embedding AI across Datado who -- every type of engineer can move faster. Our AI agents now surface context, identify problems and recommend fixes faster than any human can. They can be proactive and get in front of issues and users can interact with them in plain English.

The second category is to cover the AI applications or agents our customers are building or running themselves. And we call [indiscernible] Datadog for AI. If you're putting LLMs or agents into production applications, those systems do need observability. They need to be monitored like any other critical app. In fact, as I mentioned earlier, this takes [indiscernible] hire as AI agents can now take action. So we're building a full stack of products, so users can understand and improve their AI solutions.

So here's that AI for Datadog bucket, but laid out across our platform. So as you can see, there's capabilities across every single layer of our platform now, and we're building more. And here are the capabilities in the Datadog for AI bucket. Same thing here. AI-specific instrumentation [indiscernible] every single layer of our platform, and we believe we are uniquely positioned to win market share in each of these areas.

The last time we had an Investor Day, 2 years ago, we told you this. We wanted to make our customers [indiscernible] more productive for every single step going from code to business value from end-to-end. We call this closing the loop. We are well on our way to deliver this vision on behalf of our customers. We do this across 30,000 customers from the most tech-forward native companies to the largest Fortune 500 companies in every industry, in every geography around the world.

Here's the loop that we've been helping our customers close. For the production systems, it's an end left cycle of making or incurring changes, figuring out how to understand the affected systems and fixing problems as they arise. It's complicated, it's frustrating and it's expensive. And today, we save our customers time and money by breaking down silos across teams and data sets, and we close these loops tighter, faster and cheaper.

But there is a second key workflow that is becoming increasingly relevant, thanks to the rise of AI coding. And that's the loop that moves software from development to production. The time spent and value created have to this point, been heavily concentrated on the build part. But with the rise of AI coding, software engineers will work much faster. Already today, we have at Datadog experienced engineers who are building features 10x or 100x faster than they could before. And I think I've seen some of them in the room today. So please don't hire them away anywhere else.

So the value is instead shifting quite rapidly from coding to being able to evaluate changes and deliver business value. This is the space where code meets production environments, and that is exactly what bringing 100x more code to production will not be easy. The tough part is making it work in the real world. It will come into contact with all the other coding components in the environment. It's going to need to be reliable and scalable while being cost efficient. It will need to maintain the security of the business and user data. And in the end, it will have to deliver great business outcomes.

As you hear today, we think our investment in innovation, deep domain expertise, large and diverse customer base and massive [indiscernible] of data are all factors that will help us be a critically important part of the solution to this problem.

Our place in the world is right where code meets production environments, other applications, other agents, end users, and broadly speaking, the real world. And that is what we think is the most impactful place in AI development Datadog. At Datadog, this is what we've been building towards for the past 16 years. We've been around long enough to be part of the transition from monitoring to observability and to drive the adoption of DevOps at the time when cloud was just emerging.

With code development accelerating potentially by orders of magnitude, the problem we solve is expanding to be an even more important pervasive and valuable one going forward. And solving this problem will take us from observability to the edge of autonomy. Enabling autonomy will mean that we validate our customer systems, apps and agents. And that we do so as they are increasingly AI coded or rely on probabilistic AI models that are, by definition, harder to test or predict. It will mean that we help maintain their security and safety that we keep our customers' agents aligned with their intent and constraints. That we give our customers the right control mechanism and automate their feedback loops, and that we verify that every change generates the expected business outcomes. So that's where we're headed.

We think we have a unique opportunity to enable autonomy for our customers across development, operations and security to support our customers in their goal to rapidly deliver business outcomes accelerated by AI, to bring everything together end-to-end and to give our customers the ability to harness complex new technologies and deploy with confidence. This is the latest and by far, the biggest area of opportunity for us. As you'll hear later, our market just observability is very large and growing quickly. And even though we've successfully grown our business over time, our market share is still only in the mid-teens. And as I showed you, we have made significant headway in building capabilities in other areas, and those do significantly expand our addressable market.

Furthermore, we are building both AI for Datadog and Datadog for AI. And we're embedding both broadly and deeply into the Datadog platform across every single product or use of AI. And we're already delivering AI capabilities that our customers [indiscernible]. Meanwhile, our investments will deliver a quickly accelerating pace of innovation as our engineers themselves built with AI and [indiscernible] technology. And we are very focused on deploying those investments to achieve this vision of enabling autonomy for our customers. If we can get this right, the sky's the limit. And this is why we feel that we are still just getting started and barely scratching the surface.

Now we'll turn it over to Alexis Le, who will talk about our data-driven advantage and how we apply that to NextGen AI.

Thank you. Thanks, Olivier. Hi, everyone. My name is Alexis Le-Quoc. I'm the CTO and one of co-founders.. So I think Olivier gave you a clear picture of the [indiscernible] complexity that our customers operate at and presented very clearly the opportunity that's ahead of us. Given the platform we have and the platform we've been building for 16 years almost.

What I want to do now is explain further why we at Datadog are uniquely positioned to use AI to deliver on this vision. So data is unique in how much data we get, how much data we have, but also how much we know about infrastructure applications and systems running out there. We ingest data at a significant scale, trillions of data points, billions of traces, exabytes of logs. We also have a diversity of data that users are selling to us about their system. This could be besides metrics, traces, logs, user sessions, data jobs, lineage, LLM and agent traces, team structure, service names and many, many other pieces of information. And they come from our SDKs, from our agents and from our crawlers, from integrations. That data is, of course, what powers the Datadog of today. That gives the current observability to our customers.

But it is also the foundation for the AI needed to deliver fully autonomous operations. A few years ago, we started an AI research lab because we were convinced that given the amount of data we have and given our R&D capabilities, we could be leaders in building AI specifically for observability and security.

How did we start? So we wanted to prove that having lots of data, lots of domain-specific data give us an edge and thus was born our first foundational time series model in Toto. Now if you look for the largest public data set of time series data, you get to about 300 billion data points. And it covers domains that you'd expect; finance, health care, energy, transportation, some web traffic and so on.

But we train Toto on 3x that amount. And the vast majority of the data we have, we use for training, for pre-training actually, is completely unique to Datadog. And it's all related to applications, infrastructure, software systems. So as a result, when we compare Toto to other time series model and its ability to forecast, it performed far better than other models out there. So we reach state-of-the-art. And from there, what we did is, we released it as an open [indiscernible] model and hugging phase that -- I think that was last May. And we've seen a significant uptake since about 9 million downloads.

Now you may wonder why release it as open [indiscernible]? What's the point? Well, for 3 reasons. One, and maybe importantly, we want to contribute to the field. This is a nascent field that I think can use all the help it can get. Number two, it was important for us to establish our credibility as an AI lab. And number three, because it's actually a way to understand how important these models are by just seeing how many downloads they get. But I'd say the most important difference that we notice is the difference of cost compared to AI models you use every day. We spent about [ $750,000 ] to train this model in 2025, which was, at the time, 3 to 4 orders of magnitude cheaper than a frontier model of the same vintage.

Now sure, frontier model can do a lot more than what Toto can do, right? It can speak hundreds of human languages. It can review and amend legal contracts or analyze medical imagery. But none of that matters, right, in the context of observability. And with Toto, we can show that -- we get -- we showed that we can get good results with lots of proprietary data and small models. But I'll get back to that.

Let me talk a little bit about training. So really, when you have a -- when you're creating a model from scratch, you spend a lot of time and money on the pre-training stage, if you will, training is an important piece. And it's both pre-training and training are essential to produce models and agents that are useful in real-life situations. So let us see how we're training the Bits AI [ SRE ] agent. And if you're not familiar with Bits AI, sorry, it's what's in the name, it's effectively a site reliability engineer. It is tasked with finding and building a plausible causal chain starting from a symptom on a software system somewhere and sort of building the chain from there.

So when an alert signals an issue in an application, for instance, Bits AI SRE, develop hypotheses about weather problem, where the problem is coming from and [indiscernible] is all the data available. And the goal is to identify the root cause that can then be addressed. And so that the symptom goes away. And as you can imagine, and you'll hear more about Bits AI SRE, it's very popular because there are always problems out there on software stacks around the world.

Now in order to train an agent to correctly identify issues in production, we need a solid baseline of past incidents and correct root causes. And here's how we've done it. Like any complex system, our own platform is constantly evolving and constantly being maintained. Think of it like the [indiscernible] around us. The streets need to be plowed, [indiscernible] needs constant attention and effort to fight entropy, so that life can keep going.

So in the course of any day, our engineers investigate in fixed issues. And what they do to is the record their finding as well as the entire set of ability that was needed to reach a conclusion. And they turn that package into an evaluation or an eval. Because that eval comes from human analysis of an expert, and it was used successfully to troubleshoot and fix an issue, we know we can trust it. And every time we make a change to a model or a model instruction we run through the growing body of evals and see if the model gets better or worse.

So this is a chart behind me showing a number of evals that our engineers have recorded over time. This is not something that can outsource to nonexperts out there. And it is important that you cover as many use cases as possible. And for that, you need a large infrastructure to do it, and we have the large infrastructure.

And again, going back to the city analogy, to keep New York City up and running, it takes a lot more variety and scale of effort than if you only have to keep up a small village, for instance. And so as we recorded more evals, we've seen the accuracy of Bits AI SRE it has gone up, not as a straight trajectory, it's up and down, but we can see generally the trend. And so that's how we've improved the quality of Bits AI SRE. And normally, we're going beyond this sort of human curated set of evals in building. So [indiscernible] is to generate synthetic data, and that's really to reach an even larger scale. And we think it's necessary.

And as our customers have begun to use Bits AI SRE, they've actually -- the -- sellers feedback, right? This is useful, not useful. This has worked, not worked, and here's why it was useful and and why it was not useful. And we, of course, use that as evals. And that's great because it continues to enrich the diversity of environments and problems that our agent faces. The reality is there's no shortcut in training -- pre-training and training to get this kind of high-quality result. You just need to have lots of data and expertise. And that we feel is a strong differentiator for us.

So let me maybe sum up our advantage. One, we have access -- continuous access to lots of clean and rich data that we can use for pre-training, for training and -- of financial models and agents. Number two, we are building our own models. Number three, it's not only the shared amount of data matters, it's also the diversity. It has to come from a broad context. That's very helpful. And before, we bring to bear our domain expertise to improve our model and agent performance. So we think that we're unique in our ability to deliver the best in observability powered by AI [indiscernible].

Now you may wonder, okay, that's great. Why not just take the data and throw away out of the frontier model and see how it does. And thus, you would replace data [indiscernible] with just a bunch of data in frontier model. And maybe you have the data to do that. But first of all, is it enough? And what you'll get is you'll get -- so we've done that internally because our customers probably do that. What you can do is you can -- the Frontier model is going to be great at summarizing data. You have to give it a bunch of context. You're going to get some good results. The main problem is extremely expensive.

So here's the idea. Here, I'm plotting costs and accuracy, if you will. And read -- first of all, training a frontier model, it's starts at $1 billion. And it's unclear how high or how the accuracy slope trends as you go orders of magnitude past $1 billion. Whereas the research is telling us a different approach works and this we think is better. We've built much smaller models on the exabyte of data we have. And the small models, what we've proven is that they have orders of magnitude better accuracy per dollar because simply, we're not paying for things that frontier models have, but we know won't be useful in the context of observability and security. And even I'd say if you hire lots of engineers to fine-tune and do RHLF and so on, it's not going to be able to match the accuracy at the same cost. Because really, when you use the frontier model, observability is you're paying for the amortized cost, the pretraining, training and on top of that, all the -- so the vast quantitative hardware that's needed to run in front of the Frontier model.

So in summary, one, we believe that the autonomous operation needs very good models at a low cost. And number 2 is the approach with small dedicated models, lots of [indiscernible] training data and ongoing evals, we think, is the way to go. So I've shown you the reason why we build our models and also how we can improve them based on real life observation.

Now you may wonder how do we tie all this together to get to fully autonomous operations because autonomy requires validation, safety, security, alignment and control. In other words, we have to go beyond only observing a system. We have to understand its behavior and maintain control through verified changes. So in our lab, we've built, as I mentioned, a bunch of models, but we've basically understood that what we need really for this domain is a world model.

So how are we going to do that? We take all the data we have, and it needs to encode and represent the state of distributed system. Now we are already in a position to observe these systems, right, because we plugged in everywhere. We get that -- these exabytes, trillions and so on of data every single day. So what we're working on right now is an optimal representation of code, system structure, system behavior. And we think it is essential to predict the future behavior of software with high enough accuracy. For that, you need to understand past behavior and have knowledge on how such [indiscernible]. Without it, it's not going to work. Without -- and finally, without high accuracy, they won't be autonomy. So we have to get to a high accuracy.

And when we think about steps towards autonomy, we think in terms of stages, here represented on the right-hand side. Started with customized and adaptive observability than proactive alerting, automated remediation. And then you get to the predictive and proactive and preemptive sorry, -- and finally, you get to autonomous operation.

And with bit SRE, we've tackled the first stage, automating away sort of the more manual steps of observability. So the need, for instance, to build dashboards by hand and so on is gradually going away. And we've built proactive learning and automated remediation with the goal to take care of an increasing number of cases when currently people are seeing the loop to fix things. Now there, we think our trajectory is going to be like that of coding agents, limited in scope in the early days, more narrow, but increasing as time passes as we refine our models. And as that happens, as accuracy and coverage continue to increase, we'll be able to further predict and then preempt and prevent issues before they occur. When we reach that, we have a self-healing, self-managing, self-optimizing system, able to operate customers' infrastructure and application with fairly limited human intervention. And that is our goal, as Olivier said.

Now there's obviously a healthy amount of work to get there. But we think we're uniquely positioned to make it happen. That is where we're going because that is the only worthwhile goal. But for now, let us here about what we're delivering for customers today.

And for that, I'll hand it over to Yrieix.

Thanks, Alexis, and hi, everyone. So my name is Yrieix Garnier. I'm super excited to be here today for my second Investor Day. So Olivier and Alexis talked about how we're moving forward towards autonomous operations. In these sections, I want to take it back to its foundation, the Datadog platform. Platform is what Datadog started. And over the years, it really helped us break down silos amongst teams and data. So to do that, we've built a robust set of elements, thousand of integrations, common UI, data services. And we have about half of the 4,000 engineers working on the platform. And the investment allows us to be evolved very quickly and to seamlessly integrate AI to better serve our customers.

Bits AI, this is how we're calling our AI capabilities is really present throughout the platform. Let me give you a few examples. So it can analyze and correlate all the different data types that we're getting from our customers, to detect, investigate and remediate code fixes, or interact with Datadog through natural language. And we're just getting started with more AI capabilities to come.

But the platform is also critical for a rapid pace of innovations. By leveraging the platform and all these building blocks or engineers, team can really stay very lean but also move really quick to deliver either new products or enhance existing features. This is really the flywheel between platform and product. For instance, it helped a handful of engineers to deliver companies autoscaling end-to-end solutions. Or after the Metaplane acquisitions, we were able to take to market data observability in a record time. So today, we have dozens of products, and we continue to advance our platform to accelerate this flywheel effect and build even more product faster.

But let's look at the data injection side. We've also added a number of data sources including like valet, LLM inferences, and we can ingest extremely large amounts of data to provide analysis and correlations in the context of our products. But our end goal is really to deliver value to our diverse users, but also to the uprising number of agents. They all need the data in one place. We need to provide the best outcome.

As Olivier said, our customers are facing a rising level of complexity. And our job is to stay ahead of that. And if we do all that right, we provide a single set of truth to break down silos across all users and agents. So the platform actually come a pretty long way over the years. Let me reflect on that.

In 2015, we started with few integrations. [indiscernible] millions of events per hour and only had one product in from monitoring. But our customer needs and demand grew really exponentially. And today, we have over 30,000 customers, including some very, very large ones. We have 25 products and we can handle trillions of events per hour. As you can see, our platform reached unprecedented scale. And it's one of the main reason our customers keep choosing Datadog as we can store, process and move data with always improved performance or cost efficiency.

But let's look at this from a customer angle. This chart is a very fast-growing AI company. The customer demand grew very quickly. But where the end user were experimenting bottlenecks, they needed a unified [indiscernible] solutions to get to the root cause as fast as they could. And to do that in about one year, they adopted 16 Datadog to products, and that was really key to their business success. So that's a platform.

Now let me speak of the way we're scaling and our customers just retain and analyze the growing data sets. So to expand scale, let me take the example of the log management. We launched it back in 2018, and it had multiple scaling phases. The first phase was called logging without limit. And it's all about ingesting all your logs without limit and only processing the relevant ones with correlations with infra monitoring and APM. So bringing it logs in a valuable but still economical way is really the foundation that helped grow that business 7x to $1 billion ARR. But really, it's the foundations of our customers value. And it worked great for observability logs.

We also knew that we are only capturing a fraction of our customer logs. Of the use cases like transaction logs or audit logs typically involve log volume that were other magnitude larger and which needed to store for a much longer period of time.

So as the second stage of growth, we've extended the capabilities to support those use cases with Flex Logs, Frozen and [indiscernible] Search. By doing so, we've unlocked new market opportunities and delivered more and better outcomes to our customers. And it actually worked. We saw very strong adoption of Flex Logs from the start. Now our customers are storing tens of trillions of events and Flex Logs is approaching $100 million ARR and growing very rapidly.

But to keep pushing on this log journey, we've leveraged the platform to build a new product, Cloud SIEM. Cloud SIEM is actually a natural extension for text logs and it requires logs to investigate security issues over a long period of time. And Flex Logs really unlocked the security revenue. With 18x growth in 5 years, we are still seeing an acceleration in Cloud SIEM adoption. And if we look at this, we are still actually at the beginning of that opportunity phase here.

Sorry. So as you can see on the chart here, Flex Logs really unlocked that security revenue. It grew like 18x in 5 years. And we do see that acceleration. Yes, I just already said that. So Tim is going to talk more about this as we actually do see that the beginning of that opportunity.

So let's now look at the impact of the platform data scale and actually a whole customer journey. These e-commerce customers started using infra. Then in 2022, they adopted APM, Synthetics and Logs. And in Q4 2023, they become a Flex Logs and SIEM customer. As you can see over time, they consolidate more tools within Datadog, which enabled them to address an increasing number of use cases, cover more environment, larger data sets, and most importantly, enhance their customer experience. And that's how Datadog and customers really scale together.

So to finish, I want to talk about enterprise coverage. This is where the platform effort help our largest, our most sophisticated customers to drive tool consolidation. Beyond monitoring everything in the cloud, a number of our customers asking us to cover more of the environment by combining cloud and on-prem. With our extensive integrations, this is something which is out of the box for Datadog. We're already supporting on-prem servers and network. And now we also monitor wireless access point, end user devices, like laptops and desktops or edge devices. So with that, customers can really see the entire physical footprint in one place and also combined with the cloud environment. And for us, it means a larger footprint and a bigger market opportunity.

We also continue to expand our coverage across our customer tech stack. Historically

[Audio Gap] for cloud security, we provide posture scanning, run-time vulnerability detection, attack analysis and combine that with observability enriched prioritization to tell you what cloud risk matter the most. With AI and data security, Datadog automatically secures sensitive data and offers comprehensive run time protections for AI agents to enable safe AI transformation.

And finally, for developers and DevOps teams, we have code security, helps to identify vulnerabilities and first-party code, open source libraries, infrastructure code, all before they move to production and therefore, dramatically reducing alert fatigue. And with Bits AI, code security generates bulk remediations, so developers can still deliver secure code while getting to spend more time on building and innovating.

Now plus our security products, because they're built on the Datadog platform, they can all take advantage of Datadog's shared services to accelerate remediation. For example, integrated case creation and incident response to security, SRE and DevOps can rapidly collaborate with one shared view, or even one step further, agent builder can be used to build custom automated remediation workflows. So we have growing proof that the data log advantage that I just outlined of unifying security and observability is delivering value to customers.

Today, Daydog has over 8,500 customers using our security products. This includes one in 4 of the Fortune 500, and we've now surpassed $100 million in ARR. And you know who else is using our security products, we are. The Datadog security team secures the Datadog platform for our 30,000-plus customers using Datadog. We put our reputation and our business on the line using our own products as an indication of our confidence as a security vendor. So we're off to a good start. If you wanted to pause [indiscernible] okay.

So we're off to a good start. We believe we have a big opportunity ahead of us. Today, 70% of our $1 million customers use one or more Datadog security products. But the spend security represents, it's together is only 2% of their Datadog spend. So as a result, we see potential for much more wallet share as we deliver more security products and capitalize on that Datadog advantage.

Let me give you an example. Here is a long-time Datadog customer in the media market, who over time has adopted a number of security products from Datadog. They wanted a unified observability and security platform, and they chose to consolidate on Datadog over market alternatives like Palo Alto, CrowdStrike, Wiz, Google, Microsoft, just to name a few. As a result, today, 20% of their Datadog spend is on our security products. And that could just be the start.

For another one of our $1 million customers, 38% of their business with us is security. So we see a clear opportunity with all of our current and future customers to go deeper and broader with security. And how we get there is the value of the Datadog advantage. It combines and correlates security and observability in a truly unified platform.

So that's it for me. Let me hand it over to Michael to talk about what we're doing for developers.

Thank you, Tim. All right. I love seeing everybody taking a ton of note. Anybody using AI right now in this very moment? I knew it. There are some people out there -- advantage, right? So I'm Michael Whetten.

So we live in this era of speed right now, right? I don't know if you all feel it, but my customers are under intense pressure to compete and innovate and build and ship product that works to customers and try to scale out as fast as possible. And it's been like that I've been in Datadog almost 10 years now. It's been like that since the beginning with the cloud, this competitive advantage that technology can bring but it feels like it's accelerating, right?

But as Olivier said, the complexity is a drag on their ability to innovate, right? The big companies have a lot of complexity, and they're feeling the drag in different ways than small companies who are having trouble getting to scale, right? And they have drag in different ways and different types of complexity.

And if there's one thing that you take away from today, it's that -- I know some of you have traditionally written about Datadog as kind of an insurance company, utility company or some -- must have for companies that scale. But more and more, my customers are telling me or they're adopting even the fastest-growing companies in the world right now who are in the most hypercompetitive landscapes, they're buying Datadog because they need to move fast and Datadog enables them to do so.

So let me walk you through a few examples of how I see this working. So one type of complexity that we see a lot is fragmented visibility, observability or monitoring or product analytics or whichever point solution they have here. So here's a simplified diagram of an application, a single application at a company. The fragmentation, each of these tends to represent a different team at a company, right? And even though it's a single application, and this user is interacting with your application, making requests, getting response traversing through whatever your application does, the fragmented nature of the organizations that are required to serve this user, they don't always provide the best user experience so that when an issue happens, right, if I go to a lot of my customers right now and I say -- or potential customers, usually, I say what's -- my favorite thing is kind of a [indiscernible] question.

But my favorite thing to ask them is where -- how do you know that things are broken now? What triggers an incident? And most of the time, those cheapestly look at the floor and say, well, customers, like a support team where somebody tweets something is down and then we call an incident and immediately start reacting. And some of you are nodding because you must hear this from people as well, right?

And the problem is, is that all of these different teams are using different tools. So we have a bit of a tower battle problem, right? And who knows that these signals that are going off even point to the root cause of the problem. So to use a real-world example, there's a major global bank in which they have 5,000 engineers. So that was one application.

Now at this bank, there's hundreds of business units, and each one has many applications. So 1,500 applications spread across the organization, right? And the problem that they have is these -- the way that the organization is set up doesn't match the user experience, somebody might be trying to make a withdrawal or deposit at an ATM or on their phone. They don't know how the company is set up and whose problem is who. So they just know they can't do something.

But when something goes wrong, these are all interdependent technologies, and troubleshooting this is a nightmare, right? So they consolidate everything on to Datadog. They went to all those point solutions and brought it all in. And the beauty is that, not only is it just one single pane of glass, the real advantage is that from the time of collection till the time it lands in Datadog, we're automatically summarizing and correlating all that data. We're making sense of that data. So that when it lands here, most of the work is done. And when a signal goes off, it's typically in the right place, notifying the right team and they can [indiscernible] responding much faster so that you have global impact within the organization just from consolidating onto one platform, right? So millions of dollars a day in avoidance, better customer sentiment.

But the real advantage, the real value isn't actually displayed here. The real value is all those people that were responding to those incidents that were taking hours and now take minutes. Those are sometimes the highest paid or smartest engineers at your company. And rather than building value for the company and living the bank forward into what is the next generation of bank software, they're spending time and incidence. And here now, they can spend time adding value for the customers.

So ripping and replacing load-bearing technologies that have been there for 10 years across 5,000 engineers, it's not super easy for a lot of organizations, right? Either it's a mandate as it was at that bank or a trend that we see happening now to address some of this complexity and scale is a new movement that we're seeing where people are calling it user-first monitoring. So much complexity on the back end, so many teams to coordinate, so much politics to try to coordinate. Can we just start with the user experience and start making our way backwards, right? What are the most load-bearing critical user journeys that we need into in visibility and start traversing the organization that way?

And so the crux of your business really is your application front-end stack. That is what your developers are typically making, right? And then they scale it out with infrastructure network. So we have a suite of products for this solution and the space is called Digital Experience Monitoring. But really, it is what is the user experience, how are they impacted by the changes my engineers are introducing into production? Are they making things better or worse? And so in some ways, this is accountability software. Are my engineers who I'm paying who are there to bring value to the customers, are they making things better or worse for the end user?

So we see a big push towards enabling engineers to have direct visibility into the business impact of their changes. And so this is one of our fastest-growing areas. And we see that -- when the APM, which is the back-end instrumentation and the front-end user experience are stitched together, it's a story that works and it spreads through the company much faster and it spreads through organizations much faster, and we see that when these things are together, we have -- we provide a lot more value, which turns in more revenue for the business.

And this isn't unique to digital experience and APM bundling, as customers grow with us, they do find more value. The land and expand does work and more products equals more value, spreads in more parts of the company until they have that consolidated approach. Why? Because this is better for the human responders, right, bringing more people into the conversation to troubleshoot faster together is meaningful and does increase the value.

But an advantage of this single platform and that automatically correlated and summarized data that we do is it's better for our AI agents, too. We found that when the AI -- when we have the full context of the entire stack and is already correlated for humans, AI also operates much better on that. It can act faster, right? So what might take a team of people before many hours to solve, but then they adopt Datadog and they can do it in maybe one hour. The AI can come to the same conclusion in minutes, and we'll hear more about that soon.

But it can traverse. This is, again, one application. It can reverse all the applications. It's working 24/7, right? And so the advantage here is when it has -- you can't separate the AI from the underlying data formats and context. There's a lot of work you can do to make the AI more efficient, faster and more important, more accurate.

So Datadog does bring equilibrium into that DevOps life cycle. So the developers can develop as fast as they can. What are they doing? They're introducing change into production. That's their job is to introduce new things, but production can adapt to that quickly and make sure that it's done safely and securely. At Datadog, we benefit from this. The reason that we're able to ship so fast is because we use Datadog. And so we are the embodiment of that DevOps life cycle, and we do have that equilibrium. This last year, we released -- we announced and released hundreds of features into production, right? These aren't ideas, they are actually out there in customers' hands, iterating with customers, proving value.

Now as has been said already, there's a sea change, right? When I walk around Datadog office, I see a lot more engineers coding on their phones, having multiple windows open, directing lots of agents and writing code 24/7, they say it's addictive, right? And I'd say it's only accelerating. We've got one guy who's coding with his glasses, right? He's sending prompts and he's saying, looks good, now change this, right? And so this is a real movement in the industry right now.

But what does it do? We don't hire developers just to write code. So 100x more code might not actually translate to 100x more value. Are you really going to release 100x more features? Are you going to release one feature that's super sophisticated and can do a lot more? How do we verify that this 100x code is good that it works and that is a good product? There's a difference between code and product. So this is -- the bottleneck is no longer coding as Olivier was talking about, but bringing value to customers maybe at 100x. And this is where Datadog has a unique opportunity to help the AI movement forward. So developers love to make changes and bring things into production, but they break things.

The number one cause of incidents is faulty code, right? And that's when humans are reviewing it and taking painstakingly efforts to make sure it's good. But Datadog has automated integrated code testing. We also have the best understanding of how production works, how that code is deployed, how it's connected to other parts of the application, what data is flowing through that code at all times, that we can actually inform the entire life cycle here, whether it's humans or agents with AI coding, we can tell the code that you're writing right now, hey, this code that you're -- this idea that you have that you're going to propose into production, it's going to increase latency. It's going to make things slower because look at all these requests coming in right now and feed that into the agent. So the agent can make better decisions or so that the person whose hand coding can make better decisions and write better, more efficient, more secure code that we already know is going to work in production.

Now as you're sending it out, right, there's a risk mitigation strategy that already exists. You write your code, you write unit test, you run those tests locally. There's Git hooks in there that's going to run all your test locally, then you push your change in some centralized pool of [indiscernible] called CICD and it runs a bunch of tests and those tests, sometimes they pass, sometimes they fail and you have to kick it and it goes again. Then it will land in a staging environment where people kind of vibe check it, type of few things, looks okay, let's push it into production. If you're more sophisticated, you got feature flags, which means that you're going to slowly roll it out region by region, 5% hit it, 10% hit it, 15% hit it, what are you doing? You've got Datadog at least you've got monitors there to tell you if things are going off. If you don't -- usually, you're just praying and waiting to see if requests are going to come in, we can actually do much better than that.

We have automated testing and we just launched our feature flags product, which is unique in the domain in which we can actually contain or sandbox that change. When it goes to production, we can attach metrics to it automatically. And as it starts to roll out, we can statistically tell you this is going to be better. This is going to be worse. Roll it back or go straight to 100%, right? And so that's feature flags and experiments.

So but the gist here, and I can talk about any of these things, but the gist is we can automate much of this, just as AI is pushing more code or AI and the manual tooling that we're enabling for customers as well, which the AI will use, can accelerate this path into production. So rather than becoming a bottleneck or a super high risk and just breaking things all the time, we can reestablish that equilibrium even at the pace of AI.

So that's -- my throat is getting dry, but I am almost through it. So the AI -- or sorry, the code gen that most people are doing right now is to just build existing applications. And there's some exciting stuff. Technology does empower the creative individual, right? So I had -- somebody last night told me that from the time they got a white paper for a new database -- this person works for a database company. So research paper, they went [indiscernible] the idea into a working prototype and they thought they were so smart and novel and they were the third one on GitHub to have put it up. And this is like in hours after the white paper was published, right?

And so this is what I mean by speed. Well, everybody is trying to take advantage of -- they're trying to build more intelligent applications and they're trying to also see if they're going to be safe in the environment. So it means that us as Datadog, we have to continue to build new types of products, new types of observability, new types of security to be ahead of the curve when people need to start bringing these things into production.

So one of those things -- when I do talk to CEOs, CTOs, CIOs, one of the big concerns right now is the proliferation of agents in their company. What are these agents, right? Who made them? Why are they there? How much do they cost? How are they permissioned? What are they supposed to be doing? Are they doing a good job at that, right? Am I getting value? I see 5 of these agents that are supposed to be doing the same thing, should we choose one of them, right?

So the AI agent [indiscernible] gives leadership a way to actually track and teams who are building these things a way to track all of these questions about the agents that they're bringing into their market. Now those who are building these agents are trying to enable their product with more intelligence, they're also stuck unless you're at a leading research lab or Frontier model lab, you probably don't know where to get started. And so there's a lot of information out there.

The nice thing about AI observability product is that it comes with out-of-the-box framework. So just by using the product, you start to understand more about if you're already an expert and you'll be familiar about these tools. If not, it's almost an on-ramp in how to think about nondeterministic applications. I need to evaluate these things for basic competencies before I send them out into the world. Once they're in the world, I can experiment to see what is good behavior, what is bad behavior or trending which direction, I can try different models, et cetera. I can sandbox and I have playgrounds, all the stuff that these researchers and AI engineers need.

So the momentum on this is growing. There's been a lot of investment in learning over the last 24 months or so, but we're starting to see these things try to get into production and who helps people get things into production is Datadog. So that's why these products are growing with us now.

And the last thing I'll say here is we're not only getting started, right? This movement and helping this movement to figure out what it is and what it will become. Datadog is perfectly situated to enable that and to talk more about the exciting things we're doing with AI is our Chief Product Officer, Yanbing Li.

Thank you, Michael. How's everyone doing? This -- you are getting to the last stretch of our first half. So my name is Yanbing Li. I joined Datadog as the Chief Product Officer about 18 months ago. Before Datadog, I spent time at Google, responsible for the observability function powering Google's [indiscernible], infrastructure and services. So when I went to speak to a senior SRE leader to get some candidate feedback, this is what they tell me. They simply said, go look at Datadog. And this was back in 2019 before Datadog was even a public company.

After Google, I got to lead engineering and product at autonomous trucking company or innovation, actually, the very first company to operate commercial driverless truck on the U.S. public roads. At Aurora, get to learn firsthand what it meant to ship safety-critical autonomy product into production and at scale. So this is why I'm excited to be here at Datadog to help our customers ship faster without breaking things and operate reliably and safely, all while navigating the increased complexity of AI.

So let me circle back to this DevOps loop that Oli showed earlier. This is the reality of what our customers DevOps team leaves through every day. They need to detect issues as they emerge they try to investigate and find a root cause and the next step of action, and they take action to remediate back to health. And because systems are always changing, with new code, new traffic, new dependencies, this loop just doesn't stop.

So what happens when a major accident -- when a major incident happens to a production system? Our largest customers often tell us, they need to mobilize tens or even hundreds of engineers because they bring different knowledge, different data, different tools and also different system boundaries. And also, most of those teams, not only they have a partial view, they're motivated by proving it's not their problem rather than finding the problem.

So the area under this curve represents the time and times resources that's part of this operating expense. And certainly, incidents, we all know is very expensive to business outcomes with lost revenue, lost customer trust and reputation of risk. So this is a structural efficiency we are trying to solve at Datadog.

So Datadog is in the business of keeping this DevOp loop healthy and running for our customers. So when there is production stress, we detect issues, we help coordinate the customer's team, get the right team involved to investigate and take action to remediate the system back to health. And the previous speakers have already talked about Datadog's unified end-to-end observability platform can shorten the incident response with fewer people, less time and closing this loop faster. So the result can look something like this with faster detection with the right team involved with the right information, they're solving the incidents much faster.

And we all know when you have an incident, time is money. Let me take you through this with a concrete customer example. So this is a major U.S. insurance company who's been a customer for 5 years. Before Datadog, they experienced thousands of severe incidents every year. And with Datadog, after they standardize on our core pillars of product, they begin detecting and fixing those issues proactively, preemptively before they became real production escalations. And as they have seen a 10x reduction in their severe incident count.

And certainly, when they have fewer incidents, when they solve them faster, there is a significant boost to their engineering productivity, and they're saving about equivalent to 70 employee years and translate to $11 million every year.

So what does this mean from their business point of view? Again, with fewer incidents with faster resolution they are actually seeing a whopping 20x reduction in customer impact that's caused by this incident. So this is the kind of value we've been providing to our customer with our unified platform. Now by applying AI, we're taking that to the next level. We've launched a fleet of Bits AI agent. Yes, in Bits with those futuristic sunglasses. And so -- so we're helping our customers autonomously detect, decide and taking action so that we're closing this loop even faster.

So let me give you a few examples of the Bits AI agents and starting with the SRE agent. So you've heard about this several times throughout this presentation. So why SRE? By now, the world has recognized that the future of coding is going to be AI coding. But still, a lot of our customers are struggling to really measure and establish the real ROI. And we pick SRE as our first agentic effort because not only our primary user personas are SREs, but also when an incident happens, it's often acute, high stake. And better yet, the verifiable results of what Bits AI SRE can do is very obvious to our customers. Actually, many of our customers to test Bits AI ASR, they simply play back all of their previous serious incidents and see if Bits AI can get it right.

Obviously, the business outcome is also very tangible when you can reduce incidents. So because of the verifiable nature, our customers are really excited about what Bits AI SRE can do for them.

So how does it work? Don't worry, this is not an eye test. Let's focus on the left-hand side. When alert triggers, Bits AI autonomously investigate the issue. It first gather all the necessary data and relevant context, it then can reason like a group of engineers in different parts of the system, try to establish multiple hypotheses of what happened and then investigate all these hypothesis in parallel.

The right-hand side is intended to show you how that parallel works and is very visually explained to our users. It then identify the root cause and even can propose the next step of action based [indiscernible] and better yet, Bits AI can learn and it's getting better with every investigation.

So the superpower of Bits AI SRE also comes from this holistic understanding of our customers' entire environment, spending systems and applications and users and teams and even business processes. So it doesn't just leverage the rich real-time observability contacts and telemetry inside the Datadog platform is broadly integrated with third-party knowledge stores and also third-party telemetry. So our customers can really get that full picture of what's happening in their system.

Even though we're still in the early days with Bits AI SRE, we are already getting a lot of positive feedback from our customers. Here, you see 2 examples that customers are telling us how Bits AI SRE can accelerate their incident resolution and how it's acting like experienced engineers to help them understand their complex system. And when they matter the most, our customers are also telling us Bits AI SRE get the job done.

So if you remember, the major AWS outage last October, we've got many customers reach out to us saying, when that outage happened, Bits AI SRE was able to autonomously root cause to the outage before being notified by AWS themselves. So even though Bits AI is AI agent, it actually gets a lot of love notes from our customers. As you can see on the screen here, and not just because this is a Valentine's week, we hear this from customers all the time how pleasantly surprised at how smart Bits AI is, how it gets to the root cause, how it's saving them time, how it's boosting their productivity.

And the best indication of that is the actual usage. For new product, we look at the usage metric very, very closely. So since the Bits AI SRE launch, we've had our customers run well over for 100,000 investigations and since our GA last December, this rate is increasing and accelerating. And in January alone, we have more than 2,000 customers run investigation with Bits AI.

Okay. So let me switch gear to talk about how we're using AI for some of our other use cases and products and starting with this security example. We have our Bits AI security analysts in preview. And so this agent can autonomously investigate Datadog's Cloud SIEM signals and conduct in-depth investigation for potential threats and also enable users to remediate those threats all in the Datadog user interface.

I think a better way to explain this is a real example. So a major financial services company was testing Bits AI security analysts for the first time. And actually correctly identified a live, serious security threat. So the situation is a compromised automation system in their environment change their cloud firewall setting such that it's open to the entire Internet. Some of the sensitive management ports are open and exposed. Does this sounds quite serious?

Yes. So without Bits AI, so the investigation would happen that they may receive some security signals and that goes into a queue and human investigate them one by one, and it could take hours for them to come to this realization versus was this security analyst agent could do investigating in parallel. And within minutes, we were able to service this severe threat to the customer. Obviously, the customer did become true believers of this technology afterwards. And this is just an example how Bits AI security analysts can truly transform how security teams can investigate and resolve security incidents.

Let me give another example because Bits AI SRE and security analysts, they're doing investigation or trying to understand what's happening in the environment. What about autonomous remediation. So this is why we introduced the Dev agent. So the Dev agent can automatically analyze the telemetry and code when there's an error happened in our customers' system. It can explain the root cause in plain human language and even map it directly to the relevant code, files and function. And then proceed to generate a context of where fixed and [indiscernible] is important because this fix would be generated based on the real production contact that Datadog uniquely brings to this problem. We can then proceed to test the fix in isolated sandbox so that you have high confidence that this fix is ready to push to production. And all of this can happen without a developer even logs in. And of course, the tool can interact with the developer. When they do log in, they can review the code, they can ask questions and they can help also merge the PR.

So you may be wondering, is this yet another AI coding agent? Now there are already so many on the market. The answer is no. Because Bits AI Dev agent is deeply integrated within our DevOps loop to truly [indiscernible] bringing that full production context, so to help create a better PR. It's also very proactive. A lot of our customers are really pleasantly surprised that they received a Slack message from Bits informing them, there is a high severity error, and Bits already fixed it for them with a PR that's ready to be merged.

So I shared 3 Bits AI agent example. And the important thing is with AI, how our users and engineers are interacting with their tool is also radically changing. So the good news is our customers can use Bits AI from anywhere that's fitting into their workflow, whether it's in their Datadog UI or through collaboration tools or from their favorite IDE or getting involved by another AI agents. And all of these interfaces are also enabled by the Datadog MCP server.

So the Datadog MCP server enables our customers and the AI agent to access Datadog's docs AI-driven observability [indiscernible] from -- directly from their existing workflow. And as you can see on this chart, since the launch of our MCP, we've seen also exponential adoption and growth. And many of the customers are integrating this to their existing workflow. They're also building custom AI agents so that they can build those agentic workflow to help them with incident investigation performance optimization and many other use cases.

So with Bits AI and MCP, now incident resolution can look like this. We can help our customers narrow to the root cause, take action within minutes and with very few people involved as opposed to the tens and hundreds of engineers working tirelessly over hours and days. And better yet, the Bits AI agent can easily work alongside human SREs and human security analysts and developers to make them far more productive. So we're closing the loop even faster. That's the value we're bringing to the customer.

So I just spent the past 10 minutes also taking you through how Datadog is solving the structural inefficiency in the DevOps loop by providing an end-to-end unified observability platform and now turbocharged with AI that we are helping our customers closing this loop very, very rapidly. And as Michael and Olivier alluded to earlier on, we've also been shifting to this loop on the left-hand side to the preproduction environment to help our customer ship better software into production.

So our long-term vision, as Olivier outlined, is to achieve autonomy across Dev, Ops and Security. So that will require us to help our customers validate their system, their application, their AI agents, and in addition to helping them shipping production ready [indiscernible] hold faster and preventing incidents from occurring at all, we have to help them maintain their safety and security to achieve true alignment of their AI application toward their intent and business outcomes. And in the meantime, give them control and feedback to help them improve.

So I am personally very excited about this vision. It is special having built autonomy for trucks now building autonomy for development, ops and security. So at Datadog, we are very excited of this vision that our customers need us to bring this together now more than ever.

With that, thank you, everyone. And I will hand it over to Yuka for Q&A. Thank you.

Okay. Thank you, Yanbing Li. We are going to start a Q&A session. Now joining me on stage are all the print centers you just heard from. Their names are up on the screen. We are -- we're going to be taking questions from the in-person audience. I have 2 of my colleagues, Megan, on your right; Eric, on your left with mics. And we'll alternate between them. So please, yes, raise your hand and wait for them to get to you so we can all hear your question.

We're going to start on Megan's side.

It's Kirk Materne with Evercore ISI. Tim, I was wondering if you could talk a little bit about the silo tax that you brought up. I mean one of the reasons there's always been silos is buying silos, meaning you've had SecOps, DevOps, IT apps have different budgets. I was curious as we head into a world where you need more talent [indiscernible] across all those areas. Are you seeing those budgets collapse into one? And if not, how do you make sure that you're talking to the right person to get more sort of visibility on the security [indiscernible].

Great question. So I think we're seeing 2 things right now. Number one is the recognition that we can make security easier, better with the unification and consolidating on a single platform. And it's more just the [indiscernible] or no longer the need to have those separate budgets for those items because they can get it all through the single platform. So that's one item.

The second point is, I think there's also a broader recognition that, in fact, we use this ourselves at Datadog that having organizationally security and SRE, the same word has a lot of benefits, particularly when it comes across the board for both reliability as well as security. The team is working together. Of course, in our case, using our own platform and tool sets it will work much more effectively. So I think that's something that will also emerge over time is probably a growing trend across organizations as they see the need to be more effective.

Just to add up to that, it's not completely about the [indiscernible] between buyers. But if you think of the impact of coding agents, there's going to be much less separation between the roles like I think as we build a lot more, a lot faster roles that used to be [indiscernible] as how you're building it separate from whether you're building the right thing, which is separate from whether you or not [indiscernible]. [indiscernible], which is separate from who you operate it. I think all of that gets merged together quite a bit as the coding itself and how you build it is left to the agent.

Great. Thank you. Eric's side.

Alex Zukin from Wolfe Research. Thank you so much for the presentation today. I particularly wanted to ask about Bits SRE agent. And given kind of the increasingly heterogeneous environment that is increasing the magnitude, scale and complexity. When you think about the ability to read from other kind of data sources outside of the Datadog perimeter and compiling kind of complete tasks across that information. Can you talk a little bit about kind of where your tool, how you differentiate in that context versus other folks building there? And then maybe some pricing and even a customer example kind of who's at the frontier showcasing.

Great question. So when we started Bits AI SRE, we actually focus on the data and telemetry that's within Datadog because the most important thing for AI agent is to show that they can get it right. It shows value to the customer. And when we are within the platform, we have real-time rich data that allow us to really showcase that value to our customers. Obviously, a lot of our customers do have those heterogeneous environment. So now we're expanding our telemetry to cover those outside data sources. So yes, so -- and we also see there's a lot of other start-ups and company out there that they probably take an approach that's looking from the outside. And so the way we are uniquely able to bring the power for Datadog but also integrate with those external data sources have shown that we can simply generate better outcome and results.

We'll be more right with the data we have and we -- for which we have more -- or I would say more higher resolution, more reach, et cetera, et cetera. If we took out the whole stack, we can be more right, which is also why that's where we started. But I will say, when you look at where the market is today, the market is very active today. The market right now is you have an issue and you [indiscernible] and to be honest, you can get a pretty good result if you ask [indiscernible] to do that. It can ask a number of different systems. So for that particular thing, I think it's okay.

For where the market is going, which is you want to be [indiscernible] proactive and you want to prevent issues that just doesn't work at all because the data doesn't flow through [indiscernible] different systems. [indiscernible], back to autonomy in self-driving. So after [indiscernible] as today, you can send the pictures to ChatGPT and it will help you tell you who was right or wrong, but the crash happened. But ChatGPT is not going to drive the car. You have a separate [indiscernible], you have a separate everything. And I think the same happens is going to happen to us with observability. As we get all of the data as we control the data plan for that as we can run -- develop and run models live on all that data, we'll be in a position to get in front of the issues and prevent them.

Yes. And in terms of the customer adoption, as I mentioned, we have 2,000 customers. So the product is still fairly new on general availability. So we're in the process of getting -- make sure customers can let us use their names. But what I can share that our 2,000 customers is widely represented in all kinds of [indiscernible] and verticals and geo locations from the largest Fortune 100 companies to the most innovative AI start-up. So there is a fairly broad non-discriminative adoption of this technology. And this is why I'm personally excited about SRE is really a very strong well-fitted use case for AI because of the result is in [indiscernible] and verifiable. So this is why we're seeing such a rapid increase in adoption.

Then finally I just mentioned that our pricing is completely transparent. You can go to our corporate website, and I believe that Bits AI SRE $500 per 20 investigation, right, Kai? So but you can check out all of that stuff by yourself whenever you want.

All right. Megan? Megan's side

It's Keith Bachman from BMO. Tim, I also wanted to direct this to you is how do you think about the boundaries within security about where you want to be in terms of portfolio expansion and talk about some areas of interest or where you don't want to be?

And the reason I bring it up is to your slide deck, you're still a relatively small part of generating ARR for Datadog. And some of your competitors have a much broader portfolio [indiscernible] a consolidation game. So in some measure of success begets success. And so I'm just wondering about how you think about portfolio expansion to try to get deeper penetration within your existing customers?

Yes, something I think about daily, hourly, minute by minute. One of the things that leads our thinking obviously is where there's existing mature spend. And we believe, as you saw in my overview of the portfolio that we are well positioned to go after areas of established budget, established spend. They also have a lot of established competitors. But then again, as you probably heard me say during my -- repeatedly in my piece, I think we do have an advantage because of the fact we have this unification in the platform, which really pays off when it comes to incident response or is being more proactive with security overall.

So right now, in the markets we're in right now, we see a lot of runway to go after that. We see the advantage that we can bring for differentiation. And we also see a strong ability to pivot off of our existing relationships to get into those security conversations. Now even within those areas, obviously, with AI and agentic, there's going to be new areas, new services that we'll be looking at because it's a new set of problems for security teams and for the enterprise as a whole. Outside of that, obviously, we'll look at where it makes sense to expand where we bring that advantage we have with our platform.

Great. Eric side?

Ittai Kidron from Oppenheimer. And my question is to you, Alexis. Your presentation was quite interesting, and thanks for making the case that frontier models. I guess, can't scale or do what you do without going outrageously cost or ineffective giving the results that deliver accuracy. I guess when I look at your business, you talked about the future massive feature set that you have, the domain expertise and your ability to take a small model, right, and make a much better use of that in your business. I guess if we try to flip it on the upside down a little bit, the question is if you take $750,000 to build a small model that delivers much better accuracy, how do you think about the barrier of entry from third parties into your business. How do you think about the risks? I mean, the opportunities are clear. The revenue is clear. What, in your view, are the risks of AI to your business?

I think the -- this is where the data advantage, I think, is clear. And it's -- I think on the -- so in the case of our [indiscernible] model, it's just -- we just have a volume of data like legitimate real data that's just not publicly available. So that's one edge. I think in the case of the sort of the training of the agents, it is the -- both the volume and the quality of evals we can build, and we have that I think differentiates us. So I think there are obviously other companies trying to build small models. They don't -- there's no -- I don't think there's a clear financial barrier of entry, but the quality of the data you get, I think that's the real moat.

The -- one of the issues, and we see is for instance, generating synthetic data in our domain, it's not terribly easy. It's not like you can basically sample what's out there, text to image and then you remix and create something plausible. It is much more if you will, the relationship between the way software is built and the observed behavior, it's much more intricate. And so you can't just go to one of the providers and say, okay, I want synthetic data. I want tons of it, so I can train my sort of small models. So that's where we retain advantage.

I think the other piece is what Olivier alluded to, which is, look, we just sit that data, we get it for free as anywhere for training purposes, right? Because it's used for by our customers in the day-to-day. And that's where someone else just isn't sitting in that flow. They have to require that flow somehow. They don't have the scale to do that. So it really -- I see it as a positive flywheel that we get data, we get more and more data. We can get the SRE agent to generate more and more evaluations, which makes, hopefully, Datadog more and more valuable. So it's really accretive in that sense.

And remember, we -- so we spent $1 billion in R&D last year, maybe between $700 million and $800 million the year before and then another $500 million the year before that. So spending all this money is why it costs us $750,000 to train the model.

Great. Megan side.

Sanjit Singh from Morgan Stanley. I wanted to get the team's view on how fast are we racing towards this vision of autonomous operations. Like what does this look like a year from now? What does this look like 3 years from now? And in terms of executing is that vision, are there other pieces of the stack that Datadog needs to own? Do you need to own the software delivery pipeline to really execute on this and that gets into a build versus buy question. So just on the -- in terms of the race to autonomous operations, what does that look like 12 months from now and over the next couple of years?

Yes. I think it's really hard to tell. If there's one thing we could tell with AI is that the rate of progress is very surprising. So you get these big jumps of capability. And then looks like things are sort of stagnating a little bit. And that when you're ready to write it off and it starts jumping again. We've had a jump recently just 2 months ago in the quality of coding agents, for example, that made a very notable difference, at least in our usage internally and what we can see from our customers.

So I think it's smart to tell whether we get there in a year or in 3 years. But what's pretty clear is that we're going to get there. Like the problems are getting solved one by one, the technical approaches, look like they're working. So we're going to get there.

In terms of the moving pieces, we had identified a few areas that -- where we thought we needed to move faster. So one of them is future flagging and experimentation. We were not all that interested in future flagging and experimentation a few years ago because we thought future flagging was a bit of a commodity itself, and we thought that experimentation was more on the surface, more something that related to AB testing, button colors and things like that, which is -- it's interesting, but that was not our core business.

I think we've changed our minds quite a bit on the topic as we understood that this would be a key part of automatically shipping and iterating on software. So that it could really make use of the productivity gains you get from the -- on the coding side from the AI agent.

Another example is data observability. We thought also this was an interesting market, but this was a little ancillary to what we're doing. Now that this becomes one of the key limiting factors or the data quality and timeliness is one of the key limiting factors for building and deploying AI models is also something that is -- that has boiled up to the top of the list for us. There's a few other areas we're thinking about, but I'm not going to tell you about it.

Can I add some more comments that -- what I've learned about autonomy for Dev, Ops and Security is not a 0 to 1 game. This is very different from putting trucks on the highway without the driver is absolutely 0 to 1. There is nothing in between. What we're seeing is the adoption of this closing this autonomous DevOps loop is the evolutions of technology and human behavior. So we start partly with the investigation piece. Initially, a lot of the customers don't trust the result of the investigation, and they verify that. But now as they use it more, they start to gain trust. Same thing was pushing a code fix. Most of the people still are very comfortable absolutely requiring people in the loop but as more confidence get built and the technology to get more mature, we would expect customers to get more comfortable.

And really, the holy grail is proactive, preemptive, predictive detection because this can truly move the needle toward autonomous operation because we detect and fix issues before they even occur. So what we're trying to do is to demonstrate tangible progress along this circle to increase customer trust, improving the coverage and rest of the technology, I don't think this autonomy is going to happen in a 0 to 1 fashion. It's going to happen in this partnership between technology evolution and our customers comfort and trust and culture evolution.

Thank you. Eric side?

Eric Heath here at KeyBanc. I wanted to come back to maybe bring your own cloud and understand that opportunity a little bit more. Can you just talk a little bit about who the addressable customer bases for this, maybe the timing of -- when do you make this product more broadly available and the go-to-market strategy around it?

Sure, I can start with that. So maybe to your question about timing, this is something we're already previewing. So we have customers actually leveraging those -- that solution bringing your own cloud, we have [indiscernible]. And the idea is to go and go to those markets where historically they potentially could not leverage Datadog. I was talking about like data residency, like some markets require the data to stay in that country. And that's like for those that would be interesting to have the technology inside their infrastructure and then be able to leverage it from there.

So this will be some of those opportunities we extend, I think would be different geos that can look at it, but also industry where you need to have like more compliance and you need to keep some of the data inside your environment. So that's fully wide from like geos to different industry and and we already have been customers, and we're building more and more like from logs to other type of cemeteries that we're actually building on the [indiscernible].

There's another type of customer we're targeting with that, which is the [indiscernible] customer. When certain customers want to make use of infrastructure they already have or they want a licensing model that is more favorable to them than the SaaS model, and that is something that we are addressing with [indiscernible] so we see some of these customers coming to us.

Everybody wants that SaaS solution and feeling for their users, right? But making it affordable when you have 30-plus petabytes a day is tough over the wire. So they're already finding you.

Megan side?

Fatima Boolani from Citi. My question is just around the Bits AI suites. I can appreciate that is the gateway drug, so to speak, to the autonomous vision. But just taking a step back and maybe asking a more pointed question. You all are very excited about the code security that you can absolutely get confused right out of the gate. But [ OPUS 4.6 ] and the [ Codex 5.3 ] iteration, I mean they are absolutely coming up with relentless capabilities around code security inherently. So I'm wondering how you create a protection barrier to the value you're providing to customers and where that competitive edge is vis-a-vis the type of code security, hygiene and rigor you're providing vis-a-vis the context from your platform versus the general purpose LLM who could maybe have a broader coverage around code security because to your observations, the coding assistance are only going parabolic.

I think it's an advantage myself, like I don't think it's us versus them. Any way like having the LLMs be really good at creative thinking and ideating around these things is fantastic. But one advantage of our code security is that we can see how that code is deployed in production. So for [indiscernible], for example, it might find that there's malicious packages or vulnerabilities and packages but you don't know if that package is actually deployed in production. So you might pull a fire alarm and have everybody waking up and then it's actually not even deployed in production at that version, right? It's not a real vulnerability.

So I think these things can work in conjunction as we say. We're using all these technologies in the appropriate ways to inform, but I think there's still something unique to bring to the table, there is my opinion.

Yes, I don't think we're going to see need of defense in depth. But clearly, we should think about and also understand how far in the left now with coding agents we can solve for a lot of the security problems. But to Michael's point, there's a lot involved, there's a lot of complexity in these production runtime environments. And that's what you're going to -- that's not going to go away. And there's always going to be this need to understand for something that has been found to degree, for example, a vulnerability, is it actually something that's not only being loaded but is it actually being executed. And that's the area that we'll continue to focus on, even with the [indiscernible] adoption of coding agent.

It's not [indiscernible]. And again, there are a few examples of ways in which this breaks down. It's also because the code at the time you wrote it because it was secure at a time, doesn't means it's still secure 2 weeks from now. So there is something that needs to be reevaluated [indiscernible] permanently. There are some things that Claude might think is secure, but you, as a company decide is not. And so you might have your own rule, you manage your own tech and everything.

So all that to say that there's going to be a lot of room for a lot of specialized tooling that is going to complement the general purpose coding agents. And -- but definitely one, that tooling might use some of the same [indiscernible] and two, the agents are here to stay and they're going to do more and more. So it's going to be the question of working with them and complementing them, not trying to replace them.

And can we produce a good product [indiscernible] told you why would you build a monitoring company for cloud software when the clouds will probably do it, right? Here we are. [indiscernible]

All right. Ryan?

Ryan MacWilliams, Wells Fargo. It might be early, but I love to hear about the differences in monitoring an AI agent workflow versus monitoring a normal SaaS application? Do AI agent workflows acquire more data intensity and more logs that are required to monitor? And maybe more observability across the wider surface area, love to hear what you're seeing so far?

There's a lot of recursion and uncertainty in one, what the agents are doing. It's changing a lot. Even internally, we're always experimenting with different [indiscernible]. So it's a very volatile area. And so it does require some specialized tooling. Also the fundamental testing dimensions of quality assurance, verification that it's good and validation that it's working appropriately and a good product, right, are a little tougher when you don't know what it is doing, and then you put it out there and see how people are engaging with it, and what it's doing. It requires a different feedback than when you can write deterministic software and test it in pretty predictable ways.

So I think it does require some new things. That's why we have playgrounds and sandboxes and experimentation and why experimentation really became important for the major research labs and foundation model providers, all them are big experiment [indiscernible] foundation users because they don't know exactly what it's going to do in production.

But it's super early. So I'd expect there will be a lot -- we'll have a lot more clarity about this space in a year, in 2 years, 3 years. It's so early that right now, the companies that are building agents are on the leading edge. And so we're all learning together.

Megan side.

This is Arti Vula from JPMorgan here for Mark Murphy. Olivier, anyone who wants to chime in. A couple of days ago, you guys talked about one of the largest AI foundational model companies adopting Datadog, consolidating open source in-house hyperscaler solutions. We spoke with another AI company that said that your platform was critical and they couldn't replicate it if they wanted to. So can you just help us understand that journey that some of these really highly innovative companies are taking where they come to the realization that they can't do it themselves. They don't want to do it themselves. Is it the breadth of capabilities, the fact that it takes more resources even with developers than they think it does? Is there like an aha moment for them?

I mean it's been the story of the company, I mean, since day 1. And these AI companies are not any different from the cloud native we're serving initially or from the larger enterprises, we started selling after that. They all have some mix of homegrown and some various tools they bought in the past. It's always -- it never works quite well enough. It's always a time sock. It always becomes a big issue at some point because keeping your systems up and right and safe and keeping shipping software is an absolutely business-critical need and you absolutely have it -- have to have it nailed down and it breaks and then it causes some questioning of what you're using. folks realize that they have other problems to solve to be competitive than to reinvent something that they can buy and they just typically buy what we do. So the question is not, like, look, if the biggest companies in the world try to do that as their sole focus, could they do it? Maybe maybe not. But the point is they're doing other things. They have to do other things, and there's no point in them building their own monitoring, their own observability and their own autonomy.

Great. All right. Eric side.

Kingsley Crane at Canaccord. So you've used Datadog to help observe and build Datadog in the past. How do you think about observing your own agents? And in solving that recursive challenge, does it help you build a better agents console and be the best product on the market to help customers observe forms of agents?

So yes, but we have to be careful because when the field is brand new, we are not every other company. So the one mistake you can make is to mistake yourself from the customer. And the way we learn is by speaking to as many customers as we can. Then we do through the product, we need to make sure it needs to make sense to us, too. It needs to be amazing for use cases. But just because it works well internally does not mean it's going to be the right product for customers.

Great. Okay. This will be our last question on Megan side.

Howard Ma with Guggenheim Securities. I wanted to ask about the perceived threat of open telemetry and other forms of open source observability tools and -- or I guess, open telemetry being more of a standardized protocol. And what is Datadog's competitive moat while embracing these open source standards? And specifically on the back end, I'm curious how defensible are having 1,000-plus integrations and the ability to correlate lots of different data sources in the way that Datadog does it, that's different from others.

And from a coverage standpoint, you had a slide that shows monitoring virtualized environments on one end. So going more on-prem in one direction and then GPU monitoring in another direction. I mean it really -- is the right way to understand it that you want to address highly customized enterprise needs out of the box, and then that is really the true moat.

The collection has never been the moat, right? We -- when we started the company with Galaxy, we thought we decided everything that's on the server side, the SaaS is going to be the smart. And then the -- what leads on the customer environment, like the agents and everything else and the collections and the integration is going to be open source. And our agent and everything that came with it is open source. It's actually very premise license is Apache. Is it still Apache...

I think so.

Yes. license. But we didn't change it at the very least. And by the way, early on, our competitors were using our agent, and they were using our integration and everything else. Today, we're very happy to see OpenTelemetry come up. This is -- we are open telemetry native. It's great. It's a great way to get more data into the system, make it work faster, reduce friction. I think it makes everybody happy. It's never been the differentiation.

When you talk about having a tight integration, the question is not, can you plug into the system and get that out. The question is, how do you -- how well do you understand that? How well can you use it? How does it come together with the rest of what you have? And whether or not you're using OpenTelemetry or some of its predecessors because they used a few different standards before that, that part is what is fairly unique to us, we're doing much better than anybody else.

And maybe just to add on the OpenTelemetry side, it's not really competition in some way because we are like a big contributors to open telemetry. If you look at Datadog, we're like the top contributors of hotel, and we have like now we fully support open telemetry, and we're like supporting like any type of data will come the same way being OpenTelemetry or own agent. So that really for us to Olivier's point, how the data is coming is what we do with the data internally, which is more important.

Great. Okay. Great. Well, this ends the first half of our session. So we are going to take a 20-minute break. That means you'll be back here at 3:30 to start the second half. Thank you.

[Break]

All right. Welcome back, everyone. Let's kick off the second half of our Investor Day. I'm pleased to welcome Sean Walters, our Chief Revenue Officer, on to the stage.

All right. Good afternoon. My name is Sean Walters, and I've been with Datadog for 7 years. I lead our global sales team. I'd like to start by describing our go-to-market motion, how we're organized and how we've expanded our capabilities over time. Our go-to-market motion has 3 main parts. The highest volume part of our go-to-market in terms of net new logos is our self-serve market. A lot of other software vendors only focus on enterprise use cases. So for them, there's only a finite number of accounts to go after. Whereas for us, even though we have well over 30,000 customers today, we believe that there are a lot more new logos to go get.

We will see self-service customers who start trials with us, visit one of our demo booths at a conference or otherwise indicate their interest in Datadog. And if they've contacted us in any of these ways, it's a fairly warm lead. So we're going to check in with them quickly with our commercial team. The commercial team is a heavy outbound motion. They work on those warm leads from self-serve customers, but they're also doing a lot of work on prospects, proactively reaching out to see if Datadog can provide value. And commercial is a velocity logo engine and often lands logos with a small amount of dollar value, but that's just fine because we are a land-and-expand model, and we have the opportunity to grow with these customers over time.

While the focus for commercial is landing new logos that are smaller, some of these companies achieve great business success and become very large customers with big cloud footprints and a lot of Datadog usage. In fact, 24% of our top 25 customers are commercial customers. 50% of our $1 million-plus customers are commercial and 72% of our $100,000-plus customers came from the commercial business. So that's really the power of the land and expand motion at work. The third leg of our go-to-market strategy is the enterprise team. Enterprise account executives will stay with the customer throughout the relationship, working with all the teams across Datadog to support and achieve success with that customer. We deal with enterprise customers with increasing sophistication, and we've built our capabilities over the years. So I'd like to dig into some of that.

First of all, there's our strategic enterprise team. This is our typical enterprise sales motion. And it's a full life cycle motion. We're doing outbound pipeline generation. There's lead gen through corporate marketing as well as account-based marketing, events, referrals and many other efforts. If we get our foot in the door, we apply a custom selling methodology that guide sellers through qualification, technical selling, trial and evaluation, champion building and all the things we need to do to build the confidence and the value that Datadog brings. Once we land that customer, the enterprise rep will continue to develop that relationship alongside other teams, including customer success, post-sales support and service.

Then there's our majors team. These are our largest existing customers. Our reps here are focused on increasing usage for our current use cases, but also getting more strategic with the customer by going wider across departments, personas and use cases and solving multiple technical and business problems with our platform. Finally, there's our key accounts team. This is a more recent bet that we've been making to enlarge new customers that we haven't yet engaged with. These customers may have a longer sales cycle, and it may be more of a top-down motion than we traditionally go after. So we're focusing these reps on a longer journey.

In the first year, these reps may have objectives like number of meetings that they schedule. But in the second year, their goals are shifted much more to revenue and closing deals. Our continued innovation is delivering capabilities that these sophisticated customers demand like enterprise class governance, access control and data security. And since they operate at very large scale with very diversified environments, our recent innovations like Flex logs, frozen logs and bring your own cloud play a very important role. This team made significant strides in 2025, including some faster-than-expected wins that are already expanding very rapidly. Our enterprise sellers go after the largest customers in the world, including companies in the Fortune 500. We are making great progress in penetrating this group, but we still have more than half the Fortune 500 to go, and we're working to engage them as they move to the cloud. And our median spend for these customers is still modest at less than $0.5 million per customer. So we think we have much more opportunity here.

Compared to commercial, there's not as many new logos to land in enterprise, but the size of each customer can be orders of magnitude larger. As we have more products to sell and as we've grown in our ability to sell our full platform, we've seen the average size of our enterprise lands increase and particularly so in this last year. And if we do a good job developing the relationship and delivering more value over time, these customers ultimately expand from their land size. Here, I'm showing the average enterprise revenue per customer against that average land size. Finally, I'd like to talk about our technical services -- our technical support and services team. As a mission-critical partner, it's up to us to provide not only a platform that solves business needs, but also to help our customers establish best practices and execute on their learning curve with Datadog.

We've developed a variety of services to support our customers in their use of us, including implementation services to get started with Datadog best practices and a customized plan; technical enablement services to offer training and knowledge building of the Datadog platform; premier support, an extra layer of support that is more individualized to the customers' needs; and technical account managers who provide guidance to enable and accelerate Datadog adoption and support customers in their journey with Datadog. Okay. So that's what our go-to-market looks like. But we've been investing in going bigger and deeper over time. So let's talk about some of that.

First of all, we've been bulking up our capabilities in channel and alliances. First, the hyperscalers. These are very important partners with us. We're seeing more and more synergies over time. We're always working to improve our relationship, our co-selling motions and our technical partnerships with them. Then there are the resellers and other partners. In areas like Latin America, Korea, Japan and many other regions, we're working with resellers and others is critical to our success in those regions. They have customer relationships, technical capabilities, service offerings that make working together a really productive thing to do. So we're seeing a lot of success with these folks. And then finally, the system integrators. We're not a service-rich product, which is good because customers don't want to spend a lot of money on ongoing professional services. So with SIs for us, it's more about aligning to strategic initiatives. We've signed some really exciting partnerships with SIs, and we're building out those programs. With hard work over the year, we've seen meaningful growth in channel alliances in Fluence business, but we have a lot more opportunity here.

Another area of growing investment is security. We started our security and the typical usual bottoms-up sales motion. This works well with our observability users who can be champions for our security products or if they're in the DevSecOps organization and part of the purchasing decision. We also started from the bottoms up because we were working on product readiness. The best salesperson for our products is the products themselves. That's why we invest so much in product. because when the product is ready and delivers value and makes the selling motion efficient and effective. And these days, the product is broadly ready across our security platform and particularly in areas like SIEM. A couple of years ago, we started adding to our security go-to-market motion, starting with a small number of sales engineers. These are our technically skilled staff who demo our products to customers. By specializing, they develop experience in showcasing our security products to security personas.

About a year ago, we began hiring folks in channel and alliances to activate and build partnerships with our security channel. We're on our way there, and we're learning about how we go to create win-win partnerships with these partners. Recently, we decided to start a pilot of security-focused sales teams. We want to build on our security successes so far. With the product ready to go and competitors in this space consolidating, we are excited about our opportunities. Here's an example of an expansion with security. This is one of the biggest cruise lines in the world. Over the years, they have grown to be a multimillion-dollar customer with us. They chose Datadog as a strategic security vendor. As they believe -- they also believe that their unified observability and security approach is essential to maintaining operational efficiency and minimizing downtime. And over the years, they adopted nearly every product in the Datadog security stack. Recently, they told us that their on-premise SIEM environment was causing problems. It was hard to manage and scale and security investigations were taking way too long.

Last summer, they chose us to replace this on-premise SIEM with our cloud SIEM to get faster detection and investigation at a lower cost. Today, about 1/4 of our business with this customer comes from security, and we have more security opportunity with them. So this is a great example of our potential with security. All right. So that's security. I also want to talk about investments we're making to expand our presence geographically. A few years ago, our sales teams were relatively concentrated in places like U.S., Ireland, Singapore and Japan. But as we've grown, it's become important for us to place sales teams in more places so we can literally meet with the customers where they are. So in places like Brazil, Mexico, India, Australia, Middle East and many other countries, we're putting people, channel and alliances partnerships, local language marketing and our investments in place so that we can capitalize on these opportunities.

I'd like to give you an example of how these investments are delivering for customers regionally. Our Latin America go-to-market teams are pursuing opportunities with grit and a team mentality. They are persistent and patient, and they are delivering. We are winning some of the largest companies in that region from banking to e-commerce to retail to telecoms. Today, our business in this region is 5% of our revenue, but they're growing far faster than our overall revenue and the pipeline of opportunities in this region is great. And finally, of course, AI. As you heard in the first half, our product teams are delivering AI innovations across our platform. And our customers are focused on their AI efforts. It literally comes up in every single meeting we have. As you heard from Yanbing, FIT AI SRE is ready and is delivering tangible value for customers now. We just had our sales kickoff, and we are going all in on AI. Every seller has received training and is ready to have that conversation.

And as we move forward in time, the preview products will go GA, and our sellers will have an even broader suite of AI capabilities to sell. So those are just a few of our investment areas. We keep experimenting and expanding our go-to-market capabilities to match the innovation of our product teams. I'm extremely proud of the sales team we've built over the years. And here's the evidence that our hard work is yielding results. This shows our bookings by year, including a stellar 2025. I've never been more excited about our opportunities and our potential to execute, and we are just getting started. Thank you for your time.

I'll hand it off to Adam Blitzer now to talk about how we deliver customer value.

All right. Thanks a lot, Sean. Good afternoon, everybody. This is the part of the presentation where we have back-to-back pocket squares. So we hope you enjoy it. My name is Adam Blitzer, and I'm the COO here at Datadog. I've been with the company for just about 5 years now. I'm involved really in all aspects of our go-to-market. I get to work with some of our largest customers every day and also get to spend time on our largest, most strategic deals.

I want to focus our time right now on why our customers choose Datadog, how we solve their problems and deliver value and why and how they continue to grow with us. So let's go ahead and jump in. This is the observability market. It is dynamic. It is large. It is fast growing. And there are many options in this market. It is a competitive market, as we talked about earlier. And vendors stake out different spots within the market in terms of their pricing. There are premium products, there are sort of middle-of-the-road products. There are commodity and siloed products, and there are even open source products. And as you know, Datadog is a premium product. So given that there's always been a rotating cast of low-cost and siloed commodity products, why do customers overwhelmingly continue to choose Datadog and continue to grow with us once they've made that decision? Why do we keep gaining market share? Well, we've always focused on delivering value to our customers as our North Star. And a key way that we do that is through our unified platform, a single pane of glass for observability and security.

Now platform has always been our DNA. You saw this slide earlier, but I just want to highlight, we started with a rock-solid foundation, a unified platform. In fact, we started work on the integrated platform before we ever launched our first product, which happened to be infrastructure monitoring. We made an early bet on DevOps, and the idea was that breaking down silos between teams would empower them to solve problems more efficiently than ever before. From there, we really let our customers guide us and really have a customer-driven road map. And we look for places where we can break down silos between teams, between data sets and continue to deliver mission-critical observability and security applications for our customers. So let's take a look at how that plays out in action. This is an example of the single pane of glass. This is one of our customers who is one of the largest technology companies in the world, and they have tens of thousands of software engineers. Now prior to Datadog, they were using a whole host of point solutions for observability. Now you can see by standardizing on Datadog, it's allowed them to see all of their telemetry data in one place. So no more swiveling between screens and applications, no more painful correlations between sets of data.

In a single year, they saved thousands of hours for their SRE teams or engineers that are responding to incidents. But what's even more interesting is they saved over 100,000 hours of time across all of their other software engineers who normally would have to have downtime during an incident. So instead of twiddling their thumbs, waiting for something to be resolved, they can focus on innovation and delivering new products. That's an incredible return on investment and really is the core value proposition of the unified platform. So while the original benefit of that platform was all about productivity and speed for our users and for our customers, one other really interesting trend emerged over the past few years. And that was that buyers really sought to consolidate on platforms. So instead of using a different tool for sort of each possible job, they saw that they could gain immense buying power by consolidating onto a best of suite onto a true platform. Here's an example of that playing out with a European home improvement retailer. They were struggling with incomplete visibility, alert fatigue, long incident resolution times and high operational costs. By standardizing on Datadog, this customer estimated that they saved over $10 million. Now some of that was indirect software costs. Some of that was in engineering time. Some of that was in customer experience. But we're seeing this trend more and more. Customers that consolidate on Datadog save in many different ways.

So the unified platform has led to productivity gains. It's led to direct cost savings. But we think this becomes even more valuable in the age of AI. Customers who want to make use of agents for observability, for security, for software engineering, find it much easier to do so when they have their telemetry data and their security data in one place. And it doesn't matter if the agents that they're using are Datadog zone agents, as you saw earlier, their own custom-built agents that they want to run on top of observability or third-party agents from other companies. Now as our customers have grown with us over time, we have found many, many ways to deliver them more value and more purchasing power. So let's take a look at a few of those. This slide really shows how our economic model works. It's very, very similar to the cloud providers, which essentially all of our customers are familiar with. We have volume-based pricing.

But as our customers grow with us, we bend the curve of cost for them. So they continue to get more and more value, but more and more leverage for that value as their volumes grow. We give them discounts for the amount of commitment that they make to us. We give them discounts for using multiple products, multiple combination of products. We give them discounts for term length. So the more a customer grows with us, the more we're going to bend that curve over time. We also work with them to optimize their usage, of course, of Datadog. We want them to use us in the best way possible. Since we're a usage-based product, right, we're only generating revenue when our customers use us, but we want that usage to be as valuable as possible. And if we find any optimizations to make with our customers, we'll work with them on that, and we find that they then invest that back into other areas of usage. But they can also use Datadog to optimize what they're spending with other software packages or with the cloud providers themselves, and we'll get into that in a bit.

And then the last piece is we're constantly delivering innovations. So maybe the way we store data, different products that we launch that have novel pricing and packaging. You heard about some of that from Iris earlier today, and we'll get into a couple of examples. But the key thing to remember is that we are along for the ride with our customers' growth. So we scale with our customers. And as long as a customer is growing and their technology footprint is growing, their observability spend is also going to grow. Now we want to, again, bend that curve for them over time so they get more and more leverage and more and more buying power, but we're along for the ride with our customers' growth. And sometimes that growth can be quite exceptional. This is an example of one of our AI native customers. They have experienced tremendous growth in a very short amount of time. And you can see that their footprint with us has gone from using quite a few products to quite a few more products, but the really rapid ascent of the ARR that we're earning from this customer. And you see this pattern play out. As customers grow, their business grows, their revenue grows, their Datadog usage is also going to grow alongside it.

Customers also scale as they adopt more products. This is an example of one of the largest online sports betting companies in the world. Their entire business is built on the cloud, and it depends on real-time low latency performance. So they find tremendous value in using our full platform. And you can see their adoption journey from product all the way to 21 products. And each time they adopt a new product from us, it isn't the same as using multiple products in isolation. It's really a force multiplier. So when they adopt a new product, it increases the value of all of the products they've already adopted prior to that. Not only are they saving money by consolidating spend with fewer vendors, but they're also unlocking additional capability and value.

Now as I mentioned earlier, we also deliver cost savings and efficiencies to our customers as we innovate. [indiscernible] mentioned this one specifically earlier today. But this is an example from one of our tech customers. They've been a long-time logs user for us, and they adopted Flex Logs, which for many of their newer logging use cases. And for Flex Logs, in particular, they found that it really sharply drove down the unit cost of logs for them, and it allowed them to scale their log usage 75-fold. So we delivered more business value, but we did it in an economically efficient and business sensible way for the customer.

We also launched new products that lead to very direct savings on both Datadog or on cloud costs or on other products that a customer may be using. So examples of these could be cloud cost management, Kubernetes auto scaling, et cetera. Now here's an example from a major software company that turned on our cloud cost management product and immediately found significant savings. So they were just really looking across 6 of their environments, of which they have many environments, immediately found $1 million in savings. And this is just an example of new products that we deliver to customers to help them with their spend in general.

Now finally, I want to come back to the topic of us investing in innovation. We spoke about that a lot in the morning session, but we think of innovation as really the secret sauce of the company, and it's even more critical to us in the era of AI. Our space is incredibly dynamic and complex, and we have seen rapid technology shifts throughout the life cycle of Datadog. So first with the era of cloud and now in the era of AI. And our ability to out-innovate our market and guide our customers through change is a key differentiator for us. We take 30% of our revenues and invest it back into R&D. So that was more than $1 billion last year. We've always invested more on a percentage basis than anyone in our market. But not only that, you can see that this high percentage of investment, coupled with our rapid revenue growth means that, that compounds over time, and it leads to this very significant advantage in R&D spend. We're now at about 3x the R&D spend of our next closest peer. And we think that really makes us future-proof and that's what customers want to bet on in this fast-changing era of AI.

You can see that our platform approach, coupled with a relentless focus on R&D investment has allowed us to deliver new products at a very fast pace. So if you look at this chart, it's interesting because our pace of innovation hasn't slowed down as we've grown. It's actually sped up. So how is that possible, right? Well, it comes back to everything being built on one platform. I have sold a lot of software in my day, and I've shown a lot of slides that said platform with arrows back and forth. But in Datadog's case, this is truly one platform that we're building on top of. And so it means every time we launch a new product, we're not starting from scratch, right? That new product is built on the foundation of all of the products that has come before it -- that have come before it. And it, in turn, adds back into the platform. So it creates this amazing virtuous cycle and delivers constant value to our customers.

I want to talk a little bit about the cohort of AI customers that has really standardized on Datadog. So you can see 14 of the top 20 AI native companies are using Datadog. We have many of them that are spending more than $1 million a year with us, and the cohort itself is quite large with more than 650 of them using Datadog. And we've always been trusted by the market at large, but specifically by the most tech-forward companies, right? That's where we've really made our bones. As you know, cloud-native companies have overwhelmingly standardized on Datadog over the past decade. They pushed us from a technology perspective. They were using the newest tech. They had the newest types of architecture, and that all benefited our customer base at large. But what we're seeing now with the AI cohort is it's exactly the same thing, right? They trust us to meet them where they are and innovate rapidly as their space continues to evolve. And this innovation, again, will ultimately benefit all of our customers as they continue to meaningfully adopt AI themselves. All right. So that wraps it up for me.

But with that, I'd love to turn things over to our CFO, David Obstler.

Thank you, Adam. For those of you who don't know me, I'm David Obstler, and I've had the great pleasure of being the Datadog CFO for the last 7 years. I'm looking out into this group, and I'm very gratified. I see so many familiar faces in the investment and analyst community. And I want to thank you for your support of Datadog since our IPO and your continued support. There's one other group I want to thank. You've seen Yuka on stage. Yuka is the tip of the spear on our Crack IR design and production group, and they work very hard, and I want to thank them without them, would not have had this day come off. Thank you, team. Thanks a lot. Thank, we only do this every 2 years. There's a lot of work.

So let me start now. What you've heard today has been Datadog and its platform strategy in solving our clients' increasingly complex problems. And I hope you've come to understand that, that is only being enhanced by the advent of AI. You also heard from Sean and from Adam that we are broadening our go-to-market, making some investments there. all to create a broader and more uniform platform that solves our clients' problems and provides more value. And to use the word that we started on, all of this to try to increase the autonomy in the analysis detection and remediation of our clients' problems. And so sort of that's what you've heard. But I want to dive in a little deeper into the DNA of Datadog and what makes us grow, what are our opportunities that we see in front of us and how do we turn that into a profitable vital organization.

So let me hit that. How do we grow? One of the most important slides that we've shown you from time to time is this slide. This shows our land-and-expand model at the center of Datadog. And as you can see, it goes back 10-plus years. And what it shows is not only do we continue to land new customers, but we grow with them over a long period of time. The first cohort here says 2015 and prior, and you see the growth of our relationship with those customers over time. And it's that which creates the engine that makes Datadog. Now within that, it is a combination of our business that we have from the previous year, plus the new clients we land, and I'll discuss that, and the growth of our existing customers, which makes up the majority of our ARR growth in any one annual period. Diving into that growth, about -- and we've discussed this before, about 1/4 of that ARR growth each year, plus or minus, it varies, comes from adding new customers, which is the engine for the future.

And then the remaining approximately 3/4 comes from, one, our clients adopting more of the products that they've used in Datadog, the growth of the client's business and they're using more of the Datadog products and cross-sell, which is dependent upon the platform investment that you've heard in the previous presentations. And what that has produced is a rapid expansion of the customers we get, the land, 18% growth of customers over the years and the average revenue per customer growing 17%. And together, that produces a compound annual growth rate over this time period of 42%. And it's that land-and-expand model and investment in the platform that creates the sustainability of that that's been driving Datadog.

Now what are the types of growth opportunities that make this flywheel continue to churn. So I want to go over some of those. You've seen in Ali's presentation at the very base, there's a very, very long secular tailwind from digital migration and digital transformation and cloud migration. And this is a slide that we've shown you, which is upward sloping. It's the investment in cloud spend. And that is -- and we've talked about this many times in our investment meeting. That's the bedrock in that there is a very long-term trend, and it's got a lot of legs to it. Then on top of that, we believe that there'll be an accelerant from the adoption, and we spent a lot of time in today's presentation of AI, both Datadog for AI and AI for Datadog, which is driving up the speed of the creation of software and the cloud transformation. And we're seeing this in a number of ways. You've seen this slide. This is a slide about the spending on AI, which everybody reads about all the time in the press. And how is that working for us? Well, we are having signs that, that's translating into greater and broader platform adoption. In this case, this is our observability of LLMs, which through our LM observability product is beginning to take off. We have many customers using it. And just in the last year, that has increased 10x. That's the span sent to us to our LM observability.

We also are dramatically increasing our integrations and another piece of evidence that's starting to spread out throughout the customer base is the number of customers that are using those integrations. And we just talked about this in our public release. 5,500, and this has been upward sloping of our customers are using integrations. And guess who those are. Those are some of our larger and more sophisticated customers because that accounts for about 80%, just under 80% of our ARR. So our larger and more trenched, more progressive customers are using our AI integrations. In addition, we've been able to service many more clients. This is a statistic that we've given out and we showed earlier that we're supporting our customers, which are AI native and becoming the platform of choice for them. And that number has grown substantially and at the end of last year was about 11% of revenues. So a lot of trends in cloud migration enhanced by AI.

Now when you get down to more specifically, how is Datadog growing against that very positive secular backdrop, I want to speak about our growing and retaining customers. Even though we are a leader, we have a lot of white space to go. What we've done here is curate down from the millions of customers that the cloud providers have. We then have taken other buying signals like how much they're spending, how many hands do they have on keyboards, what are their projects to curate down to just under 500,000 global customers that we believe are target customers for Datadog. And of course, we've been growing our customer base quite rapidly over 32,000, but that's a penetration in logos of only 7%. There's a long way for Datadog to go. Now what we've been doing is accumulating those customers and growing with those customers. These are metrics that we've been giving out since we've become public. And this looks at our total customers, then our customers that are over 100,000, and you see we have over 4,000. Once a year, we give out our $1 million customers, which this year grew to over 600 because of the growth you've heard of from our other customers.

But here's a new one for you. This is brand new, hot off the press, debuting our customers that spend over $10 million with us. And that here is the time series you see. It's been growing quite rapidly, over 60% growth, and that's now 34%. So this illustrates how we're landing and you've heard from a lot of the other speakers about how we're expanding our value with our customers. Now who are these customers? Now first of all, they are diversified -- in a geographical sense. Yes, we started in North America. But you've heard about from Sean, Adam and others how we've been making major investments around the world and enhancing our investments with complementary types of relationships and channel providers, et cetera. So even though North America is our largest portion, our international business is quite -- is growing quite rapidly. But I find the other slide here maybe even more interesting because when you think about observability of in cloud, we started out and you might think about software companies, technology companies, cloud natives. But we have a very diverse customer base here with major segments in those, but also in media and entertainment, financial service, travel, consumer, et cetera. And what -- this is indicative of what is happening in the digital economy. All companies are digital and becoming digital and cloud native. And we, as you've heard from a number of the other speakers, address that very, very big and broad and diversified market.

Okay. Now within that, what's happening? We have very good customer references and proof points in a wide variety of industries. And some of those are what you might expect in the technology, the Internet, the software, but we also have 8 of the top industrial manufacturing 8 of the top 10 logistics companies, et cetera, 10 of the top entertainment companies, further proof points that our end market is very broad. And all of this together is coming together in value to maintain a very high gross retention percentage. It's 97% plus in the company. And not only that, it's 98% plus in enterprise, but in SMB and mid-market, it's 96% plus. And what does that mean? That's indicative of the high value that our customers place in Datadog and the stickiness of the product. Once you have your Datadog, it's tough to get rid of your Datadog and you don't want to get rid of your Datadog. So this has been fundamental to the economic model of Datadog and the value we bring to.

Okay. Now moving on to some other things that are growth drivers is our expanding products and use cases, and you've heard a lot about that. This is at the core of what makes Datadog. And we were just looking -- when we first went public, we had this metric of 2-plus products, and that has settled in the 80s. And as the years have progressed, we've added more and more because of the adoption, more and more products here. And now you see we're out to 10-plus products because of this flywheel and adoption. We have some numbers, half of our customers have 4-plus products, and we have just 9% of 10-plus. And I would expect, as we continue to penetrate and consolidate, you'll see these continue to go up.

And we have created a very, very strong business. We just announced this on our last earnings call of what the penetration of the 3 pillars or I think Yanbing referred to them as the 4 pillars because when you have digital experience get as large as it is, we are also calling that a fourth pillar within the application monitoring. And as you know, we've crossed $1.5 billion to $1.6 billion in infrastructure and $1 billion in log and APM. Now while that's really good, that shows a lot of platform momentum. I want to show you, just like I showed you in the penetration of the logos, how much opportunity there is for us in our core business. Only 53% of our -- sorry, only about half of our customers use all 3 pillars. So we still have a penetration that's roughly half of customers who don't use the 3 pillars.

And what we found is that when a customer standardizes on Datadog and uses all 3 pillars, they spend a lot more with Datadog. In fact, they spend 15x more. So half of our customers are not using the 3 pillars. And what does that produce? You see over time, we have a broadening group of customers who buy more products. And the ARR, of course, is very consistent with how many products they use. And so as you go out, this has been the consolidation and the growth opportunity that we've been realizing over time with so much more to go. Another benefit from this is as customers standardize on Datadog and use more of our products, they churn less. So this is a very important driver of growth in the future.

Now outside of observability, which I've been talking about, we are also addressing new markets and expanding beyond observability, which you've heard about a lot today. Within observability itself, it's a very large market, a $30 billion-plus market. And we are the leader. We have the highest market share. And we've been growing our market share over time. But at the same time, as we've been addressing more complex client problems, what we've been doing is we've been increasing our TAM. And this slide illustrates what we've been doing over time in going from the blue of observability to add security, software delivery, service management, product analytics to get -- to expand our market to well over $100 billion. And these are all markets where we have products, we've made investments, and we're seeing traction.

Now one of these, just to show the slide that we showed already is the security opportunity. It's one of those expansion markets. And you can see here that we've grown quite rapidly. We have quite a number of customers using it, and we announced 1 quarter ago that we've passed the $100 million mark. But there are a number of different vectors for growth there. So that's sort of going over from a little more of a quantitative lens, what are our growth opportunities and why we expect them to continue to be going. But of course, it's about getting the top line growing, adding value and also creating a strong business model. And so I'm going to spend the next bit of time on how we turn that revenues into profits and how we've been doing that over the years.

The first place is gross margins. This plots out our gross margins over the last 5 years, 5, 6 years by quarter. And we've given guidance that we're planning our gross margins plus or minus 80%, leaving the flexibility to invest in additional platforms, data centers and products. But we've been really good about working on our platform to provide extensibility, cost initiatives. We optimize our own platform very well. And you can see we've been successful over the long period of time, plus or minus of having this 80% gross margin. You've heard many times because at the very center of Datadog, that we have been investing for a long time and are the leader in R&D investment. I won't repeat what you see here, but it's quite extensive. And that means we have been able to invest enough in R&D to maintain R&D as a percentage of revenues, and this has been our target around 30% and that has some fluctuation, and there may be some things about the use of AI that might be levers here, but we've been really good at investing methodically in R&D.

At the same time, one of the reasons we've been able to manage the company economically and still invest substantially in the platform is because we are very efficient in go-to-market because of the frictionless adoption we talked about. And even though we've been investing at a very high rate and expanding our go-to-market, as Sean and Adam talked about, we still are much more efficient in how we do it than our competitors. That has allowed us to maintain a high growth of sales and marketing, yet keep that sales and marketing as a percentage of revenues in the roughly the mid-20s range.

On top of that, we've been really efficient already in our own optimization and automation in G&A. And we've been able to run our G&A while scaling the company. I see a number of my G&A friends sitting here. Thank you for all the hard work you do there at around 5%. And the combination of all of that has resulted in us having an operating margin where it does have some fluctuation because as we've talked to all of you about, we set an investment plan. We try to set an investment plan based on the opportunities. We have a consumption model, and that changes faster than we can hire people when that changes. And therefore, there are some fluctuations, but essentially, we are trying to invest behind the opportunity while being -- having good financial management, which has resulted in us having increased scalability and margins that have been in the last couple of years in sort of the low to mid-20s.

Now our financial performance here is summarized on this slide. I've talked about revenues. But while we've been investing substantially in the opportunity, we've been able to grow our operating profit at a higher rate and be good cash flow margin managers. Our cash flow margin, as you see here, last year was 27%. Our operating margin -- by the way, this is all non-GAAP. I wouldn't want you could to say anything. So it's all non-GAAP, and it was 22%. So we've been able to be good cash flows. We're not very capital intensive. That allows us to be very efficient in the conversion of profits to cash flow.

Okay. Now our financial goals. We've shown this slide before. This is the same slide as 2 years ago, updated for the years. And I think what that shows is the consistency of the management of Datadog despite the fact we've been making substantial R&D and sales and marketing investments and scaling the company. And so here is a time series you can all take away. And there is a reaffirmation of our long-term target of an operating margin at 25% plus. And that's what we've said last time as well. We think we've been able to prove, if you look at this time series that we've been able to invest comprehensively, yet balance that in being a strong profit deliverer as evidenced by this time series.

Just sorry, just a few other things to say. In capital allocation, we get asked this a good deal. We are strongly cash flow generative. What are we doing with all this cash? Well, one, we want to manage the company in a prioritized efficient way to grow over the long term our free cash flow, and that starts with our revenues. If you compound the revenues and you do it in a prioritized way, you will compound the free cash flow. We want to make sure we have the flexibility on our balance sheet to invest, and that includes within our own company as well as potentially in the M&A market. And at the same time, we've shown and we'll continue to maintain a thoughtful and disciplined acquisition strategy. So far, that's been focused, no surprise on products and adding technical capabilities to the company. And we've been very effective.

Some of the products that you've heard about, you heard most recently about Eppo and you heard about Metapplane, these are product areas we're working on but enhanced through strategic acquisitions. And finally, our target in net share dilution, which is 2.5% to 3%. Again, we are trying to balance stewardship with being able to attract and retain the intellectual capital to continue to grow the company. And with that, I think I want to thank all of you for coming. Hopefully, you learned a lot. And we're going to repeat the Q&A session by having Sean and Adam come back up with Yuka and Ali, and we'll open it back up like last time to Q&A. Please direct your questions to Megan and Eric, who are in the aisles. Thank you very much, everybody. Great seeing you.

All right. Thank you, David. So we'll start another half hour of Q&A. Same rules apply. We will start on Eric's side. Please go ahead.

All right. Great. Thanks for a great presentation. It's Gregg Moskowitz from Mizuho. A question for Sean or Adam. So 2025 saw a big increase in Datadog account execs covering major accounts and key accounts. And you mentioned that in year 2, their objective shift much more towards revenue. Well, we are now entering year 2 for many of these folks. So how are you thinking about the likelihood of them driving much bigger land and expand with very large organizations?

Yes. I think with the advent of key accounts, the hypothesis a couple of years ago is really putting a lot of focus in those must-win customers and new logos for us was the way to do it. And so scarcity drives opportunity within those accounts. We've seen -- the first year was a lot of the groundwork, getting meetings, getting contracts in place. And what we saw last year was kind of that all coming to life. And so we saw some really incredible lands and customers that we had been chasing for a long time. And we're seeing many of them as they get on to the platform, grow from a use case and a product perspective, and they're starting to take off. So we're going to continue with that in 2026, and we expect -- we see a very strong opportunity there.

And we're talking about it today, so we are optimistic about it.

Okay. Great. Megan side, the right side.

Andrew Sherman with TD Cowen. Sean, on the 53% of customers using all 3 pillars, that's gone up a bit versus 2 years ago, but obviously still a long way to go there. What is usually the product missing there? I think it's probably logs. It's a highly competitive area. I know your business is now $1 billion plus, which is great. But what are some reasons why customers would buy that or not? And over the next few years, where do you see that number going?

Yes. I mean I think every customer engagement, every opportunity that we enter is different. And what's important for us and our sales team is to listen to what the customer needs and kind of the pains that they're exhibiting at that moment. So it's not -- we're not going to try to force 3 pillars on everybody. Everybody who comes into Datadog as a salesperson gets trained, like 3 pillars at the core of what we're focusing on to get people trained up on. We get them focused, but we get them to go in there and ask good questions to understand the challenges that our customers are having and then get started with maybe the 3 pillars, maybe some other products. But staying engaged with those customers, typically, we'll see the growth into the 3 pillars, and we're just going to -- we're going to keep doing that, whether it's logs or APM or DEM. The more the customers spend time in the Datadog platform, the more value they see from the solutions and the easier it is for them to add on more of our products.

Andrew, I wouldn't think of it as one particular pillar that's missing, right? Like there's -- every combination is represented there in some scale and has a lot of opportunity for us to work on. All right. Eric side?

Gabriela Borges, Goldman Sachs. I think this one is for Sean and for David. Really appreciated all of the customer examples you shared us with the ARR over time and the products on the bottom. What's curious about the charts is that they all go up into the right, but the pattern within them tends to be a little bit lumpy and you have periods of time where ARR may go down before it goes up. So my question for you is, what can you do to get ahead of some of those conversations to maybe initiate cross-sell? And what is the underlying driver of those temporary dips in ARR?

We lived through some pretty volatile times in the last 5 years. We lived through a period of very rapid growth. And then after COVID, as we talked about an optimization trend, then a stabilization and then a reacceleration. So I think that essentially, we have learned a lot. And what we are doing is we broadened the platform. We've gotten stickier. We essentially are working with our clients through some of the things that Adam and Sean can talk about with our account managers, et cetera, to get clients to use the platform in an optimal way. and to try to sell through the platform value, which I think we've gotten a lot better. So some of that volatility has to do with the end market and some of it has to do with our growth in helping become partners with clients over a longer period.

Yes. I would also say for some customers, it's very intentional account management work with them. So the trend with the customer may be very up and to the right over many years. But in a given year, the customer may have sort of exceeded the usage that they had originally planned for. And so we work with them to say, hey, as I mentioned in sort of the value presentation, we'll give them better terms or more discounting by them sort of committing to more over a longer period of time. So they may be sort of past what they expected. We say, "Hey, let's look at how you're using Datadog today." Let's do a longer-term commitment with you, and it might drop you temporarily from where you were. And then over the long haul, you'll step back up to a higher place than where you started.

And remember, it's -- we are a usage-based model, right? So if you are looking at a seat-based company, you'd have a perfectly smooth line into Infinity. In our case, we -- customers use more or less of our products. Usually, they use more, as you can see, over time. And we didn't actually cherry people that much, like every single account looks the same way for all of our customers. They grow with a little bit of choppiness. And we do see optimization on a regular basis. Sometimes we actually tell customers to optimize, hey, it doesn't look healthy. If you do something about in addition, we can help. Sometimes they're about to renew and they want to optimize before they recommit, so they have a better idea of what it is they need. So we see some of these contractions.

And then they start growing again because we deliver more value, they grow on their end, they buy more products, et cetera, et cetera. So that's the motion we're going through. That also explains some of the conservatism we put in guidance because we're a usage-based model. So we're very confident about where we'll be in the midterm. In the short term, we do not know exactly how the usage is going to trend in 1 month from now.

The final part is our customers have different times when they're busy, right? Some of them are e-commerce companies and holiday is a big season for them, and then it should ramp down, right, in usage. Some of them are big media companies, they have specific events, right? And so that's the benefit of the usage-based model for our customers as well. And the reason why you don't perceive it necessarily in our numbers, right, all of that volatility is because with a diverse base of 32,700 customers, right, they're all experiencing their own volatility, but they're all experiencing it and with that broad set of customers, that broad diversity of industries represented, right, it doesn't tend to show up in the quarterly aggregate numbers.

One of the things that cohort chart, why it's so important is when you look at that and pair that with gross retention numbers that are so high, the clients stay with us. And then when you look at the length of those cohorts, they might move. They may not be a straight line up, but over time, they compound up, which is a very powerful driver of our business model.

Great. Thank you. Megan?

Yes. This is Peter Weed from Bernstein here. This is maybe a little bit of a question for Olivier and a little bit of a question for Sean. I think you've been telling a really powerful story, not just this year, but over the years about the coverage that you're getting across all of the different personas, both in the kind of operations function and in the product function. But maybe there's a different way of looking at this, which is when you kind of step back and think about like how you're serving the leadership of those organizations, the Vice President of Operations, the Vice President of Product and how that influences both product and commercial conversations that you're having, what's unique about Datadog's positioning where you could step away from being kind of trapped at something that's kind of individual persona and really kind of helping be that line of business application that helps these senior leaders succeed kind of almost regardless of the kind of individual tooling that roles on their teams might be using?

Yes. I mean as a sales motion, we're trying to go -- we have a very broad landing space, having a very large product set. So we're definitely selling across lines of business, across functions, across personas all the time. And not every one of them is right at the right time. So as a salesperson, it's a great thing to have because we can solve problems across. And then as the best seller internally for us is we get one person to be a champion at an organization and the product team, and they do a lot of selling for us. And that's usually what you see like that's why the expand happens so rapidly because you just see success in -- maybe we found the right person at the right time and the success of that person kind of bleeds into the other functions and helps us grow broader and having a broad solution set helps it happen pretty quickly as well.

And one thing I've seen recently from talking to some of our largest customers is, of course, Datadog started with SRE teams and they think about SLOs and service level objectives and all the metrics on how their systems are running. And really at the executive level at these companies is they've turned that around into like business SLOs, right? Like how is the business running? And so I use the example from a financial services company that's processing an incredible amount of payments at any given time. The business doesn't care that there's some latency in some system somewhere or there's some problem introduced. They care about what is the throughput of payments on the platform. And so they're using Datadog, which was really built for Dev and ops teams, but they're using Datadog to run the business in real time. And that's happening more and more with our largest customers.

Yes. We're leaning more and more into that into the user analytics, business analytics and making sure that the -- we are used to -- I mean, look, the company is in digital company is the applications running the business. So if you instrument the application, you instrument the business. And that's something we saw happen organically originally with our cloud native customers where the CEOs at Datadog on their desk because that tell them exactly in real time what their business was doing. Now we are building products for the rest of the market that is not necessarily cloud native to go through the same thing.

Another way in which we make leadership successful is that we help actually implement transition, change, migrations, like adopting the cloud, adopting all those things that are actually really hard from a process and a people perspective. They are more used to buying software that doesn't get deployed or people don't use, and we never have that problem. And that's one way in which we shine. We help people shine like say, hey, we did this. We adopted this tool last year. We actually mentioned one of those customers in our latest earnings call, one financial institution in Latin America that started adopting us last year. And they had a pretty large deployment initially, but we are still a small fraction of the organization.

And then what they did is they ran surveys like from their teams, like what do you use? What do you want to use? What is making you more productive? And they got like overwhelmingly positive responses from Datadog, met the executives who made the choices look pretty good. They made the right choices. They got their product use. They had a business impact. They had an impact in transforming the organization. And as a result, we could scale quite a bit with them. And that's a motion that we're repeating in many different places.

Great. Okay. On the left side with Eric.

Koji Ikeda from Bank of America. Maybe to continue the conversation and the question from the previous one. When I looked at the slide on the median spend of the F500 customers being only $450,000, I was like, oh, there must be a lot of opportunity there. And so digging specifically on that market, what are you guys doing to target those customers to expand that medium spend much higher than the 4500? And maybe digging a little bit deeper on the TCV bookings, $4.5 billion plus, it looks like in 2025. Maybe bifurcate the growth there, the TCV bookings coming from the biggest customers like the F500 and more of the mid-market size that are spending a lot with you guys.

Yes. I mean I'd say in the -- so the Fortune 500 customers, obviously, they get a lot of attention, whether it's an existing customer in our major accounts group, maybe they're an existing customer in the traditional strategic enterprise or key accounts. Maybe they're like not a customer or -- and we want to focus on them and make them a customer. They're very complex organizations and sales cycles are very long. So it's, again, working with the customer to understand and many times, they're in competitive contracts that are 3, 5 -- they can be very long contracts that we're working on. So we spend lots of time working with those customers in key accounts or other getting net new logos and landing where we can land and then working on that. But we're seeing more and more over the last couple of years, tool consolidation. Once we're ingrained within those organizations, we expect to see those will continue to grow with us as well. So there's lots of focus on that.

But there's a lot of opportunity to be out there. And in a way, you can say that for a few years, we were a bit victims of our own success in that the product grows really well once it's deployed with customers. And as a result, it was, I would say, a little bit maybe too easy. I don't want to say easy, nothing. A bit too easy to grow an existing customer from, say, $10 million to $15 million in revenue by adding more products, driving more transformation, et cetera, et cetera. But it's a lot easier to do that than to get 10 customers from 0 to 0.5 million. And by getting 10 customers from 0 to 0.5 million, you create a lot more opportunity for the future. And so we've made a number of changes internally so that our organization got a lot better at pursuing these opportunities and not just the so-called easy growth with some of the larger customers.

Great. On the right, Megan side.

Howard Ma with Guggenheim Securities. I believe this one is mostly for Sean, maybe a little for Adam and part in the multiparter, I'll try to keep it concise. It's on geo expansion. So as you expand into more geographies, I guess, number one, are there notable differences in certain geographies because you're making a pretty big expansion. India, for instance, we're hearing more about the offshoring more to India. I feel like there's less so Eastern Europe, more India. A lot of developers like do you have an opportunity to shift left more there, industry regulations, archiving, storing log data, for instance, like is that something to look out for?

And then I guess last part is FTEs. Is there an opportunity to deploy for deploy engineers to, for instance, help organizations understand how to use B AI that they didn't have before and could -- sorry, really a multiparter. Last part is new logo contribution could that actually, it's really hard for a company of your scale to increase the relative mix of new logo contribution. So how big can the geo expansion play a part into that?

All right. So you're like -- this is a 2-part question. It's actually more of a comment or an opinion. I'll speak a little bit to markets being different from one another. The good news is that there's opportunity everywhere. It's just the observability market is large and growing incredibly quickly. One sort of proxy, it's not an exact proxy, but one way to think about opportunity for Datadog is where is cloud adoption taking off or where has it taken off? How mature is it? And largely where clouds have been successful, we have followed that.

Sometimes we help people transition to the cloud, but also we're a very good choice for people using modern architectures and people who have standardized on clouds. There are markets in particular that are also interesting because in some cases, they've sort of skipped the generation of computing, right? So there's a lot less legacy and they're sort of -- either went all in on mobile, went all in on cloud, and those lead to interesting opportunities for us.

To your point on regulatory concerns, that varies wildly by market as well. And so you've seen us make investments in data centers, partnering with the cloud providers. And a lot of that is about giving choice to our customers who might be -- who might have regulatory requirements from a geographic standpoint. They might be in regulated industries, and we want to give them that choice. And then we're also doing that from a product angle. So you heard this morning about BYOC or bring your own cloud. That gives us, again, more deployment options for customers that might be facing regulatory hurdles in different ways. So that was, I think, 2 parts of your 5-parter.

You can talk about the FDE. So forward deployed engineers, we start -- we now have 4 deployed engineers on staff, and they are useful in a number of situations. And I should say it's a term that's a little bit elastic like it's been used in many different ways in the industry. But for us, they're useful in a few different situations. One is customers that need to adopt AI and need to transform and we need to understand how it's going to apply to them. The second one is the new type of AI live type of customers that have large needs that are in an emerging area where we probably are building products that don't exist yet, and we do that by helping those customers in real life.

So I remembered one more part. So like on the India piece specifically, you mentioned offshoring. We see a lot of different opportunities in that market. But one of the interesting opportunities is a little bit less kind of offshoring of non-Indian companies, but to some degree, like massive cloud-first, cloud-native tech-forward companies being created in India every day. And the interesting thing about a tech company in India, especially a B2C company is if you get any traction, you have instant scale, right? So if you think about food delivery or streaming, anything that we interact with as consumers, when you apply it to that population, you just have massive scale. And so the need for something like Datadog becomes pretty pronounced. So it's an exciting market in the vector that you mentioned, but also in the vector of just interesting companies being created every day at massive scale.

One difference that I think has been pretty important in development is the importance of channels in some of these international markets. So that is true for Brazil, Korea, Japan in some ways. There are a number of markets where it wasn't enough to have a direct sales team. We had to also develop the channel relationships in order to have that work. And so that's been very important in a number of the international markets.

Okay. Great. All right. I think we're going to Eric's side on the left.

Alex Zukin with Wolfe Research. I wanted to ask a question about the AI native, specifically the adoption cadence, both from the types of products that they start with, the size that they're starting at and maybe the pace of expansion within that cohort specifically and maybe what you're seeing develop that's either the same or different and kind of how we should think about that going forward?

It's very similar, like the types of products adopted are very similar to the other companies. It starts with 3 pillars and then you get more into user experience, sometimes you get more into developer experience. Like there's a mix basically of everything that is being used in these companies. The growth can be a lot faster than other companies just because their consumption of infrastructure is pretty massive. But again, that also depends on the customers. Some customers are growing more slowly. Some customers have 2 distinct sides that they have one side where they do research and they train models, another one where they run applications and they use us on one side, but not the other. They have some homegrown thing on the other maybe. But we've seen both. We've seen some of these labs approach us for the live side and some other labs now approach us for the training side. So we've seen a bit of everything there. So...

Yes, I think they -- we created this distinction, and we own it, but they are essentially cloud natives. Fast-growing cloud natives, meaning they don't have legacy infrastructure on-premise. Therefore, they are using -- it's very important that they use a modern observability platform like Datadog. There's great product fit. They are using it for production environments, principally, so the pillars plus. And then Ali made the point that they're fast-growing cloud natives because of their demand environment. And so I think the major difference has been maybe that some of them are growing very fast, but they're operating otherwise like cloud natives. Ali, anything else to you?

Yes. We only caveat that they're not always -- in some cases, they're not always very cloudy. Like maybe they're going to consume infrastructure from like a single large data center or a couple of single data center that are nominally provided by cloud providers that are really single tenant, very specialized infrastructure for that we cater to that very well as well.

Great. Right side of Megan.

Fatima Boolani from Citi. I wanted to direct this to Sean and Adam. I was hoping you could opine on the real-world implications of taking Oli's and the team's vision of an autonomous self-healing environment to commercializing that with customers. So I'd love to hear your perspectives on how customers are reacting to that. And specifically if that would involve over the course of the next couple of years, a fundamental rethinking around how you are pricing the platform?

And then as a related question for David, the question -- or excuse me, the slide with the customer cohorts and the growth in those cohorts. I'm curious if you can give us a little bit more detail on if the incremental profitability or profitability profile or the unit economics of a $10 million spender is materially different from a $100,000 spending customer.

I'll start with easier. So yes, we give volume discounts. But given our broad set of customers, fundamentally, and one of the reasons we've been able to maintain our margins the same at the gross is that we have a whole net set of customers coming in that are not using $10 million. So our weighted average has stayed roughly the same. So when you look at those cohorts and you basically make them weighted average for their ARR, they've roughly stayed the same despite the fact we're giving volume discounts to the largest customers.

I noticed before you started answering that, you said , do you want to start with the easiest question? It's not a competition, David.

Yes. I mean I would just say to the vision around autonomous sort of self-healing systems, when I speak to our most sophisticated customers and they draw up where they want to be a few years from now, that's what they draw up. So they draw up that exact vision. And I think it's a difficult place to get to, right? We're hard at work on it, but I think it just makes so much sense for the future of observability.

And do they want you to tell them how they should pay for these outcomes? We're absolutely moving to an outcomes-based sort of pricing modality for almost all of software. I mean, if we're not going to get there in a year or 2 years, maybe it's faster than that. But yes, they kind of want you to tell them how it should be priced or what sort of, I guess, negotiating leverage do you have in those conversations and scenarios?

I'd say right now, we're probably too far away to be talking about pricing models for a future state with the customer. But again, I think more of the customers are thinking about how could I achieve this with Datadog in the future. And again, we show our product road map around it. But I wouldn't say we're at the point where customers are thinking, hey, 2 years from now, how am I going to pay for my observability and...

We're not in the same situation than most other software companies that we don't charge per seat. We charge per usage. And our usage is typically related to some other fundamental usage our customers have, such as their usage of infrastructure or network or storage or something else. So it's -- I would say that the question of how the pricing model can work, I think, is a lot easier to solve for us. It doesn't mean we have a pricing packaging in mind just yet. I think it's -- we still have to see exactly what the shape of the product is and what the market will bear. But it's not a big shift or a big turn for us to support any of that.

I will say the vision of the autonomy is something that resonates with customers, like they do want to get rid of these pains. And there's a big difference from when we stood here 2 years ago. Like 2 years ago, this was pie in the sky. Today, with the recent advances of AI, the coding agents, et cetera, et cetera, it's a pie on the very, very high shelf. -- but we can see it. And our customers also expect to get it at some point. I think they can see it, too. And so we -- that's why we're pretty hard at work.

Yes. And everybody has that vision, but I think we still have a lot of problems to solve. Even customers have a lot of organizational problems to solve before they can even get there. So...

Okay. Great. Left side there.

Yun Kim, Loop Capital. If you can just talk about, Sean, maybe talk about the partnership opportunity with cloud service providers, especially around the fact that most of the AI workloads where there's a lot of growth there, obviously. And most of them -- I mean, all of them are really running on CSPs. And last time I checked, there's a lot of CapEx spending to support that in the future years. So obviously, there's a really huge growth opportunity to target these AI workloads. Is there like a joint partnership opportunity with CSPs that you're working on, for instance, targeting the deployments and the workloads specifically, whether the customers independently? And how much of your Datadog for AI is available on their App Store and marketplace?

I mean hyperscalers and the CSPs are the relationships that we've had the longest in our channel and alliances. We're a cloud-native company, and we started very early on working like even in the early stages of our channel alliances or that was the place that we spent a lot of time. We still spend a lot of time, and I think we're getting better and better at our co-sell motions, our technical collaborations and the things that we're doing with them. So I'd say, in general, our sales teams are always in the field thinking about how do we partner with our hyperscaler partners and whether it's just as simple as sharing notes on the accounts to actually planning and strategizing around accounts and how we win them together.

And to your question on products, they're all available on the marketplaces of the large cloud providers. And AWS, for example, at their most recent re:Invent conference, they announced their top partners in terms of sales through their marketplace, and we were one of them.

This will be the last question on the right with Megan.

Arti from JPMorgan here for Mark Murphy. I appreciate you give me the last question and great presentation. Said it only happens every couple of years. Any way to quantify or conceptualize the sheer volume of code that's being produced today, [indiscernible] cloud code, OpenAI, CdX.s it subtle or overwhelming amount of code being created? And is it moving into production and driving activity for you guys?

Well, we see the increase of code. We see the increase of the -- like all the signals we get in the reference series that are linked to us, also what we can see in the open source point to a lot more code being generated. So yes, it's there. If it actually ends up in the repository, it's going to end up being shipped. And so that we also see that coming. I will say most companies are still early in the transition there. I mean the AI labs are completely in on it.

The brand-new start-ups are completely in on it. The rest of the companies, some of them see the light, some of them don't see it yet. And even when they see the light, it takes a while to get the engineers to all transform to get all the new processes to work that way, to identify what the new bottlenecks are. So I would say I would expect quite a bit of that to happen this year. And so we should see where we are at the end of the year.

Okay. All right. Well, with that, I am going to conclude our Investor Day. Thank you so much to our presenters for sharing the Datadog story. Thank you to all of you for spending 4 hours with us. If you want to watch it again, a replay will be available shortly on the website as well as the slides. So thank you very much. Have a good evening.

Good day, and welcome to the Q4 2025 Datadog Earnings Conference Call. [Operator Instructions] As a reminder, this call may be recorded. I would now like to turn the call over to Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead.

Thank you, Michelle. Good morning, and thank you for joining us to review Datadog's Fourth Quarter 2025 financial results, which we announced in our press release issued this morning. Joining me on the call today are Olivier Pomel, Datadog's Co-Founder and CEO; and David Obstler, Datadog's CFO.

During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the first quarter and fiscal year 2026 and related notes and assumptions, our product capabilities and our ability to capitalize on market opportunities. The words anticipate, believe, continue, estimate, expect, intend, will and similar expressions are intended to identify forward-looking statements or similar indications of future expectations. These statements reflect our views today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially. For a discussion of the material risks and other important factors that could affect our actual results, please refer to our Form 10-Q for the quarter ended September 30, 2020. Additional information will be made available in our upcoming Form 10-K for the fiscal year ended December 31, 2025, and other filings with the SEC. This information is also available on the Investor Relations section of our website, along with a replay of this call.

We will discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial measures in the tables in our earnings release, which is available at investors.datadoghq.com. With that, I'd like to turn the call over to Olivier.

Thanks, Yuka, and thank you all for joining us this morning to go over with a very strong Q4 and overall, a really productive 2025. Let me begin with this quarter's business drivers. We continue to see broad-based positive trends in the demand environment. With the ongoing momentum of cloud migration, we experienced strength across our business, across our product lines and across our diverse customer base. We saw a continued acceleration of our revenue growth. This acceleration was driven in large part by the inflection of our broad-based business outside of the AI-native group of customers we discussed in the past. And we also continue to see very high growth within this AI-native customer group as they go into production and grow in users, tokens and new products.

Our go-to-market teams executed to a record $1.63 billion in bookings, up 37% year-over-year. This included some of the largest deals we have ever made. We signed 18 deals over $10 million in TCV this quarter, of which 2 were over $100 million and 1 was in HCG land with a leading AI motor company.

Finally, churn has remained low, with gross revenue retention stable in the mid- to high 90s, highlighting the mission-critical nature of our platform for our customers.

Regarding our Q4 financial performance and key metrics, revenue was $953 million, an increase of 29% year-over-year and above the high end of our guidance range. We ended Q4 with about 32,700 customers, up from about 30,000 a year ago. We also added Q4 with about 4,310 customers with an ARR of $100,000 or more, up from about 3,610 a year ago. These customers generated about 90% of our ARR. And we generated free cash flow of $291 million with a free cash flow margin of 31%.

Turning to product adoption. Our platform strategy continues to resonate in the market. At the end of of Q4, 84% customers use 2 or more products, up from 83% a year ago, 55% of customers used 4 or more products, up from 50% a year ago, 33% of our customers used 6 or more products, up from 26% a year ago, 18% of our customers used 8 or more products, up from 12% a year ago, and as a sign of continued penetration of our platform, 9% of our customers used 10 or more products, up from 6% a year ago.

During 2025, we continue to land and expand with larger customers. As of December 2025, 48% of the Fortune 500 are Datadog customers. We think many of the largest enterprises are still very early in their journey to the cloud. The median Datadog ARR for our Fortune 500 customers is still less than $0.5 million, which leaves a very large opportunity for us to grow with these customers. So we're lending more customers and delivering more value, and we also see that with the ARR milestones we're reaching with our products.

We continue to see strong growth dynamics with our core 3 pillars of observability: infrastructure monitoring, APM and log management as customers are adopting the cloud, AI and modern technologies. Today, [indiscernible] monitoring contributes over $1.6 billion in ARR. This includes innovations deliver visibility and insights across our customers' environments, whether they are on-prem, virtualized servers, containerized host, severe deployment or [indiscernible] GPU fleets.

Meanwhile, log management is now over $1 billion in ARR. And this includes continued rapid growth with Flex Logs, which is nearing $100 million in ARR. And our third pillar, the end-to-end suite of APM and DEM products also crossed $1 billion in ARR. This includes an acceleration of our core APM product into the mid-30% year-over-year and currently our fastest-growing core pillar. We have now enabled our customers with the easiest onboarding and implementation in the market, while delivering unified deep end-to-end visibilities into their applications.

Now remember that even with these 3 pillars, we are still just getting started as about half of our customers do not buy all 3 pillars from us, or at least still not yet.

Moving on to R&D and what we built in 2025. We believe over 400 new features and capabilities this year. That's too much for us to cover today, but let's go over some of our innovation. We are executing relentlessly on our very ambitious AI road map, and I will split our efforts into 2 buckets: AI for Datadog and Datadog for AI. So first, let's look at AI for Datadog. These are AI products and capabilities that make the Datadog platform better and more useful for customers. We launched [indiscernible] SRE agent for general availability in December to accelerate [indiscernible] and internet response. Over 2,000 trial and paying customers have run investigations in the past month which indicates significant interest and showed great outcomes with Bits AI. And we're well on our way with Bits AI agent, which detects code level issues, generate fixed uses production contracts and can even help release the monitor [indiscernible]. And Bits AI security agent, which autonomously triage SIM signals, conduct investigations and delivers documentation.

The Datadog MCP server is being used by thousands of customers in preview. Our MCP server responds to the AI agent and user promps and uses real-time production data and restated of context to drive travel shooting, root cause analysis and automation. And we're seeing explosive growth in MCP usage with a number of tool calls growing elevenfold in Q4 compared to Q3.

Second, let's talk about Datadog for AI. This includes capabilities that deliver end-to-end observability and security across the AI stack. We are seeing an acceleration in growth for LLM Observability. Over 1,000 customers are using the product and the number of 10% has increased 10x over the last 6 months. In 2025, we broadened the product to better support application development and iteration and in capabilities such as LLM Experiments and LLM Playground and LLM components and custom and [indiscernible]. And we will soon release our AI agent console to monitor usage and adoption of AI agents and cutting assistance.

We are working with design partners on GPU monitoring, and we are seeing GPUs increase in our customer base overall. And we are building into our products the ability to secure the AI stack against pop-injection attacks, model hijacking and data poisoning among many other risks.

Overall, we continue to see increased interest among our customers in next-gen AI. Today, about 5,500 customers use 1 or more Datadog AI integrations to send us data about their machine learning, AI and usage

In 2025, our observability platform delivered deeper and broader capabilities for our customers. We reached a major milestone of more than 1,000 integrations, making it easy for our customers to bring in every type of data they need and engage with the latest technologies from cloud to AI. In log management, we're seeing success with our consolidation motion. During 2025, we saw an increasing demand to replace a large legacy vendors, we take out in nearly 100 deals for tens of millions of dollars of new revenue. And we improved log management with notebooks, reference tables, look patterns, calculated fields and an improved life tale among many other innovations.

We launched data observability for general availability. Data is becoming even more critical in the AI era. With data observability, we are enabling end-to-end visibility across the entire data life cycle. We launched storage management last month, providing regular insights into cloud storage and recommendations to reduce spend. We delivered [indiscernible] of autoscaling, so users can quickly identify which other provision clusters and deployments and rightsize their infrastructure.

In the digital experience monitoring area, we launched [indiscernible] analytics to have product designers make better design decisions with clear data about user experience and behavior. And we delivered run without limits, giving front-end teams full visibility into user traffic and performance and dynamically choosing the most useful sessions to retain.

In security, we are seeing increasing traction and are actively displacing existing market-leading solutions with cloud SIEM in nonenterprise. This year, our engineers shipped many new capabilities, including a tripling of the amount of content packs built into the product, and most importantly, the tight integration with Bits AI security agent, which has already shown promise as a strong differentiator in the market. We launched code security, enabling customers to detect and remediate vulnerabilities in their code and open source libraries from development to production. And we continue to advance our cloud security offering, adding pasture as code or IAC security, with [indiscernible] and resolve security issues with TerraForm. And we launched our security graph to identify and evaluate attacks fast.

In software delivery, in January, we launched future plans. They combined with our real-time observability to enable canary rollouts, so teams can deploy new code with confidence. And we expect them to gain importance in the future. [indiscernible] serve as a foundation for automating the validation and release of applications in an AI agentic development world.

We are also building out our internal developer portal, which includes software catalog and score cards to help developers navigate infrastructure and application complexity, provide reach context to AI development agents and ultimately enable a faster release cadence. In Cloud Service Management, we launched Encore, and now support over 3,000 customers with their incident response processes. And I already mentioned Bits AI agent, which bears us on go to accelerate our customer an resolution.

As you can tell, we've been very busy, and I want to thank our engineers for a very productive 2025. And most importantly, I'm even more excited in the model plans for 2026. So let's move on to sales and marketing.

I want to highlight some of the great deals we closed this quarter. First, we landed an 8-figure annualized deal and our biggest [indiscernible] deal to date with one of the largest AI financial model companies. This customer had a fragmented availability stack and cumbersome monitoring workflows leading to poor productivity. This is a consolidation of more than 5 open source, commercial, hyperscaler and in-house observability tools into the unified Datadog platform that has returned meaningful time to developers and has enabled a more cohesive approach to observability. This customer is experiencing very rapid growth. Datadog allows them to focus on product development and supporting their users, which is critical to their business success.

Next, we will come back a customer that was a European data company in a nearly 7-figure annualized deal. These customers lock focus observability solution had poor user experience and integrations which led to limited user adoption and gap in coverage. By returning to Datadog and consolidating 7 observability tools, they expect to reduce tooling overheads and improve engineering productivity with faster incident resolution. They will adopt 9 Datadog products at the stock, including some of our newer products such as Flex Log, observability pipeline, top cost management, data observability and [indiscernible].

Next, we signed an 8-figure annualized expansion with a leading e-commerce and digital payments platform. These customers products have an enormous reach its commercial APM solution had scaling issues, lack correlation across silos and had a pricing model that was difficult to understand or predict. With this expansion, they are standardizing on Datadog APM using open telemetry so their teams can correlate metric, traction logs to detect and resolve issues faster. And they have already seen meaningful impact with a 40% reduction in resolution times by their own estimates. This customer has adopted 17 products across the Datadog platform.

Next, we signed a 7-figure annualized expansion for an 8-figure annualized deal with a Fortune 500 food and beverage retailer. This long-time customer to the Datadog platform across many products, but still has over 30 other observability tools and embarked on consolidating for cost savings and better outcomes. With this expansion, Datadog log management and Flex Logs will replace the legacy logging product for all ops use cases with expected annual savings in the millions of dollars. This customer is expanding to 17 Datadog products.

Next, we signed a 7-figure annualized expansion with a leading health care technology company. This company was facing reliability issues, impacting clinicians during critical workflows and putting customer trust at risk. The customer will consolidate 6 tools and adopt 7 Datadog products, including LLM Observability to support their AI initiatives as well as Bits AI agents to further accelerate net response.

Next, we signed an 8-figure annualized expansion, more than quadrupling their annualized commitment with a major Latin American financial services company. Given its successful tool consolidation projects and rapid adoption of Datadog products across all of its teams, this customer renewed early with us while expanding to additional products, including data observability, CI visibility, database monitoring and observability pipelines. With Datadog, this customer showed measurable improvements in cost, efficiency, customer experience and conversion rates across multiple lines of business. That proof of value led them to broaden their commitment with us, and have firmly established Datadog as their mission-critical observability partner.

Last and not least, we signed a 7-figure annualized expansion for an 8-figures annualized like deal with a leading fintech company. With this expansion, the customer is moving their log data on to our unified platform. So teams can correlate telemetry in 1 place and save between hours and weeks in time to resolution for incidents. This customer has opted 19 Datadog products across the platform, including all 3 pillars as well as digital experience, security, software delivery and service management. And that is for our wins. Congratulations to our entire go-to-market organization for great 2025 and a record Q4.

It was inspiring to see the whole team at our [indiscernible] last month and really exciting to embark on a very ambitious 2026.

Before I turn it over to David for a financial review, I want to say a few words on our longer-term outlook. There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. So we continue to extend our platform to solve our customers' problems from end to end across their soft development, production, data stack, user experience and security needs. Meanwhile, we're moving fast in AI, by integrating into the Datadog platform to improve customer value and outcome and by building products to observe, secure and act across our customers' AI stack.

In 2025, we executed very well to deliver for our customers against their most complex mission-critical problems. Our strong financial performance is an output of that effort. And we're even more excited about 2026 as we are starting to see an inflection in AI usage by our customers into their applications and as our customers begin to adopt real innovations, such as Bits AI agent.

To hear about all that in detail and much more I welcome you all to join us at our next Investor Day this Thursday in New York between 1 and 5 p.m.

I'll be joined by our product and go-to-market leaders to share how we are serving our customers, who we innovate the broader platform and how we are delivering greater value with AI. For more details, please refer to the press release announcing the event or head to investors.datadoghq.com. And with that, I will turn it over to our CFO, David.

Thanks, Olivier. Our Q4 revenue was $953 million, up 29% year-over-year and up 8% quarter-over-quarter. Now to dive into some of the drivers of our Q4 revenue growth, first, overall, we saw robust sequential usage growth from existing customers in Q4. Revenue growth accelerated with our broad base of customers, excluding the AI natives to 23% year-over-year, up from 20% in Q3.

we saw strong growth across our customer base with broad-based strength across customer size, spending bands and industries. And we have seen this trend of accelerated revenue growth continue in January. Meanwhile, we are seeing continued strong adoption amongst AI-native customers with growth that significantly outpaces the rest of the business. We see more AI-native customers using Datadog with about 650 customers in this group. And we are seeing these customers grow with us, including 19 customers spending $1 million or more annually with Datadog.

Among our AI customers are the largest companies in this space as today, 14 of the top 20 AI-native companies are Datadog customers.

Next, we also saw continued strength from new customer contribution. Our new logo bookings were very strong again this quarter, and our go-to-market teams converted a record number of new logos and average new logo land sizes continues to grow strongly.

Regarding retention metrics, our trailing 12-month month net retention -- revenue retention percentage was about 120%, similar to last quarter, and our trailing 12-month gross revenue retention percentage remains in the mid- to high 90s.

And now moving on to our financial results. First, billings were $1.21 billion, up 34% year-over-year. Remaining performance obligations, or RPO, was $3.46 billion, up 52% year-over-year. And current RPO growth was about 40% year-over-year. RPO duration increased year-over-year as the mix of multiyear deals increased in Q4. We continue to believe revenue is a better indication of our business trends than billing and RPO.

Now let's review some of the key income statement results. Unless otherwise noted, all metrics are non-GAAP. We have provided a reconciliation of GAAP to non-GAAP financials in our earnings release.

First, our Q4 gross profit was $776 million with a gross margin percentage of 81.4%. This compares to a gross margin of 81.2% last quarter and 81.7% in the year ago quarter. Q4 OpEx grew 29% year-over-year versus 32% last quarter and 30% in the year ago quarter. And we continue to grow our investments to pursue our long-term growth opportunities, and this OpEx growth is an indication of our successful execution on our hiring plans.

Our Q4 operating income was $230 million for a 24% operating margin compared to 23% last quarter and 24% in the year ago quarter.

Now turning to the balance sheet and cash flow statements. We ended the quarter with $4.47 billion in cash, cash equivalents and marketable securities. Cash flow from operations was $327 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $291 million for a free cash flow margin of 31%.

And now for our outlook for the first quarter and the full fiscal year 2026. Our guidance philosophy overall remains unchanged. As a reminder, we based our guidance on trends observed in recent months. and apply conservatism on these growth trends.

For the first quarter, we expect revenues to be in the range of $951 million to $961 million, which represents a 25% to 26% year-over-year growth. Non-GAAP operating income is expected to be in the range of $195 million to $205 million, which implies an operating margin of 21%. Non-GAAP net income per share is expected to be in the $0.49 to $0.51 per share range based on approximately 367 million weighted average diluted shares outstanding. And for the full fiscal year 2026, we expect revenues to be in the range of $4.06 billion to $4.10 billion which represents 18% to 20% year-over-year growth. This includes modeling within our guidance that our business, excluding our largest customer, grows at least 20% during the year.

Non-GAAP operating income is expected to be in the range of $840 million to $880 million, which implies an operating margin of 21%. And non-GAAP net income per share is expected to be in the range of $2.08 to $2.16 per share based on approximately 372 million weighted average diluted shares.

Finally, some additional notes on our guidance. First, we expect net interest and other income for the fiscal year 2026 to be approximately $140 million. Next, we expect cash taxes in 2026 to be about $30 million to $40 million and we continue to apply a 21% non-GAAP tax rate for 2026 and beyond.

Finally, we expect capital expenditures and capitalized software together to be in the 4% to 5% of revenue range in fiscal year 2026.

To summarize, we are pleased with our strong execution in 2025. Thank you to the Datadog teams worldwide for a great 2025, and I'm very excited about our plans for 2026.

And finally, we look forward to seeing many of you on Thursday for our Investor Day.

And now with that, we will open up our call for questions. Operator, let's begin the Q&A.

[Operator Instructions] Our first question comes from Sanjit Singh with Morgan Stanley.

Congrats on the strong close of the year and a successful 2025. Olivier, I wanted to get your updated views in terms of where observability is headed. In the context of a lot of advancements when it comes to agentic frameworks, agentic deployments, the stuff that we've seen from [indiscernible] and the new frontier models from OpenAI, just in terms of like what this means for observability as a category, defensibility of it in terms of can customers use these tools to build homegrown solutions for observability? So just get your latest comments on defensibility of the category, and how Datadog may potentially have to evolve in this new sort of a agentic era?

Yes. I mean, look, the -- there's a few different ways to look at it. One is, there's going to be many more applications than they were before. Like people are building much more and they are building much faster. We covered that in previous calls, but we think that the -- this is nothing, but an acceleration of the increase of productivity for developers in general, so you can build a lot faster. As a result, you create a lot more complexity because you build more than you can understand at any point in time. And you move a lot of the value from the act of writing the code, which now you actually don't do yourself anymore to validating, testing and making sure it works in production, making sure it's safe, making sure it interacts well with the rest of the world with end users, make sure it does what it's supposed to do for the business, which is what we do with observability. So we see a lot more volume there, and we see that as what we do basically where observability everybody can help.

The other part that's interesting is that we -- a lot happens -- a lot more happens within these agents and these applications. And a lot of what we do as humans now starts to look like observability. Basically, we here to understand -- we try to understand what the machine does. We try to make sure it's aligned with us. We try to make sure the output is what we expected when we started, and that we didn't break anything. And so we think it's going to bring observability more widely in domains that it didn't necessarily cover before.

So we think that these are accelerants, and we -- I mean, obviously, we have a horse in this ramp up, we think that observability and the contact between the code, the applications and the real world and product environment and real user and the real business is the most interesting, the most important part of the whole AI development life cycle today.

And maybe just 1 follow-up on that line of thinking. In a world where there's a greater mix between human SREs and agentic SREs, is there any sort of evolution that we need to think about in terms of whether it's UI or how workflows work in observability and how maybe Datadog sort of tries to align with that evolution that's likely to come in the next couple of years?

Yes, there's going to be an evolution, that's certain. There's going to be a lot more automation. We see today, like we see the -- all the signs we see [ 0.2 ] everything will be faster, more data and more interactions, more systems, more releases, more breakage, more resolutions of those breakages, more bugs, more venerability, everything. So we see an acceleration there. At the end of the day, the humans will still have some form of UI to interact with all that. And a lot of the interaction will be automated by agent. So we're building the products to satisfy both conditions. So we have a lot of UIs, and we are able to present the humans with UIs that represent who the 1 works, what their options are, give them some their ways to go through problems and tomorrow the world. And we also are exposing a lot of our functionality to agents directly. We mentioned on the call, we have an MCP server that is currently in preview and that is really seeing explosive growth of usage from our customers.

And so it's a very likely future that part of our functionality is delivered to agents through MCP servers or the likes. Part of our functionality is directly implemented by our own agents, and part of our functionality is delivered to humans with UI.

Our next question comes from Raimo Lenschow with Barclays.

Congrats from me as well. Staying on a little bit on that AI theme, Olivier, the 8-figure deal for a model company is really exciting. I assume they try to do it with some open source tooling, et cetera, but -- and actually went from like almost paying not a lot of money to paying you more money. What drove that thinking? What do you think what they saw that kind of convince them to do that? And it's now the second one after the other very big model provider, so clearly, that whole debate in the market between oh, you can do that on the chip somewhere is not kind of quite valid. Could you speak to that, please?

I mean the situation is just very similar to every single customer we land. Every customer we and has some -- has had some at home grown. They have some personal run some open stores. like that's typically where we see everywhere. The -- it's cheaper to do it yourself is really not the case. So your engineers are typically are very well compensated and the big part of the spend in this company. The Velocity is the -- is what gates just about anything else in the business. And so usually, when we come in, customer starts engaging with us, we can very quickly show value that way. So it's not any different from what we see with any other customer. And also within the AI cohort, it's not original at all like or AI cost in general is who's who of the company that are growing very fast and that are shaping the world in AI and they all had a single product for all the same reasons, sometimes the different volumes because those complete are different, but the logic is the same.

Our next question comes from Gabriela Borges with Goldman Sachs.

Congratulation on the quarter. Oli, I want to follow-ups on Sandeep's question on how to think about where the line is between what an LLM can do longer term and the domain experience that you have in capability? If I think about some of Entropic's recent announcements, we're talking about LLMs as a broader anomaly detection type tool, for example, on the security vulnerability margin side. How do you think about the limiting factor to using LLMs as a normal detection tool that could potentially take share from the severability of the time in the category? And how do you think about the moat that Datadog has that offers customers a better solution relative to whether we map and LLMs can go long term?

Yes. So that's a very good question. We see -- we definitely see that LLMs are getting better and better, and we'll bet on them getting significantly better every few months as we've seen over the past couple of years. And as a result, they are very, very good at looking at broad sets of data. So if you feed a lot of data for an LLM analysis you're very likely to get something that is very good and that is going to get even better. So when you think of what we have finally mode here, there's 2 parts. One is how we are able to assemble that contact, so we can feed it into those intelligence engines. And that's how we aggregate all the data we get, we parse out the benefits, we understand where we think fits together and we can fit that into the LMM. That's in part what we do, for example, today, we expose these kinds of functionality behind our MCP server. And so customers can recombine that in different ways within different intelligence tools. But the other part that we think where the world is going for over observability is that right now, we are -- the CLC is accelerating a lot, but it's still somewhat slow. And so it's okay to have incidents and run post hoc analysis on those incidents and maybe use some outside tooling for them.

Where the world is going is you're going to have many more changes and more things. You cannot actually afford to have incidents to look at for everything that's happening in your system. So you need to be proactive, you'll need to run analysis in stream as all the data flows through, you'll need to run detection and resolution before you actually have outages materialize. And for that, you'll need to be embedded into the data plane, which is what we run. And you also need to be able to run specialized models that can act on that data as opposed to just taking everything and summarizing everything after the fact in 15 minutes level. And that's what we're uniquely positioned to do.

We are building that. We're not quite there yet, but we think that a few years from now, that's what the was going to run, and that's what makes us significantly different in terms of who we can apply [indiscernible] detection, intelligence and preemptive resolution into our systems.

That makes a lot of sense. My follow-up...

The data plans were to very real time. And there are many others of magnitude larger in terms of data flows that are volumes that would you typically feed into [indiscernible]. So it's a bit of a different problem to solve.

Yes. Super interesting. My follow-up for both you, Oli and David, you've mentioned a couple of times now and what's the conversations you have with customers about value creation within the Datadog platform. Talk a little bit about how some of those conversations evolve when the customer sees that in order to do observability for more AI usage, perhaps that Datadog [indiscernible] is going up. What are some of the steps that you can take to make sure the customer still feels like they're getting a ton of value out of the Datadog platform?

Well, there's a few things. I mean, first, again, the rule of software always applies. There's only 2 reasons that people buy your product is to make more money or to save money. So whatever you do, when the customers uses a new product, they need to see a cost savings somewhere or they need to see that they're going to get to customers that wouldn't get to otherwise. So we have to prove that. We always prove that. Any time a customer buys a product, that's what these are happening behind the scenes. The -- in general, when customers add to our platform as opposed to bringing another banner in or another product deal, they also spend less by the [indiscernible] platform.

Our next question comes from Ittai Kidron with Oppenheimer & Company.

congrats quite an impressive finish for the year. David, I wanted to dig in a little bit into your 26 guide. I just want to make sure I understand some of your assumptions. So maybe you could talk about the level of conservatism that you've built into the guide for the year? And also, you've talked about at least 20% growth for the core, excluding the largest customer, but what is it that we should assume for the large customer? And now when you look at the AI cohort, excluding this large customer, are there any concentrations evolving over there given your strong success there?

Yes. There are 3 questions in the first is overall on guidance, except what we're going to speak about next, we took the same approach as we looked at the organic growth rates and the attach rates and then the logo accumulation rates and discounted that. So for the overall business, which is quite diversified, we talked about diversification by industry, by geography, by SMB, mid-market and enterprise, we took the same approach. We noted that with the guidance being 18% to 20% and the non-AI or heavily diversified business being 20% plus, that would imply that the growth rate of that core business assumed in the guidance is higher than the growth rate of the large customer. It doesn't mean the large customer is growing any which way. It's just that, in our consumption model, we essentially don't control that. And so we took a very conservative assumption there.

And the last point, I think you mentioned is the highly diversified. We said 650 names in the AI is quite diversified, essentially would be very similar to our overall business, which we have a range of customers, but not the concentration level. And what we're seeing there is significant growth. But like our overall distributed customer base, growth and then potentially some working on how the product is being used, but nothing out of the ordinary relative to the overall customer base in the very diversified AI set of customers outside the largest customer. Hopefully that's helpful.

Yes. And can you give us the percent of revenue of the AI cohort this quarter?

We didn't -- have not put it in there.

Our next question comes from Todd Coupland with CIBC.

I wanted to ask you about competition and how the LLM rise is impacting share shifts. Just talk about that and how Datadog will be impacted?

Yes. I mean, there hasn't been -- in the market with customers, there hasn't been any particular change in competition in that we see the same kind of folks and the positioning are relatively similar. And we are pulling away. We're taking share from anybody who has scale. And I know there's been no -- there were a couple of M&A deals that came up, and we got some questions about that. The company is in there were not particularly winning companies, not [indiscernible] we saw in deals that in had a large market impact. And so we don't see that as changing the competitive dynamics for us in the near future.

We also know that competing in observability is a very, very full-time job. It's a very innovative market. And we know exactly what we have to do and have to do to keep pulling away the where we are. And so we're very confident in our approach and we're going to do in the future there.

With the rise of LLM, there's clearly more functionality to deal and there are new ways to serve customers. We mentioned our LLM product. There are a few other products on the market for that. I think it's still very early for that part of the market, and that market is still relatively undifferentiated in terms of the kinds of products they are, but we expect that to shake out more into the future. We think, in the end, there's no reason to have observability for your LM that is different from the rest of your system in great part because you LLM don't work in isolation. The way they implement their parts is by using tools, the tools are your applications and your existing applications or new applications you built for that purpose. And so you [indiscernible] integrated in production, and we think we stand on a very strong foot in there.

Our next question comes from Mark Murphy with JPMorgan.

Olivier, Amazon is targeting $200 billion in CapEx this year. If you include Microsoft and Google, that CapEx is going to exceed $500 billion this year for the big 3 hyperscalers that it's growing 40% to 60%. I'm wondering if you've collected enough signal from the last couple of years of CapEx that trend to estimate how much of that is training related and when it might convert to [indiscernible] where Datadog might be required? In other words, are you looking at this wave of CapEx and able to say it's going to create a predictable ramp in your LLM observability revenue, maybe what inning of that are we in? And then I have a follow-up.

I think -- I think it's pretty too reductive to tack that LLM observability. I think it points to way more applications, way more intelligence, way more of everything into the future. Now it's kind of hard to directly the CapEx on those companies into what part of the infrastructure is actually going to be used to deliver value 2 or 3 or 4 years from now. So I think we'll have to see on what the conversion rate is on that. But look, it definitely points to very, very, very large increases in the complexity of the systems, the number of systems and the reach of the systems in the economy. And so we think it's going to be like it's going to be of great help to our business, let's put it this way.l.

Yes. Great help. Okay. And then as a quick follow-up, there is an expectation developing that OpenAI is going to have a very strong competitor, which is Anthropic kind of closing the gap, producing nearly as much revenue as OpenAI in the next 1 to 2 years. You mentioned an 8-figure land with an AI model company. I'm wondering, if we step back, do you see an opportunity to diversify that AI customer concentration, whether sometimes it might be a direct customer relationship there? Or it could be some of the products like cloud code being adopted globally, just kind of creating more surface area to drive business to data? Can you comment on maybe what is happening there among the larger AI providers or whether you can diversify that out?

Yes. I mean, look, we've never been -- we're not built as a business to be concentrated on a couple of customers. That's not who we become successful. That's probably not how we'll be successful in the long term. So yes, I mean we -- at the end of the day, it should be irrational for customers, for all customers in the AI court not to use our product. So we see -- we have some great successes with the customers currently in that cohort. We see more. By the way, we have more that are more inbound there and more customers that are talking to us from the largest even hyperscaler level AI lab. And we expect to drive more business there in the future. I think there's no question about that.

And you're seeing that in some of the metrics we've been giving in terms of the number of native customers, the size of some of these customers. So to echo what Oli said, we are essentially selling to many of the largest players, which results in greater size of the cohort and more diversification.

Our next question comes from Matt Hedberg with RBC.

Congrats from me as well. Dave, a question for you. Your prior investments are clearly paying off with another quarter of acceleration, and it seems like you're going to continue to invest in front of the future opportunity. I think op margins are down maybe 100 basis points on your initial guide. I'm curious if you can comment on gross margin expectations this year, and how you also might realize incremental OpEx synergies by using even more AI internally?

Yes. On the gross margin, I think what we said is, plus or minus, the 80% mark. We try to engineer when we see opportunities for efficiency, we've been quite good at being able to harvest them. At the same time, we want to make sure we're investing in the platform. So I think what we're essentially -- where we are today is very much sort of in line with what we said we're targeting. There may be opportunities in longer term, but we also are trying to balance those opportunities with investment in the platform.

And in terms of AI, to date, we are using it in our internal operations. So far, it's -- the first signs of what we're seeing is productivity and adoption. We will continue to update everybody as we see opportunities in terms of the cost structure. Oli, anything else you want to go over?

Yes. I mean, look, we -- the expectation in the short midterm anyway should be that we keep investing heavily in R&D. We're getting a lot -- we see great productivity gains with AI there, but at this point of [indiscernible], it helps us be more faster and get to sell more problems for our customers. And -- but we're very busy adopting AI for the organization.

Our next question comes from Koji Ikeda with Bank of America.

Olivier, maybe a question for you. A year ago, you talked about how -- while some customers do want to take observability in-house, it's really a cultural choice. It may not be rational unless you have tremendous scale, access to talent and growth is not limited by innovation bandwidth, which most companies do not. And so it is a year later, and it does seem like the industry and the ecosystem and everything has changed quite a bit. So I was hoping to get your updated views on these thoughts, if it has changed at all over the past year and why?

No. I mean, look, it's something that happens sometimes, but it's a small minority of the cases. Like the general notion is customers start with some homegrown or attempts to do things themselves, then they move to our product and they care with our products. Sometimes they optimize a little bit along the way, but the general notion is they do more and more with us, they're relying on us for more of their -- solving more of their problems, and they outsource the problem and increasingly the outcomes to us. So I don't think that's changing.

Look, we'll still see customers here and there that choose to resource it into themselves, again, usually for cost reasons. I would say, economically from a focus perspective, it doesn't make sense for the very vast majority of companies. And we even see teams at hyperscalers that have all the tooling in the world, all the money in the world, all the knowledge in the world and that still choose to use our product because it gives them a more direct path to solving the air problems.

And our next question comes from Peter Weed with Bernstein Research.

Our next question comes from Brad Reback with Stifel.

Great. Oli, this sustained acceleration in the core business is pretty impressive. Obviously, you all have invested very aggressively and go to market over the last kind of 18 to 24 months. Can you give us a sense of where you are in that productivity curve? And if there's additional meaningful gains, you think? Or is it incremental? And maybe where you see additional investments in the next 12 to 18 months?

Yes. I mean we feel good about the productivity. I think the main drivers from us in the future is we still need to scale, and we're still scaling the go-to-market team. We're not at the scale we need to be in every single market segment we need to be in the world right now. And so we keep scaling there. So the focus now is not necessarily to improve productivity, it's to scale while maintaining productivity. And of course, there's so many, many things we can do. Actually, we -- even though we love our performance, there's always a bunch of things that could be better, territories that could be better, productivity that could be better, things like that. So we have or tons of things we want to do on the some things improve. But overall, we feel good about what happened. We feel good about scaling, and you should expect more scaling for us on the go-to-market side in the year to come.

Our next question comes from Howard Ma with Guggenheim.

I have 1 for Olivier. The core APM product, growing in the mid-30% growth. That is pretty impressive, and I think better than maybe a lot of us expected. The question is, is that a reacceleration? And is the growth driven by AI native companies that are using Datadog's real user monitoring and other DM features as compared to -- where as opposed to rather core enterprise customers that are building more applications?

Yes. I think -- I mean, look, APM, in general, I think has always been a bit of a steady eddy in terms of the growth, like it's a product that takes a little bit longer to deploy than others, which is further into the applications. And so it takes a little bit longer to penetrate within the customer environment. That being said, we did -- a number of different things we did that have with globe there. One is we invested a lot in actually making that onboarding, deployment a lot simpler and faster. So we think we'll have the best in the market for that and it shows. Second, we invested a lot in the digital experience side of it. And it's very differentiated, something our customers love is driving a lot of adoption of the broader APM suite, and we expect to see more of that in the future. And third, we make investments in go-to-market, we cover the market better. And so we're getting into more looks at more deals in more parts of the world. And so all of that combined helps that product reaccelerate growth quite a bit. And so we feel actually very, very good about it, which is why we keep investing.

Overall, we still only have a small part of the pure APM market, like that product, we scale at about $10 billion, including the EM, but the market is larger. And so we think there's a lot more we can do there.

I want to add, we talked about -- as Oli just mentioned, that we're not penetrated across our customer base, and therefore, we're continuing to consolidate onto our platform. So we have quite a number of wins where we already have other products. We already have infra and logs, and we're consolidating APM.

David, as a follow-up for you on margin, are the large AI-native customers significantly dilutive to gross margin? And when you think about the initial 2026 margin guidance, how much of that reflects potentially lower gross margin type to those customers versus incremental investments?

On weighted average, they're not. As we've always said, for larger customers, it isn't about the AI-natives or non-AI-natives, it has to do with the size of the customer we have a highly differentiated diversified customer base. So I would say we're essentially expecting a similar type of discount structure in terms of size of customer as we have going forward. And there are consistent ongoing investments in our gross margin, including data centers and development of the platform. So I think it's more or less what we've seen over the past couple of years, not really affected by AI or non-AI native.

Our next question comes from Peter Weed with Bernstein Research.

Can you hear me this side?

Yes.

Yes, apologies for the last time. Great quarter. Looking forward, I think 1 of your most interesting exciting opportunities really is around its AI and I'd love to hear kind of like how you think that opportunity shapes up? Like how do you get paid the fair value for the productivity you're bringing to the SRE and the broader operations team and really how you see competition playing out in that space? Because obviously, we've seen start-ups coming in, there's questions about Anthoropic and where they want to go. How does Datadog really capture this value and protect it for the business?

Yes. I mean, look, the way we currently say out a lot of these products is you show like the difference in time spent. And what the alternative is you try and solve a problem yourself and you have an outage and you you start a bridge and you have 20 people on the bridge and they look for 3 hours for the root cause and you work people night for that. It's very expensive. It takes a lot of time. There's a lot of customer impact because the [indiscernible]. And if the alternative is in 5 minutes, you have the answer, you only get 3 people look at that are the right folks and you have a fix within 10 minutes, you -- shorter impact on the customer, many, many, many less folks internally involved, lower cost. So it's very easy to make that case. And so that's what we sell the value there.

Longer term, as I was saying earlier, I think the -- right now, the state-of-the-art for resolution is [indiscernible], you have an incident and you look into it. And you diagnose it and then you resolve it. So yes, maybe you could be the customer impact from 1 hour to 15 minutes. But you still have an issue, you still have impact, you still distract the team you still have humans working on that. I think longer term, the what's going to happen is the systems will get in front of issues. They will auto diagnose issues. They will help premitigate or preremediate potential issues. And for that, the analysis will have to be run in stream, which is a very different thing. You can massage data and give it to an LLM for post-hoc analysis and a lot of the value is going to be in the gathering the data, but you also have quite a bit of value in the smarts that are done in the back end by the LLM for that. And that's something that is done by [indiscernible] in the end, the opening eyes of the world today.

I think as you look at being in stream looking at 4, 5 orders of magnitude more data, looked this data in real time, and I think judgment in real time on what's normal, what's [indiscernible] and what might be going wrong doing that hundreds, thousands, millions of times per second, I think that's what is going to be our advantage and where the -- it's going to be much harder for others to compete,

especially in general purpose AI platforms.

Our next question comes from Brent Thill with Jefferies.

David, I think many gravitate back to that mid-20% margin you put up a couple of years ago. And I know the last couple of years, including the guide are looking at low 20%. Can you talk to maybe your true north how you're thinking about that? Obviously, growth being #1, but how you're thinking about the framework on the bottom line?

Yes, the [indiscernible] because we try to plan with more conservative revenues, understanding that if the revenues exceed above the targets that we give, it's difficult in the short term to invest incrementally. So we're trying to do is invest first in the revenue growth and then layer in additional investment as we see if we see excess of target. So generally, it reflects, one, the continued investment, which we think is paying off, both in terms of the platform and R&D as well as in and including AI as a go-to-market. And then as we've seen over the years in our [indiscernible] raise, we've tended to have some of that flow through into the margin line and then re-up again for the next phase of growth.

And any big changes in the go-to-market or big investments you need to make, David, this year to address what's happened in the AI cohort or not?

We're continuing. It's very similar to what we're doing, which is to try to work with clients to prove value over time that reflects -- that manifests itself in our account management and our CS as well as our enterprise. So no, I think for this year, we are looking at capacity growth, including geographic, deepening the ways we interact with customers, expanding channels, very much similar to what we've done in the previous years.

That's going to be it for today. So on that, I'd like to thank all of you for listening on this call, and I think well many of you on Thursday for our Investor Day. So thank you all. Bye.

Thank you.

Thank you for your participation. You may now disconnect. Everyone, have a great day.

Good morning, everyone. Welcome to day 2 of Nasdaq London. We're really happy to kick it off strong with the Datadog management team. We're really happy to have David Obstler, Chief Financial Officer of Datadog, to join us for day 2. Thank you, David for coming.

Thanks for having us. Appreciate it.

Yes. So David and I have known each other for years now, going back to the IPO...

7 or 8 years.

7 or 8 years. You guys were the shiny new kid on the block and time flies.

You say I'm not shiny. I'm shy. Well, I certainly not shiny.

That might be your thing. But I thought we'd kick off maybe just sort of at a foundational level for our European investors, right? And I call this sort of the Datadog flywheel. Like since you guys have been public, yes, you guys have delivered kind of top decile growth in enterprise software. That's come along with great margin as well. So wondering if you could describe the core elements of the business model that allows the company to deliver that combination of both growth and profitability.

Yes. Good question. I think about it a lot. Thank you to our founders, Alexis and Oli because they created a platform that can be adopted without friction, not a seat model, a usage model, very strong time to value. So one of the things that has helped Datadog continue to invest has been a really efficient go-to-market where once our clients land and adopt the platform, they can adopt it with the assistance of our salespeople and our account managers, but also can do a lot of it themselves.

And that creates a very good return on sales dollars. And I think that has enabled us to invest at the top level in terms of R&D, which we've maintained around 30% of revenues. And so that combination allows us, along with very good gross margin and cloud operations management to maintain and grow the profitability and cash flow yet be to draw in terms of reinvestment. I think that was shown in the last year where we reaccelerated investment both in R&D and particularly in sales and marketing, and we're able to get very good return from that, whether that be the places where we put some quota-carrying reps as well as in some of the new product investments we've made.

Yes. No, it's a really important part. I think when I think about Datadog, when I think about the like the leverage that you guys get in R&D, we're the few companies that can launch a product. And then with a couple of years, it depends on the category, we start seeing revenue from that product hit the income statement probably quicker than most enterprises...

And I'd also add that there's a lot of efficiency when you have as big a platform, you're not having to create both the platform and the new product. You're able to add applications on top of the product in a very efficient way. And then we talked about the adoption -- frictionless adoption in the platform and both those together result in very good time to value in the product investment.

And so I guess the question is, going forward, does that flywheel carry forward? And we just sort of level set where Datadog is going as a company. You guys are in this sort of multiyear evolution from a company that's in observability and monitoring, but you guys are moving into security, you guys are moving into helping customers remediate some of the issues that you guys flagged for customers. And so in that kind of long-term evolution, does that flywheel change, particularly from a go-to-market perspective? And will this kind of broader vision going from observe, Secure Act? Will that have a structural impact on the underlying unit economics of the business?

Yes. I think, again, we're in a good position where we have against the backdrop of long-term trend of modernization of software stack applications and the consistent conversion to cloud, which is creating this long-term growth are -- the world is getting more complex. And essentially, our customer base in DevOps has been on this journey with us in using more and more information in different ways to solve problems. And we've been following that. So that goes from the trend of having a single pane of glass to the trend that we're seeing now in AI and service management, where you're using the platform to detect problems, analyze it, suggest what is most likely and long term, we think, have some self-remediation.

And AI is facilitating that. At the same time, our customer base in DevOps and production is getting -- is using more signals, whether that be the security signals we're talking about or on to our recent investment in product analytics because it really is a continuum where all the different parties are software developers, production engineers, security, product analysts benefit from being on the same platform and be able to share information frictionlessly. So I think we're seeing a long-term trend and technology and practices facilitate a broadening of the platform, which is at the very core of what Datadog is doing in this flywheel, as you call it.

Yes. So where are we in that term of long-term evolution? The core observability business is quite healthy. But in terms of that move into cloud security, product analytics, remediation, where are we in that journey, if you want to use a baseball analogy or any other sort of framework to set the stage on where we are in that longer-term evolution?

Yes. I think in terms of the base, which is more and more workloads and more modern workloads, we're early innings because the percentage of modernized workloads according to research analysts is in the upper 20s, maybe 30s. It's hard to tell. We've got a long way to go there. And I think we're really in the early innings when it comes to adding this additional functionality beyond the observability platform.

When you look at the revenue contributions that have been created and what's happening in terms of the product formation and the growth, there's lots of opportunities. We know we're in early innings on the use of AI and software platforms. And for us, we're still in early innings. We're coming at this from the observability side, and we're starting to get traction. We can talk about a number of examples, but we're in early innings there where we're making investments, but we're still not in maturity. We're still adding to both the sales and marketing and platform. So I think in terms of the complementary products, we're in very early innings.

Yes. And that kind of dovetails when we talk to other players in the ecosystem, just in terms of the enterprise AI, custom application build cycle, it's -- we're seeing some movement, but still feels pretty early. Obviously, you have an AI native cohort that's doing really, really well for you guys. But that dovetails pretty consistently in terms of what...

And I think it's a good example. I mean we're one of the more tech-forward companies. And we just last week at re:Invent with GA on our first AI native application, which was the SRE agent. So when you think about it, we've been talking about this for a while, and we've been investing in it, and we're GA now. So it takes a while in terms of testing, developing and then getting client adoption.

Yes, that's pretty exciting in terms of the SRE agent. Let's talk about a little bit about Q3 results because it was notable on a couple of fronts, right? So the headline number, total revenue growth sustained at 28%. That was in line with last quarter. If you look at the core business ex AI natives, that accelerated. Your SMB business had already been doing well. So I guess the question for you, David, is, did anything change in the demand signal if you compare early 2025 to what you guys see in Q3, that sort of non-AI cohort uptick. What were some of the drivers behind that?

Yes, it's a good question. I think we -- if you remember back to the adjustment after COVID to the optimization, we then got to something we call stabilization, which means that a lot of clients had done the work on the cost side and we're looking at getting back to normal in terms of investment. And over the last couple of quarters, we have seen a more positive buying environment, which means more focus on migration and also more consolidation of the platform from Datadog.

And so I think we're not in that bubble market. We're not in a market that's focused first on cost control, we're in something in between, which is, I would say, a good strong market. And then it broadened in the last couple of quarters. So you get the total environment from seeing what's working across the board. And I think we had seen enterprise working. And one of the things -- this is not AI. Some asked, well, of course, you're seeing this because in SMB, most of those customers are SMBs. By the way, for us, SMBs can be pretty large revenue companies. They have employees less than 1,000, but they can have hundreds of millions of revenues.

And I think we have -- excluding all of that, we can get to that. We are seeing within the SMB cohort, which is a meaningful cohort for us, strong demand signals, that's translated into higher net retention and more logos. Now it's a little hard to separate because we are essentially at the year anniversary of accelerating reinvestment. So at the same time that the market has been better, we've also been investing both in product but in quota capacity. And those 2 certainly interact in terms of producing the results we did.

Yes. I think one of the things that stood out to me in the last couple of quarters, just in sort of public cloud world, the classic pillars of public cloud seem to be coming back with respect to migrations, with respect to modernization projects, digital transformation, which are some of the kind of foundational drivers for Datadog. So it sort of makes sense how that sort of, as you say, interacts with the greater investment.

Yes, definitely the case.

On the call, you guys commented that the better consumption trends continued into October and the pipeline for Q4 looks good. When you guys think about 2026, how do you sort of judge whether the trend lines you saw in October can prove durable? Like what are you guys sort of looking for? What's the data? How do you sort of assess that?

Yes. We're a consumption model. So we have a lot of experience in looking at trends. But it also -- because we're consumption, it also is hard to predict the exact slope of the line, but the signs improved in Q3. We've told everybody that they stayed strong in October. And when we look at what we can see, which is the pipeline and what we're seeing, so we're able to monitor usage net retention on a pretty much real-time basis, and we can monitor how that might be going through new logos and then new commitments.

And they're all positive trends. We'll let everybody know we'll complete the year, and we'll think about where we are relative to next year when we give our guidance in February. We'll continue in the same vein of giving conservative guidance relative to trends. But the good news is the entry point and sort of the trends, the win behind us seems to be strong in entering into 2026.

Yes, that starting point looks pretty strong. There's so much to like about Q3 results. One of the other things that stood out to me was the new logo performance. So new logo annualized bookings more than doubled year-over-year. The land sizes are getting larger on the 7-figure wins across telco, financial services, hardware. Why now for the bigger lands? And how durable is that momentum with new logo ARR?

That's a good question. So I think they're correlated with -- we've always said we're not the kind of company that works for years to try to get the full envelope. We are land and expand. But as our platform has grown and as it's become -- the whole trend towards platform has become more evident, it becomes more of an imperative to land with more of the platform. So we're seeing that. At the same time, I think, we're better at it. So -- whereas we're not waiting for all the departments. It's not a centralized spend like some other industries are used to.

But we are getting better at analyzing how to go a combination of top-down and bottoms-up. And that is everything from being comfortable with the multichannel approach, which includes partnering with the channels, the GSIs, having a bigger enterprise sales team as well as doing things like adoption credits in exchange for long-term deals, which really facilitate the speed of the consolidation. So I think we've also gotten more diverse in how we go to market. And the 2 of those together, the platform and expansion and the way we approach it, have resulted in some dividends here.

Yes. And so I think the theme that we've been having thus far is the backdrop of the demand environment looks generally attractive. You guys are executing better. I mean 2025, you guys explicitly told us that this was going to be an investment year. And judging by the results year-to-date, I think it seems like those investments are paying off. And so could you outline for us, David, the key investments the team has been making, how you measure return and productivity on those investments? And what's the intention in terms of sustaining that piece of investment going forward?

So start with sales and marketing, where we really do bottoms up. We look on the white space. The clients that have demonstrated signals to be buyers, that includes cloud, that includes hands on keyboard size of DevOps organizations. And we're a follower of that. So we are able to look at where the TAM is. And then we look at that versus our coverage. And that can be on accounts in the Northeast in the United States all the way to feet on the ground in Brazil and India and some of the other markets.

And when we look at that, we still see we have a lot of uncovered territory. And so are we doing at the right pace? So we look at things like the return on sales and marketing dollars, CAC return, which has been able to be maintained. We look at productivity of salespeople because we don't want to have 2x the salespeople to produce the same sales. We look at that. And then we look at the performance of some of our newer investments. And all of those are giving us signals. So that's how we sort of handicap, and that's what we use in planning and can stand by the statement that we can continue to expand our go-to-market.

We're also, I think, doing go-to-market in some different ways like a different way of going to market in terms of the government and Fed section. We're having more data centers. I think there's lots of ways from the marketplace to channels and resellers that you need to do it. For instance, security, we've learned. You need to add on some additional ways of going to market, given it's more of a channel and centralized. So these are some of the investments.

So then you turn to R&D and then you go and look at essentially what's the landscape, what are the revenues today and in the future that are being created in various platforms. And are we able to add that functionality and be best-of-breed in our platform? And the answer has been yes. So we are continuing to invest at this industry-leading around 30% of R&D. And then there's the thing I think Oli, Alexis, et cetera, are really good at. Well, what about not what is being used today, but we think might be used in the future. And that gets to things like the AI-ness to the platform.

And are we going to be the one given our competitive advantage in the customer base and the platform to innovate everybody and not be in a position where there's functionality by a startup that we don't have. No, what we're trying to do is do that and look ahead all of it because we get really good signals from our large customer base -- our diverse customer base and the fact that we can see usage. So that's sort of what we look at. It comes down in the end to things we've been reporting, which is what are the SKUs that are being adopted as part of the package and are we getting traction. And you know from following us that we have reliably given information to evidence that we are inventing new SKUs or improving SKUs that are being adopted by the customer base.

Yes. I think the way you guys price, you can see this -- I mean because you guys price on sort of product basis through a commitment model. So you can sort of test whether an individual product is seeing the adoption.

We price on commitments, so you buy, let's say, $2 million, and then underneath it is a price grid that's consistent with that. And then we can see every day, like we can go in and see what customers are doing and also that customer should be using this, but really isn't as much, that is gold with information. And it allows us to determine not only usage, but are we pricing it right? How are we bundling or putting it together with a transparent sign per product. So it's a really good competitive advantage for the expansion.

Yes, it's a great point. So let's talk a little bit about the AI opportunity. And this is a question that probably anyone always gets about, how is AI impacting the growth of ex company that I cover. What would you say is the clearest way to think about how AI will drive growth at Datadog in the future?

Well, the clearest and most proximate is that there is our companies, technology companies, which we're calling AI natives here. But essentially, you can scratch out AI and say cloud native because they're modern software companies. Are they going through a demand cycle that -- where their revenues and their usage is growing? And do you have product market fit? Because most of the first investment, as we all know, has been in the infrastructure side, the model building, things like that. And the answer to those has been a resounding yes. And that's one of the things you've seen in terms of the acceleration of the revenues in the AI, let's put the largest company aside, but you concluded as well.

Is that accelerating? Yes. And is Datadog the right product for that? Yes. And so I think you're seeing focused and concentrated usage. Now that's the beginning of this because essentially, the way most companies are using AI is they're still in training and experimentation, but to the -- and they're mainly calling out to these model providers. There's not -- they're very beginnings of -- you form your own model. You have your own GPUs. Think about who the commitments are from these neoclouds. They are the hyperscalers and the model providers and the AI natives.

So we're in that stage right now, Stage 1, potentially of playing with it and then getting to the point where you're starting to see some adoption in enterprises. And you can tell that from things like the number of companies, the thousands that are sending us data, the use of the platform, but you can tell that it's very early. So I think this is going to evolve in many ways, but we're seeing good demand signals. Then you're seeing Datadog itself. So that's one way. The end market first concentrated with these companies, spreading out into production models and applications.

Then you look at Datadog. We're a good example. We're a platform, we're a software vendor. What are we doing? Are we integrating AI into our platform and releasing it? And we are. So we're having quite a bit of investment in putting agents into our model, putting research. So that's another way we think. Then there's some industry trends where what is -- might be the knock-on effect of all this where in the past we've seen technology change container serverless, it's accelerated the impetus for replatforming. And this one has the trends of making software development more efficient.

And so we're beginning to see that internally, and we're beginning to see that in our customers. So that is part of the long-term trend, is this going to improve the growth of the growth rate of the replatforming of legacy and on-premise applications to the cloud. So it's important in a number of ways. The earliest you can see is the use of Datadog by those AI natives. And then over time, we believe the knock-on effects of that are going to be more pervasive to our results.

And ultimately, this culmination just more public cloud consumption is the core demand driver.

That's what we think.

Just to think about the AI natives, which is 12% of your business versus kind of the enterprise opportunity. Obviously, AI natives are growing quite well and accretive to your growth. What events are we seeing that kind of your enterprise base are building those Gen AI agentic applications, getting them into production environments? To what extent is that a driver or part of the better growth you see today? And how meaningful of this could be over the medium term?

Yes, definitely. We're seeing signals like the use of our LLM monitoring. The first thing we do is we build integrations to access the data so our clients can send us data, and we can receive it. And we're seeing an increase of the rate of that thousands. And then we are -- it's small, but we're seeing direct GPU use go up. So I think there are certainly demand signals there, but it is not at this point where we're seeing the most contribution to revenues.

So again, we can control our own use of AI in production environments. We can't control the use of our customers. So what we can do is set ourselves up so that whatever they do, they can use Datadog to access the data and the integrations, and we're certainly doing that. And although small, the trends are positive in terms of that spreading. I think that this is going to be the biggest part of the opportunity or else, the natives aren't going to do as well because it has to spread, but it's still early days, and it could go in number of different directions in terms of pace and how it's done.

Yes. It's a great point. So one of the things investors flag, and I've been covering this category for a long time, is that there's a lot of players in observability. And you sort of think about observability and security, it's sort of been on a crash course for a few years now. You guys have entered the market. I think this is further underscored by Palo's recent acquisition of a private observability competitor called Chronosphere. I'd love to hear your thoughts on Palo Alto's move to observability and what it can mean for the competitive environment going forward for Datadog?

Yes. So this isn't new. You know this. We've been working in these trends for a long time. But there have been these point or smaller solution companies that have been birth for the last 15 years, but certainly, I mean, in my eighth year, and we've had this exact same discussion many, many times. I'll get to this company because we've had the exact same discussion on this company. So what has tended to happen is there's been point solutions or smaller companies that get birth. They often get sold. The reason why is there -- our competitive advantage is in platform and amortization of tech investment.

And we've been, I think, smart enough, fortunate enough to take advantage of that. So you've seen Splunk try to do this. You've seen ServiceNow try to do this. You've seen the birth of all sorts of point solutions. And generally, they have not captured a significant share of the market. And they've ended up being -- all the way back to the Cisco acquisition of AppDynamics, they've ended up being acquired. And when they get acquired, this is the past, they tend to be swallowed up and there's less innovation, and we lick our chops.

So in this case, for Chronosphere, this has been going on for 4 years. So the same question, which is they're out there, they're saying they're doing this, that, this, they're cutting price, et cetera. And a couple of things, it hasn't worked. What they have is they don't have an observability solution, they have essentially a metric store plus, which is the way you look at infrastructure. They've been trying to do this, which is cut the price, but it's basically bought on functionality. And we've self-innovated. So we have the exact same product. We have a much bigger sort of product line.

So clients can use us or can use us and Chronosphere. Observability is not a category where for -- in a company or enterprise or even a cloud native necessarily, you get 100% of the wallet share. So we now have Palo Alto that has -- that is not really an observability that has acquired a company that doesn't have an observability platform, but has a piece. We have 30 SKUs. They have a couple of them. And they're going to continue to try to sell this. But again, they don't have more than that.

So I think we're in a situation where it's the same thing that's been going on for -- with a number of point solution companies, but also the last few years. Now is this going to benefit or weaken this because it's been going on. And it remains to be seen. You have the Palo Alto distribution, very strong position in security, but not in observability. How do you compete with someone who has this installed base, this sales team with what they've said they're going to do, which has run it as an independent entity and what happens with the investment. We don't know, but this isn't new. I think it just is a flare-up of a discussion that's happened a number of times in the last years.

Yes, there's definitely been a pattern there. When we think about -- just a comment, I mean, your point that you don't typically get 100% of wallet share. I mean there's been studies out there and particularly a couple of years ago, the average enterprise had like over a dozen different monitoring tools. Let's talk a little bit about the security business. So some interesting disclosures you guys provided. It's now crossed $100 million ARR, growing mid-50% year-over-year. That's an acceleration versus Q2. Where are you winning most in security? And what are the top 2 or 3, why you win proof points versus like your competitors?

So I think what we've done so far is we have for cloud natives, which are mainly SMS, which tend to have more closely related DevOps and security. It's called DevSecOps. We've been able to successfully bundle our products and sell through. That's not as big a market as we know security is. So what's happened more recently? What's happened more recently and the reason for the acceleration is that we've gotten to product maturity in a product, which is cloud SIEM, where we've invested in the underlying logs. We are a very strong player in observability logs.

When you say logs, you have to have a word in front of it. You have to have IT logs, security logs, observability logs. And so we've basically broadened our capabilities in logs and put them in cloud SIEM to create a very compelling product with synergies. And then it's against a backdrop where we have some disruption in the market with Cisco's acquisition of Splunk, the sort of disintermediation of Splunk, which we've already done in observability. And that's created -- and we've gotten better, I said, at enterprise. So I think we have complementary SMB DevOps. We have this enterprise cloud SIEM.

That's probably the most proximate opportunity, but what's going to happen next? Everything from how do you continue to develop to what I mentioned before is we've really not had a channel-led, a CISO-led motion here, and we've invested in our channel partnerships. And literally, in the last months or so, we brought on our first specialty security salespeople. So everything we've done so far has been product-led or strong proximate synergies to products we've already had installed.

And if you read the earnings script, you'll see if you go and you look at the names of the companies, what you see is we have a lot of products in this bank or this automotive company, and they've added a cloud SIEM. And I think it's important to think about the fact that we're not using the word SIEM, we're using cloud SIEM, meaning we're not trying to displace on-premise, Splunk, SIEM for various uses. So we're focused on the most proximate use cases and trying to improve the way we are able to attach and get market. So I think that's the big change you've seen in that we have sort of another motion going on, still early days, but we're really focusing on the places we have the most synergy and the most proximate type of workflows.

That's awesome. I'm exciting to think about going into next year. Well, out of time, David. Thank you for spending 30 minutes...

Yes. Thank you. Great discussion. Thank you, everybody. Thanks. Thank you.

Okay. Great. David, wonderful to have you back to the UBS Tech Conference in Scottsdale. It was nice to see you out on the course. David played well as I did miraculously. So we had fun outside of the venue here.

Thanks for having us. It was a beautiful -- yesterday it was a beautiful day.

Gorgeous, wasn't it? And I hope you guys had a little fun playing with Sergio for those that didn't.

So David, maybe we'll start. Like there were -- there are a number of things about your recent quarter that were really strong. Your core did better. Your AI native breadth widened out a little bit. You renewed that big contract. So a lot of good things to talk about. So let's take those in order.

So I think one of the things that stood out to me was the strength in the core. So exiting out all the AI natives, the core accelerated. Can you add a little bit more context as to where that came from? What went well in the quarter to result in that?

That's a great question, and thanks for that intro. Yes, I think we saw a very broad-based strong quarter. That was -- we were very gratified in that we've been expanding our sales capacity. We lagged a little bit on the back end or maybe we're a little conservative on the back end of the bubble bursting and then has spent the last 1.5 years plus scaling, and we found that produce returns. And that came in the form of more new logos and larger new logos. And we also saw metrics like the maintenance of attainment, the maintenance of CAC return and things like that. So I'd say the first thing is we were able to execute in go-to-market.

The second thing was we have had really good success in the platform and in attaching products. So that helped produce -- excluding the AI, that helped produce a stronger net retention. We also saw some good distribution. We had had, I think, a little bit of a pushback in the SMB side of the business, 1,000 employees or under. And these are not tiny companies, but they have under 1,000 employees. And we saw a rebound there. So we saw strength in SMB. At the same time, we maintained strength in enterprise.

And then lastly, I think we have a constructive buying environment. We -- it's not a bull market, but it's not a market that is obsessed with cost and is pushing back on optimization. So the headwinds that we had seen in other parts of the -- earlier in the market kind of abated. And all of that came together to produce a pretty broad-based quarter of strength.

David, on that last point around facing a slightly more constructive buying environment versus prior quarters this year. What do you think changed? Because just as you're saying that I'm -- and I'm reflecting on all of the software company reports that we've had to digest, not everybody has called out a more constructive environment. So could you pinpoint what you feel like turned and maybe it's a little bit unique to your category of software?

Yes. I think infrastructure. So I think we basically were based on the replatforming of software, modern software and then putting that in the cloud. And I think you have to go through some infrastructure to pull that off. So I think a combination of the return or the strengthening of the modernization of software stacks. It may be -- I mean this is all outside the AI native we're talking about.

Yes, exactly.

It might be also -- we've seen in other technological evolution periods, we've seen an acceleration of modernization of software stacks. And we may be seeing some of that. So I think that's probably why the infrastructure companies and the foundational companies have done now. That's what we're seeing.

And if part of the tailwind that helped you was companies modernizing partly by migrating more workloads off-prem to the cloud. Does that dynamic feel like it's a continued tailwind, David?

Yes. We think that, that's going to be -- that's a long-term tailwind. And so when you look at the percentage of workloads that are in the cloud, which Gartner and others say is in the 20s or 30. And when you see major parts of the economy in large enterprises, in regions and in governments, not having modernized, we think that this will continue for a long time. So it has a lot of sustainability to it, which is a friend of Datadog. And the signs we've seen already in the year-to-date as well as we said that continued in October, we've seen a good investment cycle. So we're optimistic. We can't predict the future, but we're optimistic.

What about across the product suite. Datadog obviously offers a complete platform. But you can think about it partly as core infrastructure monitoring, APM, log management. Was that inflection you're referring to in the third quarter unique to any part of that suite?

No, I think it's pretty pro rata. For the most part, our customers are using the platform in a more consolidated way. They're moving from point solutions and consolidating on the platform. So I think you have a similar type of demand development, which is in the metric traces logs, the core 3 pillars, relatively pro rata strength.

And then you have -- I think we announced that our digital experience crossed $300 million. So that's the next group of products that continue to be adopted. We also have -- when we get into this, contribution, although smaller from such products as Cloud SIEM in the security family, from product analytics and from service management on call and enhance that.

Okay. That's a good discussion of the core. How about we talk about the second positive thing, which is the AI native cohort collectively was 12% of your revenues. And as you -- and Oli pointed out, it's well beyond that one customer. So much, much more breadth.

Yes.

So I guess my first question on this front, David, is how you're pulling that off? Because it sounds easy to be selling your product to this fast-growing cohort, but it's not easy for everybody. We just -- for those of you that were on the call with MongoDB last night, Dave, who you know well. Dave was admitting, and he's been quite candid about this, that Mongo hasn't penetrated those AI natives as well as they would like. And so it sounds like they're trying to take a page out of the Datadog playbook to go after them.

So what have you done right such that your AI native penetration is maybe larger than almost any software company we follow?

Yes. So I think when you think of where Datadog has won a lot, it's been in what we used to call cloud natives, right? But now we're talking about a group of those companies called AI native.

Correct.

But Datadog monitors client-facing mission-critical cloud applications. And these AI native companies are cloud-native companies, meaning they don't have legacy stacks. Their whole product is delivered digitally through the cloud. They're modern software companies. So it's a really good fit when you think of how Datadog architected its platform. It's always been a really good fit. And then how it's frictionlessly adopted, how it grows with the client. And I think it's even probably a better fit now than it was in the cloud native period of expense growth because of the breadth of the platform. So these companies that have a lot to do and they have a lot of R&D investments are finding they're able pervasively in their observability needs to use Datadog.

Now in addition, they're experiencing great success. Many of them are releasing their revenues. They're experiencing a very significant demand cycle. And our platform has been architected. And I think this is one of the things that might be different than some of the other companies, so that it is very easy to use, you sort of set it up and the clients can use it frictionlessly. So as their workloads increase, our revenues increase. And because it's so easy to implement for these modern companies at the kind of platform they use. So I think it's a really good product market fit.

And I think we mentioned, as you said, that this is not just about a concentrated set of customers. It's over 500 customers. It's over 100 customers that are spending more than $100,000, over 15 that are spending $1 million, and it's most of the leading customers. So we have a set of AI/cloud natives that are pervasively adopting Datadog, and we're succeeding through the way we've always kind of go to market and sold our product.

Are they using Datadog in any different way than like a UBS or other conventional large company would across your suite? Are they quite concentrated in one particular area? Or they -- are the workload types the same as a typical large enterprise?

The workload types are very similar. They're using the metrics, traces and logs. Essentially, once -- it's not -- we're not talking about model training here. We're talking about production. And so when you get to the production environment and delivering the software, it's similar to other what we call cloud natives. So most of them are using the metrics, traces and logs and then the digital experience, some using the service management, some using the security, but very similar usage patterns to what we see in the rest of our customer base.

What about, David, to continue this thread around the analogy to traditional enterprises. What about the contract structure, the duration, renewal cycle? Does that also feel similar? Or are there some differences that are noteworthy to call out to the audience?

Well, in cloud natives, I think you generally have a -- what is tended to be an annual commitment. It's based on what you know about the capacity planning. And then as clients grow in both cloud natives and AI natives, you'll find that as they're growing their business, they are going past their initial potentially conservative commit. And because we price based on volume and on term, and we didn't invent this the same way the cloud providers, the hyperscalers do. What the clients find is that as they get to certain levels of usage, they can get a better deal. They can sort of get a better price point by committing and so similar to others, they have gone through this cycle of then committing longer and at higher volumes. And that's the same thing that happened with the largest customer. That's the same thing that happens with other rapidly growing.

I think contrast to enterprise, enterprises may both have more predictability and more control. So they might be willing to commit longer, 3-year contracts. But cloud natives generally have committed for the most part to around annual contracts, maybe some more.

And another thing that is similar is we've helped them use the product. So for instance, if they are not optimizing or using too much of one or the other, we help them. And because we have all these products and they make a commit, they can still satisfy the commit even though they're controlling certain products more.

Got it. And if we take a subset of those AI natives, and we talk for a quick moment about model providers. I'm just curious if their needs are a little bit different because we're all watching the compute commitments that a lot of those model providers are making.

I would assume, David, that it's correct that to the extent that any of them scale up massive training compute clusters, there's less pull-through there for Datadog than when they scale up inference compute footprint. Is that correct? Is there any real pull-through on the training side that's noteworthy yet?

Most of them are doing their own -- they're doing the analysis of their own training. There hasn't been.

They're using their own tools for them.

There is no tools. That's like their core products, right? So we're production, we're monitoring your delivery of models. So I think when you're reading a lot about -- and it's confusing when you're reading a lot about the hyperscalers, the neo clouds, all this GPU, the vast majority of the use cases is in model training. And for better or worse, that's not -- Datadog is not monetizing that. Datadog is monetizing the production environment. And so that's why the use cases, that's why the types of products are very similar to the other applications that are in production.

Yes. David, there was a lot of angst in the investor base prior to this renewal of the large one about whether they would, how much. Looking back at that period of uncertainty, what do you think maybe the Street didn't appreciate about the Datadog relationship such that you were able to renew and actually expand?

Yes. I think there's a couple of things. First of all, we've tried to tell everybody that our gross retentions in large customers are in the very high 90s, really. So 98 or plus. So we basically have tried to tell everybody that it's really a fringe case when a large customer leaves Datadog. Yes, they may change their spend a little bit or whatever. So we were trying to tell everybody that if you look at the history that there are customers that in-source, but it is not a good economic decision, and it doesn't happen very often.

I got the message, David.

Okay. So that's -- so that's one thing. Then, okay, once you see that, you see, okay, that may happen from time to time. So Oli went out of his way to say, but there are some customers that really never become big Datadog customers. He says Google, Meta, et cetera. And so we said with this one, everyone is thinking about, we don't know, but they're evidencing that they're making a decision to be with Datadog.

And then, of course, they decided to re-up the contract and extend the commit. And that's evidence of what you see, which is it's not a good decision to -- in these kinds of real-time used to do it yourself. It's very expensive. You generally want to put your R&D efforts on your product. And most companies decide not to do it. I mean we've shown many, many companies that it's economic. So we tried to tell everybody that. So then it happened, and we've been saying things like that, and I guess we proved it.

And David, is it also the case for some of these large AI natives or cloud-native companies to use multiple observability platforms as well. I think you were also messaging.

Definitely.

Calm down, there's probably room for 2 or 3.

Yes. So in large enterprise, in large companies based on use case, there might be multiple observability or pieces of it. And yes, in this situation, I think there's been a lot of noise in the market that other solutions are being used. But that's fine. I mean they may use that for this purpose and that for that purpose.

So I think with everybody saying all of these -- some of these vendors saying that they have that customer, what is being shown by extending that is, yes, we can coexist because most IT departments and most reliability engineers companies decide -- functions decide to do it at a level that's below what we're all thinking about. Like what are you doing with the logs? Are you storing them long term? What logs? Is it IP logs? Is it production logs? So you may have log stores in your -- that you're using in different terms or different purposes that may have nothing to do with Datadog, but they can say they are customers. So that's what happens.

So this is a nice segue to talk a little bit about the competitive environment. Datadog has always had strong rivals. And obviously, you've powered through to the point where you are now. But let's talk about a couple in particular because the sentiment was impacted a couple of weeks back when one of them, Chronosphere was acquired by Palo Alto Networks.

So David, what's your reaction to that deal? Obviously, the Street evidently was a little bit worried. But where was perhaps that worry misplaced?

Yes. Yes. We've had this discussion about Chronosphere for 3 or 4 years. So Chronosphere is their basic product is a large-scale metric store. In terms of if you define an observability platform at what Datadog has, they don't have that. So they have a piece of it. And we've had the same discussion when they were out doing some fundraising, et cetera, they were going, we've taken this business. And it didn't amount to much, right? So they didn't. And I guess we're hearing they have some of the same business that Datadog has. But yes, that's fine.

So basically, for long-term metric store, you may want to have that. We also have a long-term metric store. And we've been investing relentlessly in managing the price points for what you're going to do with it, whether it's real-time, long-term storage without limits, frozen, not, all these terms are about slicing and dicing metrics and logs. So we have that, too. But customers, like you said, they want to have a certain amount of metrics stored in a different way and that's fine. We see that in some customers.

So I think when you're talking about observability platform and what Datadog's business is and the kind of what's been happening with point solutions over time, you see that the balance of trade has been heading towards Datadog. And when you actually look at the size of some of these companies, I mean, we don't work there, but I think we essentially are able to put in terms of ARR, the same amount of ARR that they produce in their lifetime on Datadog, somewhere between a month and 6 months depending upon what the company is. So you see what actually is happening in the market.

David, the other category besides Chronosphere is some open source alternatives. And 1 or 2 have made noise all year and have carved out niches in things like real-time log analytics. So what's your thought on that? And to what extent does Datadog have or will soon have like comparable feature functionality?

Yes. Well, Datadog does. So when you think about logs, real-time logs, the various storage periods, Frozen Logs, Flex Logs, Datadog has successfully gotten ahead of this by slicing and dicing the platform with good margins to be able to have that business and also capture net market share in logs that may not have the same requirements as real time.

So I think ClickHouse is one that's mentioned. And I think Datadog has a very large and growing and successful log business in observability logs, increasingly non-observability logs. So I think we've done many, many of the things in the platform. That doesn't mean that we're going to get 100% of the business, but we've done really well in this area.

Can we talk about pricing for a moment? Obviously, when you're selling a software that, to some extent, is correlated to the client's infrastructure spend, as they scale that, their bill can get high. And it's, I think, incumbent upon software companies like yours to find ways to be flexible such that, that doesn't become a point of friction.

Can you talk a little bit about that journey to arrive at a place where that's less and less a point of friction?

Yes, definitely. So we price based on volume, so the unit price goes down. Now some of you may be wondering, okay, does that mean your margins are compressed or your unit pricing is lower? It isn't because actually, we have smaller customers coming in. So the weighted average does not change substantially.

We also have services. Now this is one of the areas where we've gotten better. We have good transparency of usage. We actually have SKUs and also services that help clients optimize and use the product. The fact that we sell credits or commitments and they can use a variety of products means this is something that I think is much more valuable than it was when the bubble burst, that clients can see where they're using and they can actually move to other products if they're over using. And we also are -- we have always helped our clients, meaning if they have an accident or if they are misusing it and they go over, we actually don't charge it for them, and then we fix it and then if they continue to use it, we do.

So I think we're doing a lot of work to try to help clients. In addition, I think we're expanding, as I mentioned, the value of the platform so that clients are able to spend with a straight face more money with us from their functionality. In addition, I think we've gotten better about saving them money by consolidating point solutions. That includes proving to them that they're better off from dollars, not to have 10 things, but 1. And we're getting better at migration credits to help them through that process.

Got it. You talked a little bit earlier, David, about some of the product suite extensions to drive further revenue growth, which couple stand out as getting closer to being needle moving. You've got many that are early stage like LLM observability. You mentioned a minute ago, Cloud SIEM. Are there 1 or 2 worth highlighting that next year in 2027 could move the needle for you?

Well, definitely, I think the 3 pillars plus the DEM are really good. And in logs, and these are related, the fact that we have observability pipeline that can get access to logs that are not observability, that's interesting. Now that has enabled one of the big potential opportunities, which is Cloud SIEM. So we're starting to be successful within large enterprises of extending or displacing their SIEM for cloud workloads. That is -- we also have a large competitor, Splunk has been acquired. Maybe they did aggregate pricing. And so there's a lot of synergy between our log business and our Cloud SIEM. So that's an opportunity.

Another opportunity that we're seeing is service management. Today, we went GA on our Bits SRE.

Congratulations.

It's now posted. Even though we have a lot of customers using it, and we have some revenues, we hadn't gone GA. So we are $500 per 20 incidents. So that's an opportunity compared with the other parts complemented by the other parts, On-call, et cetera. So that's a nice opportunity.

And then I think we have made some good acquisitions in product analytics, which was Eppo and in data monitoring, which was Metaplane, which are small but are significant growth areas in the future potentially when we look at the market and point solutions that are out there.

Maybe I'll close and we'll leave a couple of minutes for questions with one on margins. David, I know you've got mid-20s long-term margin guidance. You're basically almost there. So when you're thinking about the next couple of years, are there puts and takes that you would remind us to keep in mind as we model out margins?

Yes, it's a good question. We -- so we've been above that 25% for a period, and we said we need to invest more. So I think what we're trying to do as a company is while staying within this band, you can look at our guidance are long term. We're leaning in towards investments that are going to maintain as high as possible long-term growth rate and compound our revenue. So that's what we're trying to do. But we're doing an analytical way. Like we're looking at in product, are we getting good return, meaning are we getting revenues in the $50 million or more? Are we allocating our R&D resources? Are we working on our own optimization and our own platform? And in the go-to-market, are we able to expand the go-to-market yet maintain the CAC returns that are really strong and the attainment?

So right now, we think there's lots of opportunities. We've been able to do both. We've been able to invest as well as do this margin deliverance. And we think that becomes more and more powerful as you get bigger and bigger because you can invest a lot of dollars in R&D. We're by far the leader and still respect the margins and still distance yourself. So we like our position in that way.

Okay. Good. We've got a couple of minutes for questions if anybody in the audience would like to raise their hand. No?

David, any final words about things that are exciting for you as you approach 2026 besides getting your golf score down...

I definitely have to continue to being able to far more regularly. I think that we're doing our planning now. And I think that Oli's always been someone who sort of looked ahead and what you have to do to create the distance and maintain the distance.

So when I think about things like Bits SRE, when I think about what we're doing, having all the integrations with the AI providers and the kinds of information we're delivering about the number of those that are sending the data. When I think about the cohort here as well as the opportunities within Datadog, I think it's -- I think the winners will continue to -- will be the ones that realize the most out of AI in their product. And so that you won't have companies come in and be point solutions that offer something, you'll have that already. And I think he's done a really good job thinking about that.

And so watch out for some of the metrics that we deliver in some of these AI areas as a way of are we increasing market share? Are we winning because of it? Are we getting additional SKUs, additional workloads? And all of those can be ways that we monetize this opportunity.

Yes. That's an exciting story. David, Yuka, thanks so much for coming to our event. Thanks, everybody.

Thanks. Yes. Take care. Thank you.

Thanks, everybody, for coming here, and we're continuing the momentum with Datadog. David Obstler, I've known you for -- when was the IPO?

I was -- I think 5.5 years ago, I've been at Datadog for 7 years. My anniversary was November 1, 2018.

It's -- time flies. Hope you are having fun.

Yes. It's been unbelievable, yes.

Well, I was just talking to Yuka and this last quarter was certainly an inflection from a numbers perspective. And I think the point is you guys -- in some regards, it's just business as usual. You guys continue to do what you're doing. But maybe from your perspective, obviously, the non-AIPs, obviously that and your largest customer were kind of the 2 hallmark parts of the quarter. But just walk us through sort of like the strength in the quarter, and I guess, specifically the non-AI.

Yes, definitely. We can go through this. There's 3 real factors. One, we've been successfully expanding quota capacity to cover more accounts. We've -- that's translated into some successive quarters of more new logos and more dollars of new logos, larger new logos. So this has been building. Next, the platform and the investments in the product and the continued ability to have clients adopt the products. We have a number of metrics we did in our earnings. That is the 3 pillars, plus I think we said digital experience, got over $300 million, and we also talked about some of the successes we're having in Cloud SIEM.

And then the last part is that we don't have the headwind as in weighted average intensity of optimization. That doesn't mean there's no optimization. But when you look across the customer base, we have customers who have done a good job and I think we've helped them in terms of the platform, the product expansion, the way we've dealt with customers so that we have -- we don't have that kind of pushback in optimization that we had. So it's a more conducive environment. That's what's happening and that's been building. This didn't happen like overnight on a certain day. This has been building for a number of quarters. And hopefully, we've communicated that we are seeing those trends over time.

Yes. I mean, I guess I wanted to double click on the non-AI piece, the non-AI customer cohorts. I mean what -- like is there any way to put your finger on what -- and it's probably hard to answer what changed because it doesn't -- the answer is like it's hard to say. But like I think a lot of us felt the AI natives cohort was leading the way towards implying that as customers adopt more AI, more workload, more monitoring, is that -- are you seeing this as the sustainability of this non-AI piece is like the flip is switched that more AI workloads are going to production from this non-AI native cohort because it just -- it feels like there could be some durability to that element?

Yes. We think so, but I think it's a boring answer. But remember, a very large percentage, 70%, 75% of workloads are legacy workloads that haven't been modernized, rewritten and they're on-premise or legacy. So this probably AI is causing customers to focus on the fact that if you want to get utility out of applications, you have to work on rewriting them. We also believe that some of the coding tools and other AI is going to accelerate the creation of new applications. And I think that's what we're seeing. We're seeing more companies focus on the modernization of their tech stack. And as our platform has increased in sort of value, we're seeing our market share increase in the amount of ARR that's being put on our platform relative to other choices.

So the answer is it's not one thing. It could be -- trying to think differently about on-premise workload, it could certainly be AI workload, it could be Cloud SIEM. There's a variety of things there.

So in the portfolio, when you get to our position, it's -- the performance in the quarter is sort of like you have a number of different opportunities and the more of them that are going in a certain direction, they're going to add up to this because we're large enough geographically, diversified enough geographically by customer and all of that and product that you basically have a lot of efforts. And if they are heading in the same direction, they amplify themselves and produce what happened.

Yes. We don't have the answers of -- one of the questions that we get about AI is in an AI bubble, and that's not for us to sort of like think through. But like when you -- how important is AI workload growth within that non-AI cohort? I mean are we at an inflection point where like banks, insurance companies are all of a sudden like -- the answer, like I know there's more than just that driving results. Are we at a tipping point now where AI on the broader economy is starting to...

I don't think we're in a position to call that. We're sort of -- as you know, we're a follower, meaning we set ourselves up to monitor workloads. So we're setting ourselves to monitor workloads whatever they are. What we can control, you know from -- we can talk about it more. We're putting more AI in our platform, but it's still a small percentage of the value that we're able -- that we're conveying to customers. So I think we're still early on. I don't think this is a -- in the application of software, I don't think this is like we said, like inflection flip the switch. I think it's part of the overall modernization of the tech stack and a positive effect of that on that.

Okay. And then maybe just one last point here. You said that you felt the momentum was building. You've seen some of the early indicators previously that some of the stuff was coming around the concept of the durability of this. And are some of these trends that you're seeing now -- I mean, obviously, the macro can change in the future, but the durability of some of these trends that you're seeing...

Yes. The forward-looking things that we've communicated, one, we saw a persistence and continuation of the trends into October. We also said we have a very large and good pipeline in the fourth quarter and going into early next year. So that's what we can see. So again, we're not in a position to call durability or economic cycles, but it looks like things are continuing.

The other -- obviously, you guys renewed and expanded your largest AI customer, which was a huge talking point for a lot of investors. But what was really surprising, too, was the growth in all of the rest of the AI natives. I mean you're seeing some significant progress there with 6-figure, 7-figure customers. What are -- could you pinpoint what those customers are using you guys for like specifically? Is it monitoring known workloads, customer spacing side? Like what are...

Yes, exactly. Customers, same thing. So essentially, we've all created this AI native and non-AI native, but really, they're called modern software companies. So what do they have? They don't have a legacy infrastructure. They don't have on-premise legacy. Everything they're about their business model is a modern digital application. They're experiencing a rapid demand cycle. And like all of our other customers, we're monitoring their workloads in their client-facing applications.

So what you'd see there would be metrics, traces, logs, so infrastructure, APM, logs, and then you would see digital experience, which we said we've got over $300 million, and you would see database monitoring and some security. So essentially, the difference -- the only difference here would be you have, in some cases, a faster ramp to significant scaled revenue than you might have seen in other software cycles.

And they've been releasing their revenue numbers and their funding, but also their revenues. So that would be a good indication. If they're getting a lot more revenues, it's very likely they have more workloads. And if they have very -- more workloads and Datadog is the choice for these, that Datadog's revenues would be growing. That's our model. Nothing different.

One of the narratives really for the past couple of years is could AI kill software. And you guys certainly from a pricing perspective, are immune to seat-based pressure. When you look at how AI is impacting the infrastructure stack and when you think about open source technologies and things like that, I mean, how do you see AI evolving how customers think about the infrastructure or the monitoring layer in the future? And -- because there's -- you're unique and that there's no seat-based issue that you guys have...

I mean, it's also we're probably in a good position that in our infrastructure. So essentially, it really -- as long as we invest in our R&D cycle, as long as we -- and we set ourselves up to monitor whatever it is, that's what we've always done, whether it's serverless or containers or anything. So basically, we -- as long as software is going to be delivered, if it's more gentic, if it's got -- whatever it is, we're setting ourselves up to be able to meter and monitor that. And that's a good position to be in because essentially, we're not seat based, but we're also agnostic to what's being -- what's in the software. We're just going to make sure that we're able to monitor whatever the composition of that software is.

Yes. And then I guess the -- with open source alternatives or DIY, from a customer lens perspective, there's always -- and forgetting your largest customer, but what are you hearing from customers in terms of the criticality of like, oh, yes, sure, I can go do this in an open source model here or there, but like is it really -- why bother...

No, they don't want to do it. I mean that's how we got to our size. That's why we're creating versus the -- we're creating like there's other companies, we're creating those other companies in every month or quarter because they don't want to. There's tremendous value in having everything in a single pane of glass and a platform and it costs a lot less. And you're able to get the best-in-class and you don't have to put your resources that you're doing in your own business. So that's why we don't -- that's why there isn't much in-sourcing. That's why -- sorry, the outsourcing has been the trend, which is one of the things that's made Datadog. With a few exceptions, that's really what is driving the market. I don't -- it's not -- it's been moving since we went public more towards a purchase of a service or a software than in-sourcing.

Yes. And then maybe just one other kind of AI question. You sort of alluded that the AI native disclosure served the purpose. No how do you guys gave it. I forget how long ago it was first disclosed, but effectively implying that are we nearing the end of that is because you think about just the broader economy now. Is that something that we should kind of be prepared to...

I mean, we're saying -- like I said, it's a made-up thing because it's a software company. So it was done, one, to show that -- to talk about our growth drivers. In other words, to talk about our acceleration, you'd have to talk about the growth of the AI native companies. We would say that anyway, right? That's one of the growth. And then there was the factor of the largest company that we wanted to make sure we helped everybody along.

So as everything evolved, we could have a discussion about traditional enterprise, AI native and the larger company. Eventually, we believe that all of this is going to be part of the overall software cycle, and it won't be as meaningful. And so we haven't given when we stop -- we're going to stop disclosing it. We're trying to help everybody understand the drivers of the business. And so that's the utility of it, and we'll look at that in future quarters and see if there's utility.

Okay. The other thing that struck us is the acceleration in your security business, which is -- it was a long time coming as well. Can you talk -- the question is like why now? And I'm sure the answer is it's been building. But can you help us think through some of the why now piece?

Yes. I think most of the original, and it's not surprising security revenues were created in SMBs or smaller growth companies in DevSecOps where essentially security was much closer to development. And that's what we did, and that makes sense because as you're developing your product, and that's where one of our core strengths are. But I think we really didn't have the enterprise selling motion. But what happened was we got in Cloud SIEM, we got capability. We got parity and capabilities. A lot of that had to do with the work in taking logs and making sure logs could be used economically for various use cases.

We already had done an unbelievable job in observability logs. And observability logs are very close to the real-time production environment. So what we did was with Flex Logs and Frozen Logs and all that, we were able to modify the product. And then we basically saw that in the competitive environment, someone like Splunk, and this has been from a number of different perspectives, there are a number of companies was -- essentially, there was a whole set of workloads where you could get better visibility at a lower cost from a Datadog product.

And then we got, I think, better over time about selling in enterprises and attaching to our logs customers. And that includes essentially program management. We've been better about buyouts in exchange for long-term contracts which hasn't changed our profitability. It's just spreading the discount in a different way. And then there's a lot of other things like migration or from Splunk query language that we had to conquer.

And I think we just got better programmatically. We're not there yet. We still have a lot more things to do. For instance, we set up a channel motion. That takes a long time. And so a lot of what's being created is direct. Some of it is starting to come through channel, but there's a lot more things that we have -- that we're going to be doing or we've done that have a longer investment cycle. So that's kind of what happened in security.

And SIEM seems to be a big part of that. But when Oli and the team think about the white space, the shift left mentality, DevSecOps, broadly speaking, what sort of excites the team about future opportunities in cyber?

Yes. I mean I think you have code security as well where I think we're starting to do well, looking at the security in the code as it gets put in production. I'd say that has tended to be a smaller market than the other 2 than Cloud SIEM and Cloud Security. Usually, I would say, developer-led market where you start -- you go to the developer, most of what we sell is into the production and the SRE. You just have -- you have a little bit of a different TAM size, but I think we're going to be good at it, and we're getting fully capability there. So I think that's another opportunity for us.

The thing you guys talked about it, you launched it earlier this year, and you talked about a little bit on this last earnings call, but Bits AI seems to be resonating. Talk about that opportunity? How additive is it to a customer spend? And sort of like where do you see that driving additional customer value?

So Datadog has always been an aggregator of data, the organizer, you see the picture. It is very good at diagnosing where problems are. And we've increasingly moved into what are you going to do about that? How are you going to route cases or make recommendations? And I think AI is a great -- has been and is a great opportunity for us to sort of revolutionize that. So that's part of like the service management suite where you can diagnose problems more quickly and then have -- and then route them for resolution.

And right now, we're still in private beta. I think we haven't said to everybody where we -- when we're going to be in GA. But we are getting a lot of pull-through, meaning we're getting a lot of customers who are using it. This is how we launch products. And we're getting a lot of excitement about it. And we think -- and their customers is paying for it. And so we think it's a good opportunity. We don't know yet whether it's going to be monetized through increase of win rates, staving off other entrants to the market, increased workloads or through a SKU, but we know it's important to the economic -- the economics of the company.

Yes, that's great. The new engine flywheel of products has been really a hallmark of the company. And I guess the other thing that stood out in addition to some of the broader acceleration that we talked about was new logos. Logo adds were up dramatically. I mean this, I assume it's a similar answer. You've seen building momentum here. Could you put your finger on like why now and sort of like -- because obviously, there's a lot of turmoil out there...

Definitely a combo of successfully ramping quota capacity. So we've been able to maintain productivity, maintain strong CAC return and ramp. And I think we're landing larger. So it's also a size because of the platform. So we're basically going into places where I think we're better at and we're getting more lands that are larger. Remember, we used to say that, we don't try for large lands, we're bottoms up. Well, we are bottoms up, but we also are increasingly getting more of a top-down and more of a here's the whole platform or more of the platform. So it's that combination, I would say, capacity and platform adoption that is causing that, which are good things because those are things that can be sustainable.

Yes. Yes. The -- I wanted to -- I guess, sort of in the same lens, you guys have been such an organic engine over time, and you've made some smart tuck-in deals. But I think you've got about $4 billion in cash now. How do you think about, broadly speaking, deploying that? I mean, could -- as you guys get bigger, could the size of M&A deals increase? And how do you kind of think about how M&A is part of the kind of the broader R&D?

Yes. I mean at the heart, M&A for us is a part of product management. It's taking a look at the products we want to create in the platform and/or the talent needed to create it. And for the most part, we've acquired teams that have some commercial adoption, but aren't going to have the platform or the go-to-market machine that we have. And that has been the majority. That could get larger. It could be that companies that are a little farther along. We're open to larger acquisitions.

I think we answered some rumors by saying we're not working on anything major. The bar for anything major is really high because we essentially -- we're not a consolidator. We don't take companies that are lagging in technology. We don't want to do that. It basically takes us off what we're concentrating on. So we're open. So I think that amount of money is good to have, but it's not that much money that you can't do a couple of larger deals and not use parts of it. So -- but I think for the most part, the center of our acquisitions, whether it be slightly larger companies or larger companies is really about that product map. It's really led by product management.

And I assume part of the high bar means deeply and natively integrated into the platform. You don't want to necessarily have a Frankenstein model where there's different bits hanging off the side...

Tech leadership, so not decaying modern software. Consistent with Datadog, here's another one that's kind of hard, a team that wants to stay and be part of the leadership of Datadog. So that is a really important one. We're not interested in acquiring a company to make the founders rich and go away. We're interested in them being part of Datadog. That's not a bar.

In terms of U.S. Fed, it could certainly be more for you guys. How do you think -- when you think about FedRAMP, elevated levels there, how should we think about that as a contributor to kind of the future growth algorithm?

It's small right now. I think we get asked sometimes the government shutdown now or non-shutdown, it doesn't really affect us. I mean we don't have that much government. So it's an opportunity. It's an opportunity that I think we're going for FedRAMP high. I think that will enable us to go into Department of Defense and others. The government does not move particularly fast. And when you call -- for Datadog, you need modernized software stack and cloud. So that's slow. I think it's a long-term opportunity and one that will be -- augment our business. We're certainly under punching our weight. It has a lot to do, I think, mainly with the end market. But -- and we're investing behind it. We think eventually, the government will modernize more. We want to be there when it does with the right capabilities.

Yes. If you've been to the post office or driver's license, it certainly could use...

Could use. Yes. I mean, essentially, if there's -- we want to have it set up so that we can handle the most important government workloads, including defense, where a lot of dollars, where the budget is going and then we have to follow the trends of what they're doing in their tech stack. Yes, so that's it. So I think it's an opportunity that's small for us and could be a lot bigger, and we think it will be a lot bigger, but it takes time.

Yes. When we think about going into Q4, you guys are such a predictable subscription model that you don't -- budget flushes don't really impact you. When you think about how customers are thinking through kind of like AI budgets, I think one of the prior concerns was, is there just confusion around how customers are thinking about AI dollars and deployment. Do you get a sense that the customers are more comfortable now in kind of that AI spend envelope and that could help open up opportunities for increased use of Datadog in the future?

Yes. I mean I think we said our pipelines are strong, and they're diversified across a number of very interesting and traditional industries. Yes, I mean I think that it will. I think that there's -- when you're talking about a business and an application, it's not all -- it's not like 10 -- like no one -- it's not like everyone is waiting for whatever the AI thing is. They're still doing their business. AI will be part of the application increasingly. But I think we're communicating that we're in a good buying environment, and it's due to a number of factors.

Yes. I'm going to ask you one more and then we'll see if -- this is a big group, we'll see if there's a question out there. In terms of -- you and I have had this conversation about pricing and packaging. And I think you feel that you guys have the right framework to get you to multiple billions of dollars more than you are now. And obviously, there was some -- talk about your largest customer and some -- maybe some bespoke things going on there. But do you think as you get these larger customers over time, that the pricing and packaging has to evolve for a tier of customers that are growing at a rapid pace and consuming a lot more of the product? Or do you think, no, like this is the model that will get us to customer spending...

I think broadly speaking, it's the right package. It's volume and term discounts. I think there will -- there may be in the future, and I think we said it, different types of workloads, whether it be, let's say, on-premise workloads or that need to be priced differently. And I think we said we're working on it. And I think when you think about Frozen Logs, Flex Logs, log without limits, metrics, I think this is all what you're saying, which is it's all evolving the tech stack to be able to do differentiated pricing to address this.

And I think that's what's been most important. And so we think broadly, but we're working -- we work on it all the time. We have new SKUs. We have new -- we divide those SKUs. We have cost of delivery, if you don't need the logs, it's a different price. So we're pretty disciplined on looking at the gross margin and making sure if we can lower the cost, if it's not growth pricing, if you don't need the logs that -- and we don't have to retrieve them or start whatever, then it's a different price. So we're working on that.

Yes. Great. Is there -- we may have time for one out here. If there's -- anybody has a question for David? Otherwise, we can keep rolling. We'll scan, scan, scan for logs.

We're being thorough here. Yes. When you think about some of the building blocks for next year, and when you think about growth and profitability, obviously, you've done a great job balancing both as the company has scaled. What are some of the things that you would sort of like -- when you think about some of the big moving pieces without -- you're not going to guide, I understand that. But like when we -- how should we think about the evolution of this model as you continue to scale and profitability matters, but also you're seeing growth inflect at the same time?

Definitely. So it's bottoms up. So in terms of go-to-market investment, we're looking at territories and target accounts, and we're looking at accounts that could buy Datadog. And we're sort of making bottoms-up investment. We're still -- I would say, we're not saturated. We still have lots of accounts that could use additional coverage. We're looking at the go-to-market in a holistic way, whether that be enterprise marketing or channel investment and things like that.

Looking at data centers, and that would be, do we have enough of a TAM in a market that needs data residency. And so we look at that. And then when it gets to R&D, we're looking at what the platform investments and product investments are and how we have to sort of staff them. So that's how we're sort of balancing all that. I think as to remind everybody, we've given a long-term EBIT target of 25%.

As you know, we've been past it. We've been under it. But essentially, we've been in the low 20s to the mid-20s overall. We use that discipline to help us to calibrate the level of investment. It is definitely easier as you get scale. You can put a lot of money to work productively because, for instance, in R&D, 30% of our ARR is over $1 billion, a lot of money. And so I think it becomes increasingly -- the dollars are there and the impetus is increasingly on what to put them behind.

Yes. Maybe then just to wrap, when you and Oli and the executive team sit around and you think about some of these -- the big, big opportunities for Datadog, where you sit here and say, this is a moonshot for Datadog. I mean do you have any sort of like bold like -- not financially, but just like longer-term predictions on while this is...

Yes, our vision is to have a platform that addresses our core user group and all of those around it with increasing functionality. I think service management is an example of that. So you just don't -- you actually move towards resolution in the platform. I think security is an example of that where you get additional buyer groups or user groups so that you do what we've been doing all along, which is you expand the TAM by having more and more parties in the platform, using the platform and creating value. That -- I think service management, security, the AI, the bits AI, making the platform AI and covering AI workloads are probably at the top of the list and maybe I said security and class, a top of list of what could be breakout opportunities for us.

Cool. Well, out of time. Look forward to you hear from you. Thank you, David from all of us.

Thank you, everybody. Thanks for coming. Really appreciate it.

Thank you.

Thanks.

Thanks.