All right. Good morning. Day 2 morning sessions at Morgan Stanley Technology, Media & Telecom 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. Yeah. Thanks for having us back. Awesome. Yeah. Lots to talk about. Business is doing well. We gotta talk about, of course, AI and all sorts of other topics. Before I get there, for important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. 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. You're delivering operating margins of 22%, and you're now serving 32,000+ customers. We're at an interesting time in the market, particularly with software, and there's a kind of return to first principles thinking and assessing how software providers create value. 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? Big question. Datadog helps companies migrate their mission-critical applications, usually customer-facing, to the cloud or manage them in the cloud. Our legacy has been in observability, which has really been around infrastructure, APM, logs, and Digital Experience. As we've expanded the product set over the years, we've 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 with the environment. 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 one. 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 re-accelerating. It's accelerated for two quarters in a row. It grew 18% in Q2, 20% in Q3, accelerated again in Q4. What are the factors driving the re-acceleration, how durable does this rejuvenation and growth- Yeah feel to you in the context of the current demand environment? Yeah. There've been a number of factors. This is the business in companies that we're not calling AI natives. First of all, we're very early in the migration of applications into the cloud and in the modernization of applications and infrastructure. What we've seen is a good buying environment, meaning corporations down to SMB have turned back towards moving applications into the cloud. That is likely, we've said and we believe to be accelerated and then complemented over time by the replatforming that's going to be caused by increased complexity because of AI. We have a good buying environment. The second thing is that we've expanded our platform substantially over time. We have a lot of new products, a lot of new products that are getting to and achieving scale. We have a broader value proposition to sell. I would say the third thing is that we've been winning market share and consolidating. 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. I think the last thing in our hands is that we've expanded our go-to-market capabilities. Mm-hmm. We've 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. That's something very much in our hands, we've been investing for the last couple years, that has been paying dividends helping to accelerate that business. Yeah. That point around consolidation is, I think an important one because you have to have the product portfolio to do that, right? Mm-hmm. There, there is a lot of players in this space, but probably only a handful that can actually go to a customer and say, "We can-. Yeah ... consolidate 10 or a dozen of these capabilities onto the Datadog platform. Yeah. I think we 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. Mm-hmm. Once a customer standardizes on Datadog, their spend accelerates. We've done a lot. As you say, we have been consolidating, and we do have that product set, but there's so much more to go. Just maybe stick on that topic. I was gonna ask this question a little bit later. On the customers, you know, roughly half of the business- Mm-hmm ... that's not on all three 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. There were APM log products, Digital Experience, Database Monitoring out there. I think the two 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. Mm-hmm. There are install bases, there are champions, et cetera. 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. You know, we're seeing that. I You know, I think we said over the years that in our larger deals, somewhere ± a half of those largest deals have consolidation in it. You know, we've been talking about the core business trends and the-. Mm-hmm. -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 natives. Yeah. You have 19 of them spending in excess of $1 million annually, and I think roughly about 650 AI native customers overall. What has the company been doing to penetrate this segment of the market, and how do you think this cohort performs going into. Mm-hmm. into fiscal year 2026? Yeah, great question. This is an ideal customer base for us like cloud natives. They don't have legacy infrastructure apps. Mm. They are modern cloud companies. Datadog, as you know, designed its product to be optimal and to be, you know, a product that handles many of the needs of this type of customer. 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. You know, I think we have a natural product fit. As you can say, we've been comprehensively winning. 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. What we're finding is, you know, they're deciding that it makes so much more sense, given their huge investment in their own products to buy Datadog. Mm. I would say that, you know, we're winning and we're also, you know, all the type of developer marketing. Mm-hmm. Types of things we do hit very well with that constituency, and we're continuing with that. You know, on the last earnings call, you announced an eight-figure land with another leading model provider. There's a debate in the market that these types of customers want to build their own. Mm-hmm. infrastructure and tooling. What were reasons that this customer chose to go with Datadog for its observability needs? There's a debate in the market, the evidence is quite the opposite. In fact, when you look at how Datadog has grown and the market share it's 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 know, you get the best of breed. I think that's a good example of a customer that early on, experimented in trying to do it themselves. Mm-hmm. 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. When you really look at it, and you look at Datadog's gross retention, which very upper nineties, and we showed this in the Investor Day. Mm. What you'll see is it's very much fringe cases that decide to do it themselves. 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. I think the weight of the market has been to buy the Datadog platform for lots of reasons, including efficiency, efficacy, return on investment, cost, et cetera. Awesome. Let's talk a little bit about, you guys had reported, your Q4 results and guided for 2026... Mm-hmm. the other week. 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%. Mm-hmm. Given that the core business grew 23% in Q4. Yeah. What gives you the confidence that growth excluding your largest AI customer will prove durable at current levels? Yeah. Great question. There's two different questions. What do we guide to and what do we see? Yeah. 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. Generally the business is, you know, predictable in the shorter term when you look at the sort of the trends. 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. All of those trends compound on themselves and give us the confidence. Then we put conservativism on it. We take those growth trends, and we discount them in order to provide, you know, the cushion in our guidance. Pretty much, that type of accelerating performance translating into guidance is due to what we're seeing. Mm-hmm. repeating the three or four factors that I mentioned up front. Yeah. No, It's been a methodology that you've had in place. Yeah. -for years when it comes to the guidance. Let's talk. You brought the point on new logo lands and the deal sizes getting better. I mean, one of the things that I found interesting about the Investor 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 forces that's driving the big increase in the overall land size? Yeah, definitely. I think we're, our product suite is broader. That means we are landing and expanding faster in more products. We have, we're still land and expand. We're still, you know, get in there very long. We showed on the Investor Day, very long-growing cohorts. 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. 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. Those are all the things that, product side and execution side that have created that broader enterprise selling. Yeah, very impressive to see. Thanks. When this year started, I had investors reach out to me. He's like, "Sanjit, congratulations. You don't cover seat-based models, right? You cover the part of software that's insulated from... Yeah. AI risk and those types of things. I have to say, in the last couple of weeks, everything's getting questioned, including, the names that I cover as well across software and across sectors. Yeah. I want to spend a couple of minutes, you know, talking through, you know, the debate in terms of defensibility against potential AI disintermediation. 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? Mm-hmm. When agents are doing the investigating and triaging of incidents versus human, DevOps or Site Reliability Engineers? Is an observability solution that has a dashboard as its main user interface is relevant in an agentic world? Mm-hmm. That's a big question. There are lots of things, but I wanna say something up front. Mm-hmm. There's seat-based, I think, which is an important thing, but there's also the word infrastructure. Yeah. When you're infrastructure and you see that's related to seat-based, we're monetizing based on the agents or the, the containers or the servers. We're also monetizing based on how it's used. What are we doing? We're monitoring infrastructure. 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. 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 if 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. The delivery. 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, we're calling it Service Management or closing the loop. You have what to do about that. Mm-hmm. When it comes to foundational models or access to data, we're either integrating or, and I think we demonstrated at 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. I think that there's lots of reasons. Seat base is important. Mm-hmm. Congratulations, you're covering. 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. Yeah, that's an important point. The other angle on, you know, the risk to observability players, including Datadog, is the potential for customers to combine open source tooling and manage your metrics, traces, and logs, combine that with agents from like the model providers to reason over data and execute the incident response. What's the company's argument for why this line of thinking is off base? Yeah. I think that we would view when you think we had the same discussion about OTel. Mm-hmm. 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 gonna have and is investing significantly in having access to all the data. It's always been the case, whether it's OTel or direct integrations, et cetera, that what the magic is what happens after that. I think, you know, and we talked, we can talk about AI for Datadog and Datadog for AI 'cause there's a whole another set of things about AI-ness of the platform. In terms of, access to the data, you know, I think if you look at our investor presentation, we're doing it. Okay, why come to Datadog? Why continue? We believe that platforms like Datadog have 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. Yeah, it's important. This is 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, sorry, just to pick 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 the huge datasets you have access to when thinking about competition versus other AI natives or the research labs? 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? Yeah. We do believe that'll 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 datasets will be part of the equation that delivers most value and at a good price. Mm-hmm. When you're talking about generalist models and foundational models, you know, 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. Mm-hmm. 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 about the momentum that you're seeing with Bits AI SRE agents? Yeah. What's been the customer feedback with regards to its accuracy, reliability, and speed in terms of finding the root cause to incidents? It's been great. Look at the analyst presentation for some truly impressive quotes. We have, we just put it in GA. We have a 1,000-plus customers using it. We have ARR where it's being paid for. We have a pricing model on the site. The initial reception has been very strong. Of course, like all of Datadog's products, we launch them, we get feedback, and we continue to improve them. Mm-hmm. I think we're gonna, you know, we're still early stages, but getting great feedback in terms of the fact that we're on the right track. Mm-hmm customers are finding value, and we're getting the feedback to be able to handle more and more use cases. Yeah. Yeah. Continue, sorry. Yeah. I'm saying we're gonna do that. The other SR, Bits products around security and around development, I would say they're a little earlier in the cycle. We plan to do the exact same thing. Mm-hmm ... which is have, co-development partners figure out the uses, get them in GA and then learn. 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 in Datadog. Investors come to me 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. I think the implication that they're getting at here is that observability can be replicated with the help of AI and coding agents. Mm-hmm. I've heard you guys in the past mention that 50% of your engineers- Mm-hmm ... work on the core platform itself. Yeah ... 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... Yeah ... building the core platform. I'd love to get your perspective on why Datadog will be hard to replicate. Well, a lot more than that because, you know, we're spending over $1 billion a year, and we said 50%. Mm-hmm. I think there are a number of things. One, scale, how to handle the data. When you think about, when you think about ingesting infrastructure data and creating metrics, you know, 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, you know, all sorts of things, all correlated. 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- Mm-hmm ... much faster, much more cheaply, and in a more orchestrated way than competitors. If agents are important in doing that, they'll be in our platform. I think the platform itself has been it's kind of a two-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. 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, you know, between the third and fourth quarter. The spans that are sent from LLMs 10 times over the last six months. 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. Yeah. You guys built multiple billion-dollar-plus businesses. Yep. -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 to, I think we said infrastructure had gotten to 1.6, logs over $1 billion. Mm-hmm. APM and digital experience, over $1 billion. 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, you know, data and infrastructure company, which has created this competitive advantage and the ability to develop very quickly. Awesome. Yeah. Let's talk about some of the opportunities to unlock growth further. Mm-hmm. across the business. Let's get an update on the security business. Yeah. Security seemed like it shifted to a higher gear in 2025. Mm-hmm. Going back to the Investor Day. Yeah. one of the data points that you guys put out there was 70% of your $1 million customers. Yeah. Are using one or more security products. Right. In terms of the ARR, it's still relatively modest. What are the initiatives to improve the security adoption with your largest 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. 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 our 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 particularly, but also more broadly, onto some of our sophisticated large enterprise customers. Mm-hmm. 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. 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, focus on the Cloud SIEM work knowledge and functionality. Mm-hmm. Start to penetrate some very significant customers. You know, guys like me have been asking for several years when it comes to the security strategy, like, is Datadog gonna build a specialist security sales force? Mm-hmm. It sounds like you guys finally made that decision to pull the trigger on that. The question is, like, why now in terms of deploying that specialist security sales teams, and what do you hope- Yeah. the impact will be in sort of year one of 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. I think the reason that we didn't do it up front is in order to win the hearts and minds of channel partners and, you know, have the right motion, you have to have a competitive product. Mm-hmm. That's equal or better in functionality. It probably doesn't make much sense to have all that distribution if you're not gonna be at that level. 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. 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. 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 gonna help accelerate by having, you know, quota-bearing either channel partners or salespeople who do nothing else. It's early, and we'll report on it, but we're optimistic that it'll help accelerate. I guess the signal here is that you guys are confident now, more confident in the actual product capabilities. Definitely. Because you're more confident now, you feel more motivated to engage the ecosystem and the channel. Yeah. To win the channel partners and to get them recommending, you have to have a number of things going on, including the product. Mm-hmm. I think what we said is we're ready for that, and that's why we did it last year and are gonna 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- Mm-hmm. Datadog, in terms of helping customers with their AI initiatives. Mm-hmm. LLM Observability, is a product I think you guys have had out for a little over a year, maybe a little longer than that. Do you feel that this product has found a product market fit? What are some of the recent patterns on usage? Yeah, definitely. Yes. This is essentially dependent upon our customers having LLMs that are in their production environments. You know, 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 metrics in that we have over 1,000 users, and our spans sent to us in the last 6 months have expanded by 10 times. 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. 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. Yeah. It's great insight. Yeah. Another area, one that I'm particularly interested in because we sort of live in a GPU economy- Yeah. We've talked about a product for GPU monitoring that's in preview now. Mm-hmm. Yeah. Why the decision to enter this category? What types of customers would you think will be most interested in this offering, and what do you think pricing will look like? Yeah, we've had it, we've had the ability, but I think we understand that the GPU is larger than the CPU and requires. I think this will be, you know, as our clients, broadly speaking, not just the AI natives, but as enterprise customers or cloud natives start to create their own models and integrate it into their own production environments, they'll be using more GPUs. Just like all the other product areas, we wanna be there to be able to monitor it. Right now we have some use. What we wanna do is increase the functionality, optimize the pricing, same way we do always. Mm. 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. Yeah. That's true. ... in the infrastructure providers and the model. We don't think that'll be the- Mm-hmm ... the, you know, the state of play of the market down the road. Expect some progress. Yeah. That makes total sense. I wanted to wrap up the. Yeah conversation just on some of the consolidation that's going on in the market. Mm-hmm. You've seen some large security vendors acquire into observability. Yeah. You've seen data platform providers acquire into observability. Uh-huh. The question here is, the assets have been acquired, can they pull off those consolidation deals that you and maybe a handful of others... Yeah ... can do in the market? Or what's your assessment of the breadth of the- Yeah capabilities? Well, this is not brand new. This has been going on for a while. Mm-hmm. You know, since we've been public, you know, we've discussed it. Mm. There've been a number of companies in security or in automation, AI, in IT automation that have tried to do this and, you know, and they basically have not succeeded. I think it depends on what you're acquiring. So far, most of what has been acquired are point solutions. Mm that don't offer the observability platform. Mm-hmm. 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. Mm. They'd have to do that. They don't have that. Just like our challenge in go-to-market and security, they would have a challenge in sort of the bottoms-up selling into DevOps. I think it's a tall order, and most of the companies that have been acquired are point solutions that may have use cases, but really haven't been competitive in the broad observability platform. Maybe one last thing I'd love you to comment on as respect to the competitive element. Yeah. What do you think will make Datadog the winner versus, you know, the security players who also have their agents deployed in the customer environment just like Datadog does? Yeah. I think it's essentially having the data and the integrations and the user interface- Mm ... that are optimized for those use cases that are, we've learned, we've just talked about, are different than security. Mm-hmm. I think it's the same thing on the other side. Mm. Whether it be agents or whether it be developers or whatever the combination, these raw materials have to be integrated, and with the data. I think it's gonna be a lift for those others to do it. It certainly, they've been there trying to do it for 20 years. Mm. 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. No problem. kudos on that. Yeah. Thank you for giving us the update on Datadog. Really appreciate it. Thanks for having me. All right. Appreciate it. Thank you. Thank you.