Datadog, Inc. (DDOG) Earnings Call Transcript & Summary

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.