Datadog Investor Day 2026

Good afternoon everyone. My name is Yuka Broadick 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 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.datadog.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-founders, CEO Olivier Pomel and CTO Alexis Le-Quoc, followed by product leaders Eric Garnier, Tim Kudson, Michael Wedon, and our Chief Product Officer, Yan Bingle. We will follow that with a 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 for another Q&A session. With that, let me turn it over to Olivier to kick things off.

Thanks, Olivier. Hi everyone. My name is Alexis. I'm the CTO and one of the co-founders. So I think Olivier gave you a clear picture of the dizzying 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 Datadog 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 sending to us about their systems. This could be 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 Datadog 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, gives us an edge. And thus was born our first foundational time-series model named Toto. Now if you look for the largest public dataset of time-series data, you get to about 300 billion data points, and it covers domains that you'd expect — finance, healthcare, energy, transportation, some web traffic, and so on. But we trained Toto on three times that amount. And the vast majority of the data we have, we use 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 models and its ability to forecast, it performed far better than other models out there. So we reached state-of-the-art. And from there, what we did is we released it as an open-weights model on Hugging Face. 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 weights — what's the point? Well, for three 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 in 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 three to four orders of magnitude cheaper than a frontier model of the same vintage. Now sure, frontier models can do a lot more than what Toto can do — it can speak hundreds of human languages, it can review and amend legal contracts or analyze medical imagery. But none of that matters in the context of observability. And with Toto, 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'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 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 agent. And if you're not familiar with Bits, 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 with an application, for instance, Bits will develop a hypothesis about where the problem is coming from and analyzes all the data available, and the goal is to identify root cause that can then be addressed so that the symptom goes away. And as you can imagine, and you'll hear more about Bits, 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 city around us — the streets need to be plowed, the city needs constant attention and effort to fight entropy so that life can keep going. So in the course of any day, our engineers investigate and fix issues. And what they do too is they record their findings as well as the entire set of observability that was needed to reach a conclusion. And they turn that package into an evaluation, or an eval. And 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 model instruction, we run through that growing body of evals and see if the model gets better or worse. So this is a chart behind me showing the number of evals our engineers have recorded over time. It is not something that can be outsourced to non-experts 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 had to keep up a small village, for instance. And so as we recorded evals, we've seen the accuracy of Bits 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. And not only that — we're going beyond this sort of human-curated set of evals. We also use harnesses 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, they've sent us feedback — this is useful, not useful; this has worked, not worked; and here's why it was useful and why it was not useful. And we of course use that as eval, and that's great because it continues to enrich the diversity of environments and problems that our agent faces. And the reality is there's no shortcut in training — in 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 fine-tuning of models and agents. Number two, we are building our own models. Number three, it's not only the sheer amount of data that matters, it's also the diversity — it has to come from a broad context, that's very helpful. And number four, 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 develop the best observability powered by AI, bar none. Now you may wonder, okay, that's great, but why not just take the data and throw it at a frontier model and see how it does? And thus you would replace Datadog with just a bunch of data and a frontier model. And maybe you have the data to do that, but first of all, is it enough? And what you'll get is — so we've done that internally, our customers probably do that — what you can do is, the frontier model is going to be great at summarizing data. You have to give it a bunch of context, you can get some good results. The main problem is it's extremely expensive. So here's the idea here — I'm plotting cost and accuracy if you will. And first of all, training a frontier model starts at a billion, and it's unclear how high or how the accuracy slope trends as you go orders of magnitude past a billion. Whereas our research is telling us a different approach works, and we think it's better. We've built much smaller models on the exabytes 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 if you hire lots of engineers to fine-tune and do RLHF and so on, it's not going to be able to match the accuracy at the same cost, because really when you use a frontier model, the reality is you're paying for the amortized cost of the pre-training, the training, and on top of that, all the vast quantity of hardware that's needed to run inference of a frontier model. So in summary: one, we believe that autonomous operations need very good models at a low cost; and number two, our approach with small dedicated models, lots of real training data, and ongoing evals, we think is the way to go.

So I've shown you the reason why we build our own 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 systems. Now we are already in a position to observe these systems, because we're plugged in everywhere — we get 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, and 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 systems are built. Without it, it's not going to work. And finally, without high accuracy, there won't be autonomy, so we have to get to high accuracy. And when we think about steps towards autonomy, we think in terms of stages, here represented on the right-hand side. Start with customized and adaptive observability, then proactive alerting, automated remediation, and then you get to predictive and preemptive, and finally you get to autonomous operation. And with Bits, 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 alerting and automated remediation with the goal to take care of an increasing number of cases where currently people are in 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 applications 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 hear about what we're delivering for customers today. And for that, I'll hand it over to Eric.

Thanks, Alexis. And hi everyone. So my name is Eric Garnier, and super excited to be here today for my second Investor Day. As Alexis talked about how we're moving forward towards autonomous operations, in this section I want to take it back to its foundation. The Datadog platform is where 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 — thousands 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 really evolve it 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, generate code fixes, or interact with it through natural language. And we're just getting started, with more AI capabilities to come. But the platform is also critical for our rapid pace of innovation. By leveraging the platform and all its building blocks, our engineers 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 Kubernetes autoscaling end-to-end solutions. Or after the MetaPlan acquisition, we were able to take to market data observability in 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 products faster. But let's look at the data ingestion side. We've also added a number of data sources, including eval, 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 rising number of agents. They all need the data in one place, really, 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 source of truth to break down silos across all users and agents. So the platform has actually come a pretty long way over the years. Let me reflect on that. In 2015, we started with a few integrations, had millions of events per hour, and only had one product — infrastructure monitoring. But our customers' 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 reasons 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. Their customer demand grew very quickly, but where the end users were experiencing bottlenecks, they needed a unified observability solution to get to the root cause as fast as they could. And to do that, in about one year, they adopted 16 Datadog products, and that was really key to their business success. So that's the platform. Now let me speak of the way we're scaling and helping our customers ingest, retain, and analyze their growing data sets. So to explain scale, let me take the example of log management. We launched it back in 2018, and it had multiple scaling phases. The first phase was called logging without limits, and it's all about ingesting all your logs without limits and only processing the relevant ones with correlations with infrastructure monitoring and APM. So bringing in 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 was the foundation of our customers' value, and it worked great for observability logs. We also knew that we were only capturing a fraction of our customers' logs. Other use cases like transaction logs or audit logs typically involve log volumes that were orders of magnitude larger and which needed to be stored for much longer periods of time. So as a second stage of growth, we've extended the capabilities to support those use cases with Flex Logs, frozen, and archive 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 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 Flex Logs, as 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 five 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. As you can see on the chart, Flex Logs really unlocked that security revenue — it grew like 18x in five years, and we do see that acceleration. So Tim is going to talk more about this, as we actually do see the beginning of that opportunity. So let's now look at the impact of the platform data scale on actually a whole customer journey. This e-commerce customer started using infra, then in 2022 they adopted APM, synthetics, and logs, and in Q4 2023 they became a Flex Logs and SIEM customer. As you can see, over time they consolidated more tools within Datadog, which enabled them to address an increasing number of use cases, cover more environments, 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 helps our largest or most sophisticated customers to drive tool consolidation. Beyond monitoring everything in the cloud, a number of our customers are 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 already support on-prem servers and network, and now we also monitor wireless access points and 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 combine with their cloud environment. And for us, it means a larger footprint and a bigger market opportunity. We also continue to expand our coverage across our customers' tech stack. Historically, we've been focused on cloud deployment, and we've been anticipating market trends as we were first with containers and serverless. We have very heavily containerized customers, and one of them is actually our internal team, as we also heavily use Docker for our own product. But more for non-cloud environments, the stack is typically simpler and based on more kind of legacy architecture. For those types of environments, which send less data and are less complex, our pricing was actually not adapted. So now we're pricing for value to respond to our customers who want that end-to-end visibility, including their legacy stack.

But as we think about the future, there are a few customers now and potentially many more in the future that will adopt GPU-based architecture to serve their AI applications. The cost of building and running these environments are really high, and being able to improve performance and efficiency is critical for those modern AI workloads. So with solutions like GPU monitoring and AI observability, we're ahead of the curve, and we're already capturing AI-native workloads. So one thing you can take from my talk is that the Datadog platform is our key differentiation. It allows us to move fast, stay lean, fully support our customers' use cases and the tech stack, including AI workloads. But one last thing before I conclude — some customers cannot send their data out of their environment, either due to data residency compliance or due to very high volume, and I'm talking about petabytes per day, and that could become really cost-prohibitive. So Datadog BYOC — Bring Your Own Cloud — is actually built for those customers, as they can keep that telemetry in their own environment. They can index, store, and search data within their own infrastructure while using the powerful Datadog single-pane-of-glass cloud solutions for correlation, analysis, monitoring, and alerting. So we're already engaged with very large companies in previewing BYOC, and we expect to unlock even more opportunities with it. So that's it for me and the platform. Let me hand it over to Tim to talk about security. Thank you.

All right. Thank you, Eric. Hello everyone. My name is Tim Kudson, and I just joined Datadog and I lead our security products. Prior to Datadog, I held product and GTM leadership positions at Google Cloud Security, Zscaler, and Occupy, where I led global teams to deliver market-leading solutions and build security businesses. So let's begin. When we speak to customers, we commonly hear about this painful siloed attack surface. Okay, so threat detections in one place, you have application behavior and performance data needed to investigate threats in another. Security posture of resources and services supporting the apps, knowing which teams own what, in which repositories the code lives, all living in more separate silos, if at all. This results in detection gaps. This results in prioritization noise. This results in remediation slowdowns. And now in the era of AI and agentic, there's a whole new threat landscape further amplifying this problem. So but with Datadog, we flatten the attack surface. We combine and correlate security and observability in a fully and truly unified platform with all the capabilities of the SOC, for cloud security, DevOps — so all the critical teams defending the enterprise from security, SRE, and developers can work together smarter and faster. Over the past couple of years, we've been building out our security capabilities on the Datadog platform, and we now offer a broad portfolio that integrates the traditional silos between reactive security on the right and shift-left proactive security on the left. For the SOC, we have our Cloud SIEM. It delivers cloud-scale, unmatched data flexibility, direct pivots into observability data to speed investigation, and Bits AI Security to autonomously conduct investigations to accelerate threat detection and response. For cloud security, we provide posture scanning, runtime vulnerability detection, attack analysis, and combine that with observability-enriched prioritization to tell you what cloud risks matter the most.

Now, all our security products, because they're built on the Datadog platform, they all take advantage of Datadog's shared services to accelerate remediation. For example, integrated case creation and incident response, so 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 Datadog advantage that I just outlined — of unifying security and observability — is delivering value to customers. Today, Datadog has over 8,500 customers using our security products. This includes one in four 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, but we believe we have a big opportunity ahead of us. Today, 70% of our million-dollar customers use one or more Datadog security products, but security spend together represents 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 longtime 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 million-dollar 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 that 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 notes. Anybody using AI right now in this very moment? I knew it. There are some people out there. Advantage, right? So, I'm Michael Weden. So, we live in this era of speed right now, right? I don't know if y'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 it out as fast as possible. And it's been like that. I've been with 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 at 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 hyper competitive 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 trolly question, but my favorite thing to ask them is where, how do you know that things are broken now? Where do, what triggers an incident?' And most of the time they'll sheepishly look at the floor and say, 'Well, customers like a support ticket or somebody tweets something's 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 that all of these different teams are using different tools. So we have a bit of a Tower of Babel 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 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's 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 onto Datadog, they went and ripped out 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, 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 respond much faster so that you have global impact within an 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 moving the bank forward into what is the next generation of bank software, they're spending time in incidents. 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 is 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 invisibility and start traversing the organization that way? And so the crux of your business really is your application and your 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 in the space it's 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 backend 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 provide a lot more value which turns into 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 to 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 the automatically correlated and summarized data that we do is it's better for our AI agents too. We found that when we have the full context of the entire stack and it's 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 traverse all the applications. It's working 24/7, right? And so the advantage here is when it has the, 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 lifecycle. 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. And 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 lifecycle. And we do have that equilibrium. This last year we released, we announced we released hundreds of features into production, right? These aren't ideas. They're 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 see it's only accelerating. We got one guy who's coding with his glasses, right? Sending in prompts and he's saying, 'Looks good. No, 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 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 painstaking efforts to make sure it's good. But Datadog has automated integrated code testing. They 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 lifecycle 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 who's 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 tests, you run those tests locally. There's Git hooks in there that's going to run all your tests locally. Then you push your change to some centralized pool of sorrow called CI/CD 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, you know, type a 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. And what are you doing? If 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. 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, I could talk about any of these things, but the gist is we can automate much of this. Just as AI is pushing more code, our 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 it becoming a bottleneck or 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's getting dry, but I'm almost through it.

So the codegen 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 somebody yes last night told me that from the time they got a white paper for a new database, this person works at a database company, research paper, they went and vibed out 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's trying to take advantage of this, 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 product, 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 their 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 five of these agents that are supposed to be doing the same thing. Should we choose one of them? Right? So, the AI Agent Console 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 or 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, then you'll be familiar with all these tools. If not, it's almost an on-ramp in how to think about non-deterministic 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, etc. I can sandbox them. 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 and 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 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, Yamin Lee.

Thank you, Michael. How's everyone doing this? You know, you're getting to the last stretch of our first half. So, my name is Yamin Lee. I joined Datadog as Chief Product Officer about 18 months ago. Before Datadog I spent time at Google responsible for the observability function powering Google's planetary scale infrastructure and services. So when I went to speak to a senior SRE leader to get some candid feedback, this is what they told 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, Aurora Innovation. Actually, the very first company to operate commercial driverless truck on US public roads. At Aurora, I got 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, you know, to help our customers, you know, 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 Olivier showed earlier. This is a reality of what our customers' DevOps team lives through every day. They need to detect issues as they emerge. They try to investigate and find the 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 incident happens to a production system? You know, our largest customers often tell us, you know, 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 resources that's, you know, part of this operating expense. And certainly incidents we all know is very expensive to business outcomes, you know, with lost revenue, lost customer trust and reputational risk. So this is the structural inefficiency we're trying to solve at Datadog. So Datadog is in the business of keeping this DevOps 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. You know 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 US insurance company who's been a customer for five years. You know before Datadog they experience thousands of severe incidents every year. And with Datadog, after they standardize on our core pillars of products, they began detecting and fixing those issues proactively, preemptively before they became real production escalations. And 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 of 70 employee years and translate to 11 million every year. So what does this mean from their business point of view? You know again with fewer incidents, with faster resolution, they are actually seeing a whopping 20x reduction in customer impact caused by these incidents. 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. You know, we've launched a fleet of Bits AI agents, you know, Bits with those futuristic sunglasses. So we're helping our customers autonomously detect, decide, and take action so that we're closing this loop even faster. So, let me give you a few examples of the Bits AI agent and starting with the SRE agent. So you've heard about this several times throughout this presentation. So why SRE? You know 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 picked SRE as our first agentic effort because not only are our primary user personas SREs, but also when an incident happens, you know, 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 SRE, 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. You know when an alert triggers, Bits AI autonomously investigates the issue. It first gathers all the necessary data and the 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 hypotheses in parallel. The right hand side is intended to show you how that parallel investigation works and it's very visually explained to our users. And then identify the root cause and even can propose the next step of action based on the customer's runbook. 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, spanning systems and applications and users and teams and even business processes. So it doesn't just leverage the rich real-time observability context and telemetry inside the Datadog platform, it broadly integrates with third-party knowledge source 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. You know, here you see two 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 systems. And when they matter the most, our customers are also telling us Bits AI gets the job done. So if you remember the major AWS outage last October, we've got many customers reach out to us saying, you know, when that outage happened, Bits AI SRE was able to autonomously root cause the outage before being notified by AWS themselves. So even though Bits AI is an AI agent, it actually gets a lot of love notes from our customers. You know, as you can see on the screen here, and not just because this is Valentine's week, we hear this from customers all the time, how pleasantly surprised they are at how smart this AI is, how it gets to the root cause, how it's saving them time, how it's boosting their productivity.

And the best indications of that is the actual usage. You know, for new products, we look at the usage metric very, very closely. So since the Bits AI SRE launch, we've had our customers run well over 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 investigations with Bits AI SRE.

Okay. So let me switch gears 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 Analyst 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 Analyst for the first time and it actually correctly identified a live serious security threat. So the situation is a compromised automation system in their environment changed their cloud firewall setting such that it's open to the entire internet. You know, some of the sensitive management ports are open and exposed. Does this sound quite serious? Yeah. So without Bits AI, so the investigation would happen that they may receive some security signals and that goes into a queue and humans investigate them one by one and it could take hours for them to come to this realization versus what this security analyst agent could do. You know, it's investigating in parallel and within minutes we were able to surface 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 Analyst can truly transform how security teams can investigate and resolve security incidents.

Let me give another example. Because Bits AI SRE and Security Analyst, 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 is an error happening 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 functions and then proceed to generate a context-aware fix. And context-aware here is important because this fix would be generated based on the real production context that Datadog uniquely brings to this problem. We can then proceed to test the fix in an 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 logging in. And of course the tool can interact with the developer, you know 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 this AI Dev Agent is deeply integrated within our DevOps loop to truly bring 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 three Bits AI agent examples and the important thing is with AI how our users and engineers are interacting with their tools 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 invoked by other 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 agents to access Datadog's AI-driven observability context 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 workflows. They're also building custom AI agents so that they can build those agentic workflows to help them with incident investigation, performance optimization and many other use cases.

So with this 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 agents 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're helping our customers closing this loop very, very rapidly. And as Michael and Olivier alluded to earlier, we've also been shifting to this loop on the left hand side to the pre-production environment to help our customers ship better software into production. So our long-term vision as Olivier outlined is to achieve autonomy across DevOps and Security. 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 code 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. You know it is special, you know, having built autonomy for trucks, now building autonomy for development, ops and security. So at Datadog we are very excited about this vision and 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 a Q&A. Thank you.

Great. Thank you. Okay. Thank you, Yamin. We are going to start a Q&A session now. Joining me on stage are all the presenters you just heard from. Their names are up on the screen. We are going to be taking questions from the in-person audience. I have two of my colleagues, Megan on your right, Eric on your left with mics, and we'll alternate between them. So, please, yes, raise your hands and wait for them to get to you so we can all hear your question. We're going to start on Megan's side.

Thanks very much. It's Kirk Matern 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 Ops have different budgets. I was curious as we head into a world where you need more telemetry 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 side? Thanks.

Great question. So I think we're seeing two 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 obviation or no longer the need to have those separate budgets for those items because they can get it all through a single platform. So that's one item. The second point is I think there's also a broader recognition that in fact we do this ourselves at Datadog that having organizationally security and SRE in the same org has a lot of benefits, particularly when it comes across the board for both reliability as well as security teams working together. Of course in our case using our own platform and tool sets able to work much more effectively. So I think that's something that will also emerge over time as probably a growing trend across organizations as they see the need to be more effective.

Great. Thank you. Eric side.

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

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

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 making sure customers can let us use their names. But what I can share is our 2,000 customers are widely represented in all kinds of segments and verticals and geographies, from the largest Fortune 100 companies to the most innovative AI startups. So there is fairly broad non-discriminative adoption of this technology. And this is why I'm personally excited about SRE as a very strong well-fitted use case for AI because the result is instantaneous and verifiable. So this is why we're seeing such rapid increase in adoption.

And finally I'll just mention that our pricing is completely transparent. You can go to our corporate website and I believe Bits is $500 per 20 investigations, right? So, but you can check out all of that stuff by yourself whenever you want. All right, Megan's side. Great.

Hi, it's Keith Buckman from Tim. I also wanted to direct this to you. 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 through your slide deck you're still a relatively small part of generating ARR for Datadog and some of your competitors have much broader portfolio playing a consolidation game. So in some measures 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. Thank you.

Yeah, 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're 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 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 just 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 the 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 can again bring that advantage we have with our platform.

Okay, great. Eric side.

Thanks. Itai 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 costly or ineffective in the results they deliver in terms of accuracy. I guess when I look at your business, you talked about the massive feature set that you have, the domain expertise and your ability to take a small model and make a much better use of that in your business. I guess if I try to flip it on the upside down a little bit, the question is if it takes $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 this is where the data advantage is clear. And it's on the, in the case of our time series model, it's just we just have a volume of legitimate real data that's just not publicly available. So that's one edge. I think in the case of the training of the agents, it is both the volume and the quality of evals we can build that differentiates us. So I think there are obviously other companies trying to build small models. I don't think there's a clear financial barrier of entry but the quality of the data you get, that's the real moat. One of the issues and you know we see is for instance generating synthetic data in our domain is not terribly easy. It's not like you can basically sample what's out there, text or image and then remix and create something plausible. It is much more the relationship between the way software is built and the observed behavior is 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 small models. So that's where we retain advantage. I think the other piece is what Olivier did too, which is look, we just sit on that data, we get it for free as it were for training purposes, because it's used by our customers in the day-to-day and that's where someone else just isn't sitting in that flow. They have to acquire that flow somehow and they don't have the scale to do that. So 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 Datadog more and more valuable. So it's really accretive in that sense and remember, so we spent a billion dollars in R&D last year, maybe between 700 and 800 the year before and then another 500 the year before that. So spending all this money is why it cost us 750k to train the model.

Great. Megan side.

Yes, Satya Nadin from Morgan Stanley. Thank you for taking the question. 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 three years from now? And then in terms of executing 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 race to autonomous operations, what does that look like 12 months from now and over the next couple of years?

Can I add some more comments that you know, what I've learned about autonomy for dev ops and security is it's not a zero to one game. This is very different from putting trucks on the highway without a driver. 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 started with the investigation piece. Initially a lot of the customers don't trust the result of the investigation. They verify that. But now as they use it more they start to gain more trust. Same thing with pushing a code fix. Most of the people still are very comfortable, absolutely requiring people in the loop. But as more confidence gets built and the technology gets 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 the circle to increase customer trust, improving the coverage and the rest of the technology. I don't think this autonomy is going to happen in a zero to one fashion. It's going to happen in this partnership between technology evolution and our customers' comfort and trust and culture evolution.

Great. Thank you. Eric side.

Over here. Um, thanks for doing this. Eric Heat at Key Bank. Um, I wanted to come back to maybe bring your own cloud and understand that opportunity a little bit more. Um, can you just talk a little bit about who the addressable customer base is for this, maybe the timing of when you make this product more broadly available and the go-to-market strategy around it? Thanks.

There's another type of customer we're targeting with that which is the hyperscale customers, you know. So when setup customers want to make use of infrastructure they already have or they want a licensing model that is more preferable to them than the SaaS model. Um, and that is something that we are addressing with BYOC as well. So we see some of these customers coming to us because 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 use for it.

Megan side,

I think I have the mic. Fatima Balani from City, thank you so much for doing this. Um, my question is just around the Bits AI suite. Uh, 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. Um, you all are very excited about the code security that you can absolutely infuse right out of the gate. Um, but Opus 4.6 and the Codeex 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-à-vis the type of code security hygiene and rigor you're providing vis-à-vis the context from your platform versus the general purpose LLMs who could maybe have a broader coverage around code security because, you know, to your observations, the coding assistants are only going parabolic. Thank you.

Yeah, I don't think we're going to see the end for the 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 security problems. But to Michael's point, there's a lot involved and a lot of complexity in these production runtime environments. Um, and that's not going to go away. And there's always going to be this need to understand for something that has been found, you know, to the degree for example a vulnerability, is it actually something that's not only being loaded but is it actually being executed? And that's, you know, that's the area that we'll continue to focus on even with the advent and parabolic adoption of coding agents. Yeah, it's not or, right? It's and and and again, so the few examples of ways in which this breaks down, you know, it's also because the code you at the point at the time you wrote it because it was secure at the time doesn't mean it's still secure, you know, two weeks from now. Um, so you know, there's something that needs to be reevaluated permanently. There are some things that no cloud might think is secure but you as a company decide is not, you know, and so you might have your own rules, you might have your own, you know, 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, you know, one that tooling might use some of the same models as these agents and two, you know, the agents are here to stay and they're going to do more and more, so it's going to be a question of working with them and complementing them not trying to replace them, right? And can we produce a good product, right? I'm sure people told you why would you build a monitoring company for cloud about software when the clouds would probably do it right.

Here we are. Oh, Debbie for you.

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

Megan side.

Hey, this is Aria from JP Morgan here from Mark Murphy. Um, Olivier, anyone who wants to chime in. A couple 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. 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, you know, 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? And is there like an aha moment for them? Thanks.

Great, all right, Eric's side.

Hey, Kingsley Crane at Canacort. Uh, thanks for doing this. 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?

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

Hi, 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 I guess Open Telemetry being more of a standardized protocol and what is Datadog's competitive mode while embracing these open source standards? And specifically on the back end, I'm curious how defensible are, you know, having a thousand plus integrations and the ability to correlate lots of different data sources in the way that Datadog does that's different from others? And from a coverage standpoint, you had a slide that shows monitoring virtualized environments on one, so going more on prem in one direction, and then GPU monitoring in another direction. Is it really 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? Thank you.

Yeah, I think so.

Maybe just to add on the Open Telemetry side, it's not really a competition in some way because we are like big contributors to Open Telemetry. If you look at Datadog, we're like in the top contributors of OTel and we have like now we fully support Open Telemetry and we're supporting like any type of data will come the same way being Open Telemetry 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.

All right. Welcome back everyone. Let's kick off the second half of our investor day. I'm pleased to welcome Shawn Walters, our chief revenue officer, onto the stage.

All right. Good afternoon. My name is Sean Walters and I've been with Datadog for seven 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 three 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 million-dollar plus customers are commercial and 72% of our 100K 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 guides 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 teams. 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 teams. 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-grade 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 a half a million dollars 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 customer's 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 and 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 where 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 and alliances influenced business, but we have a lot more opportunity here. Another area of growing investment is security. We started our security in 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 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 developed 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're 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 multi-million dollar customer with us. They chose Datadog as a strategic security vendor as they believe the 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 a quarter 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 US, 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're 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, Bits AI Suite 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. Uh, my name is Adam Blitzer and I'm the COO here at Datadog. Uh, I've been with the company for just about five 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. You know, 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've 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 roadmap. 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. But 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. Uh and that was that buyers really sought to consolidate on platforms. So instead of using, you know, 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. Uh 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 in direct cost 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. Um but we think this becomes even more valuable in the age of AI. Um 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's own 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 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 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. Uh 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. Uh 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. Uh 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. Uh you heard about some of that from IRX 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. Uh and you can see that their footprint with us has gone from using quite a few products to quite a few more products. Uh 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 one 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. Uh 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. Uh IRX mentioned this one specifically earlier today. Uh but this is an example from one of our tech customers. They've been a longtime logs user for us. uh and they adopted Flex Logs uh 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 launch 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 autoscaling, etc. Now here's an example from a major software company uh that turned on our cloud cost management uh product and immediately found significant savings. So they were just really looking across six of their environments of which they have many environments. Immediately found a million dollars in savings. Um 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. uh we spoke about that a lot in the morning session but we think of innovation as really the secret sauce of the company uh 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 a billion dollars 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. Uh, and we think that that really makes us future-proof. Uh, 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. 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 is 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 a million dollars a year with us and the cohort itself is quite large with more than 650 of them using Datadog. 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. Uh 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 Obsler.

Thank you, Adam. Uh, for those that you don't know me, I'm David Oopser and I've had the great pleasure of being the Datadog CFO for the last seven years. Um, 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. Uh 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, we would not have had this day come up. Thank you team. Thanks a lot. Um, thank God we only do this every two years. It's a lot of work. Um, so, let me start now. What we'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. Uh all to create a broader and more uniform 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 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 a quarter 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 three quarters 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 of investment that you've heard in the previous presentations. And what that has produced is a rapid expansion of the customers we get to 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 Olivier's presentation at the very base there's a very long secular tailwind for digital migration and cloud transformation. 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 the 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 LLM observability product is beginning to take off. We have many customers using it and just in the last year that has increased 10 times. That's the span sent to us to our...