Good morning, everyone. Welcome to Day Two of Nasdaq London. We're really happy to kick it off strong with the Datadog Management Team. We're really happy to have David Obstler, Chief Financial Officer of Datadog, to join us for Day Two. Thank you, David, for coming. Thanks for having us. Appreciate it. Yeah. So, David and I have known each other for years now, going back to the IPO. Seven or eight years. Seven or eight years. You guys were the shiny new kid on the block, and time flies. Are you saying I'm not shiny? No, I'm shiny. I'm certainly not shiny. That might be your thing, but so I thought that we'd kick off maybe just sort of at a foundational level for our European investors, right, and I call this sort of the Datadog flywheel. Like, since you guys have been public, yeah, you guys have delivered kind of top-decile growth in enterprise software, but that's come along with great margins as well, so I was wondering if you could describe the core elements of the business model that allows a company to deliver that combination of both growth and profitability. Yeah. Good question. I think about it a lot. Thank you to our founders, Alexis and Oli, because they created a platform that can be adopted without friction, not a seat model, a usage model, very strong, strong time to value, so one of the things that has helped Datadog continue to invest has been a really efficient go-to-market where once our clients land and adopt the platform, they can adopt it with the assistance of our salespeople and our account managers, but also can do a lot of it themselves, and that creates a very good return on sales dollars, and I think that has enabled us to invest at the top level in terms of R&D, which we've maintained around 30% of revenues. And so that combination allows us along with very good gross margin and cloud operations management to maintain and grow the profitability and cash flow, yet be top drawer in terms of reinvestment. I think that was shown in the last year where we re-accelerated investment both in R&D and particularly in sales and marketing. And we're able to get very good return from that, whether that be the places where we've put some quota- carrying reps, as well as in some of the new product investment we've made. Yeah. No, it's a really important point. I think when I think about Datadog, when I think about the, like, the leverage that you guys get in R&D, we're one of the few companies that can launch a product and then with a couple of years, you know, it depends on the category, we start seeing revenue from that product hit the income statement, probably quicker than most enterprises. I'd also add that there's a lot of efficiency when you have as big a platform. Yep. You're not having to create both the platform and the new product. You're able to add applications on top of the product in a very efficient way, and then we talked about the adoption, frictionless adoption in the platform, and both those together result in very good time to value in the product investment. And so I guess the question is, going forward, does that flywheel carry forward? And we just sort of level set where Datadog is going as a company. You guys are in this sort of multi-year evolution from, you know, a company that's in observability and monitoring, but you guys are moving into security. You guys are moving into, helping customers remediate some of the issues that you guys flag for customers. And so in that, you know, kind of long-term evolution, does that flywheel change, particularly from a go-to-market perspective? And will this kind of broader vision going from, observe, secure, act, will that have a structural impact on the underlying unit economics of the business? Yeah, I think, again, we're in a good position where we have, against the backdrop of long-term trend of modernization of software stack applications and the consistent conversion to cloud, which is creating this long-term growth. The world's getting more complex. Yeah. And essentially our customer base in DevOps has been on this journey with us in using more and more information in different ways to solve problems. And we've been following that. So that goes from the trend of having a single pane of glass to the trend that we're seeing now in AI and service management, where you, using the platform to detect problems, analyze it, suggest what is most likely, and long-term, we think, have some self-remediation. And AI is facilitating that. At the same time, our customer base in DevOps and production is using more signals, whether that be the security signals we're talking about or onto our recent investment in product analytics, because it really is a continuum where all the different parties are software developers, production engineers, security, product analysts benefit from being on the same platform and being able to share information frictionlessly. So I think we're seeing a long-term trend and technology and practices facilitate a broadening of the platform, which is at the very core of what Datadog's doing in this flywheel, as you call it. Yeah. So where are we in that term of long-term evolution? The core observability business is quite healthy. Yeah. But in terms of that move into cloud security, product analytics, remediation, where are we in that journey? If you want to use the baseball analogy or any other sort of framework to, you know, set the stage on where we are in that longer-term evolution. Yeah, I think in terms of the base, which is more and more workloads and more modern workloads, you know, we're early innings because the percentage of modernized workloads, according to research analysts, is in the upper 20s, maybe 30%. It's hard to tell, but we got a long way to go there. And I think we're really in the early innings when it comes to adding this additional functionality beyond the observability platform. When you look at the revenue contributions that have been created and what's happening in terms of the product formation and the growth, there's lots of opportunities. We know we're in early innings on the use of AI and software platforms. And for us, we're still in early innings. We're coming at this from the observability side, and we're starting to get traction. We can talk about a number of examples, but we're in early innings there where we're making investments, but we're still not in maturity. We're still, you know, adding to both the sales and marketing and platform. So I think in terms of the complementary products, we're in very early innings. Yeah. And that kind of dovetails when we talk to other players in the ecosystem, just in terms of the enterprise AI custom application build cycle. It's, you know, we're seeing some movement, but still feels pretty early. Obviously, you have an AI- native cohort that's doing really, really well for you guys. But that dovetails, you know, pretty consistently in terms of what. And I think it's a good example. I mean, we're one of the more tech-forward companies, and we just last week at re:Invent, we had general, with GA on our first AI-native application, which was the SRE agent. So when you think about it, we've been talking about this for a while, and we've been investing in it, and we're GA now. So it takes a while in terms of testing, developing, and then getting client adoption. Yeah, that's pretty exciting. Yeah. In terms of the SRE agent. Let's talk a little bit about Q3 results, 'cause it was notable on a couple of fronts, right? So the headline number, total revenue growth sustained at 28%. That was in line with last quarter. If you look at the core business, ex-AI- natives, that accelerated. Your SMB business had already been doing well. So, I guess, the question for you, David, is, you know, did anything change in the demand signal if you compare early 2025 to what you guys see in Q3, that sort of non-AI cohort, you know, uptick? What were some of the drivers behind that? Yeah, that's a good question. I think we, if you remember back to the adjustment after COVID to the optimization, we then got to something we called stabilization, which means that a lot of clients had done the work on the cost side and were looking at getting back to normal in terms of investment, and over the last couple of quarters, we have seen a more positive buying environment, which means more focus on migration and also more consolidation of the platform from Datadog, and so I think we're not in that bubble market. We're not in a market that's focused first on cost control. We're in something in between, which is, I would say, a good strong market, and then it broadened in the last couple of quarters, so you get the total environment from seeing what's working across the board. And I think we had seen enterprise working. And one of the things, this is not AI. Some ask, well, of course you're seeing this, because in SMB most of those customers are SMBs. By the way, for us, SMBs can be pretty large revenue companies. They have employees less than 1,000, but they can have hundreds of millions of revenues. And I think we have, excluding all of that, we can get to that. We are seeing within the SMB cohort, which is a meaningful cohort for us, strong demand signals that's translated into higher net retention and more logos. Now, it's a little hard to separate because we are essentially at the year anniversary of accelerating reinvestment. So at the same time that the market has been better, we've also been investing both in product, but in quota capacity. Those two certainly interact in terms of producing the results we did. Yeah. I think one of the things that stood out to me, you know, in the last couple of quarters, just in sort of public cloud world, is that the classic pillars of public cloud don't seem to be coming back with respect to migrations, with respect to modernization projects, digital transformation, which are some of the kind of foundational drivers for Datadog. So it sort of makes sense how that sort of, as you say, interacts with the greater investments. Definitely the case. You know, on the call, you guys commented that the content, the better consumption trends continued into October and the pipeline for Q4 looks good. When you guys think about 2026, how do you sort of judge whether the trend lines you saw in October could prove durable? Like, what are you guys sort of looking for? What's the data? How do you sort of assess that? Yeah. You know, we're a consumption model, so we have a lot of experience in looking at trends, but it also because we're consumption, it also is hard to predict the exact slope of the line, but the signs improved in Q3. We've told everybody that they stayed strong in October, and when we look at what we can see, which is the pipeline and what we're seeing, so we're able to monitor usage net retention on a pretty much real-time basis, and we can monitor how that might be going through new logos and then new commitments, and they're all positive, you know, trends. We'll let everybody know, we'll complete the year and we'll think about where we are relative to next year when we give our guidance in February. We'll continue in the same vein of giving conservative guidance relative to trends. But the good news is the entry point and sort of the trends and the wind behind us seems to be strong in entering into 2026. Yeah, that starting point looks pretty strong. There's so much to like about Q3 results. One of the other things that stood out to me was the new logo performance. So new logo annualized bookings more than doubled, year over year. The land size are getting larger. There were seven-figure wins across telco, financial services, hardware. Why now for the bigger lands and how durable is that momentum with new logo AR? That's a good question. So I think they're correlated with, we've always said, we're not the kind of company that works for years to try to get the full envelope. We are land and expand. But as our platform has grown and as it's become, the whole trend towards platform has become more evident, it becomes more of an imperative to land with more of the platform. So we're seeing that. At At the same time, I think we're better at it. So whereas we're not waiting for all the departments, it's not a centralized spend like some other industries are used to, but we are getting better at analyzing how to go a combination of top down and bottoms up. And that is everything from being comfortable with the multi-channel approach, which includes partnering with the channels, the GSIs, having a bigger enterprise sales team, as well as doing things like adoption credits in exchange for long-term deals, which really facilitate the speed of the consolidation. So I think we've also gotten more diverse in how we go to market. And the two of those together, the platform and expansion and the way we approach it have resulted in some dividends here. Yeah. And so like, I think the theme that we've been having thus far is, you know, the backdrop, the demand environment looks generally attractive. You guys are executing better. I mean, 2025, you guys explicitly told us that this was going to be an investment year, and judging by the results year to date, I think that it seems like those investments are paying off. And so could you outline for us, David, the key investments the team has been making, how you measure return and productivity on those investments, and what's the intention in terms of sustaining that piece of investment going forward? Yeah. So start with sales and marketing, where we really do bottoms up. We look on, on, on the white space. The clients that have demonstrated signals to be buyers, that includes cloud, that includes hands on keyboard, size of DevOps organizations. And we're a follower of that. So we are able to look at where the TAM is. And then we look at that versus our coverage. And that can be on accounts in the Northeast in the United States all the way to feet on the ground in Brazil and India and some of the other markets. And when we look at that, we still see we have a lot of uncovered territory. And so are we, are we doing it at the right pace? So we look at things like the return on sales and marketing dollars, CAC return, which has been able to be maintained. We look at productivity of salespeople because we don't want to have two times the salespeople to produce the same sales. We look at that. And then we look at the performance of some of our newer investments. And all of those are giving us signals. So that's how we sort of handicap. And that's what we use in planning and can stand by the statement that we can continue to expand our go-to-market. We're also, I think, doing go-to-market in some different ways, like a different way of going to market, in terms of the government and Fed section. We're having more data centers. I think there's lots of ways from the marketplace to channels and resellers that you need to do it. For instance, security, we've learned, you need to add on some additional ways of going to market, given it's more of a channel and centralized. So these are some of the investments. So then you turn to R&D, and then you go and look at, essentially what's the landscape, what are the revenues today and in the future that are being created, and in various platforms. And are we able to add that functionality and be best of breed in our platform? And the answer has been yes. So we are continuing to invest at this industry-leading around 30% of R&D. And then there's the thing I think Oli, Alexis, et cetera, are really good at. Well, what about not what is being used today, but we think might be used in the future? And that gets to things like the AI-ness of the platform. And are we going to be the one, given our competitive advantage in the customer base and the platform, to out-innovate everybody and not be in a position where there's functionality by a startup that we don't have? No, what we're trying to do is do that and look ahead, all of it, because we get really good signals from our large customer base, our diverse customer base, and the fact that we can see usage. So that's sort of what we look at. It comes down in the end to things we've been reporting, which is what are the SKUs that are being adopted as part of the package? And are we getting traction? And you know from following us that we have reliably given information to evidence that we are inventing new SKUs or improving SKUs that are being adopted by the customer base. Yeah, I think the way you guys price, you can see this, I mean, 'cause you guys price on a sort of product basis through a commitment model. So you can sort of test whether, you know, an individual product is, you know, seeing the adoption. We price on commitment. So you buy, let's say, $2 million. And then underneath of it is a price grid that's consistent with fine. And then we can see every day, like we can go in and see what customers are doing and also say, that customer should be using this, but really isn't as much. That is gold with information. And it allows us to determine not only usage, but are we pricing it right? How are we bundling or putting it together, with a transparent pricing per product? So it's a really good competitive advantage for the expansion. Yeah, it's a great point. So let's talk a little bit about the AI opportunity, and this is a question that I, as an analyst, always get about how is AI impacting the growth of X company that I cover. What would you say is the clearest way to think about how AI will drive growth at Datadog in the future? Well, the clearest and most proximate is that there are companies, technology companies, which we're calling AI- natives here, but essentially you can scratch out AI and say cloud native, because they're modern software companies. Are they going through a demand cycle that where their revenues and their usage is growing, and do you have product- market fit? Because most of the first investment, as we all know, has been in the infrastructure side, the model building, things like that, and the answer to those has been a resounding yes, and that's one of the things you've seen in terms of the acceleration of the revenues in the AI. Let's put the largest company aside, but you can include it as well. Is that accelerating? Yes, and is Datadog the right product for that? Yes, and so I think you're seeing focused and concentrated usage. Now, that's the beginning of this, because essentially the way most companies are using AI is they're still in training and experimentation, but to the extent, and they're mainly calling out to these model providers. They're very beginnings of you form your own model, you have your own GPUs. Think about who the commitments are from these neo clouds. They are the hyperscalers and, you know, the model providers and the AI- natives. So we're in that stage right now, stage one potentially of playing with it and then getting to the point where you're starting to see some adoption in enterprises, and you can tell that from things like the number of companies, the thousands that are sending us data, the use of the platform, but you can tell that it's very early. So I think this is going to evolve in many ways, but we're seeing good demand signals. Then you're seeing Datadog itself. So that's one way. The end market first concentrated with these companies spreading out into production models and applications. Then you look at Datadog. We're a good example. We're a platform. We're a software vendor. What are we doing? Are we integrating AI into our platform and releasing it? And you're, we are. So we're having quite a bit of investment in putting agents into our model, putting research. So that's another way we think. Then there's some industry trends where what might be the knock-on effect of all this, where in the past, when we've seen technology change, containers serverless, it's accelerated the impetus for re-platforming. And this one also has the trends of potentially making software development more efficient. And so we're beginning to see that internally and we're beginning to see that in our customers. So that is part of the long-term trend. Is this going to improve the growth rate of the re-platforming of legacy and on-premise applications to the cloud? So it's, you know, it's important in a number of ways. The earliest you can see is the use of the product of Datadog by those AI- natives. And then over time, we believe the knock-on effects of that are going to be more pervasive through our results. And ultimately this culminates in just more public cloud consumption, which is the core demand driver. That's what we think. Just to, you know, think about the AI- natives, which is 12% of your business versus kind of the enterprise opportunity. Obviously, the AI- natives are growing quite well and are creative for your growth. What evidence are we seeing that the kind of your enterprise base are building those Gen AI, agentic applications, getting them into production environments? To what extent is that a driver or part of the better growth you're seeing today and how meaningful of this could be, you know, over the medium term? Yeah, definitely. We're seeing signals like the use of our LLM monitoring. The first thing we do is we build integrations to access the data so our clients can send us data, and we can receive it, and we're seeing an increase of the rate of that thousands, and then it's small, but we're seeing direct GPU use go up, so I think there's certainly demand signals there, but it is not at this point where we're seeing the most contribution to revenues, so again we can control our own use of AI in production environments. We can't control the use of our customers, so what we can do is set ourselves up so that whatever they do, they can use Datadog, Datadog to access the data and the integrations, and we're certainly doing that, and although small, the trends are positive in terms of that spreading. I think that this is going to be the biggest part of the opportunity or else the natives aren't going to do as well because it has to spread, but it's still early days and it could go a number of different directions in terms of pace and how it's done. Yeah, that's a great point. So one of the things investors flag, and I've been covering this category for a long time, is that there's a lot of players in observability, and you sort of think about observability and security has sort of been on a crash course for a few years now. You guys have entered the market. I think this was further underscored by Palo Alto's recent acquisition of a private observability competitor called Chronosphere. I'd love to hear your thoughts on Palo Alto's move into observability and what it can mean for the competitive environment going forward for Datadog. Yeah. So this isn't new. You know this. You've, we've been working in these trends for a long time, but there have been these point or smaller solution companies that have been birthed, you know, for the last 15 years, but certainly, I'm in my eighth year and we've had this exact same discussion many, many times. I'll get to this company 'cause we've had the exact same discussion on this company. So what has tended to happen is there's been point solutions or smaller companies that get birthed. They often get sold. The reason why is there are competitive advantages in platform and amortization of tech investment. And we've been, I think smart enough, fortunate enough to take advantage of that. So you've seen Splunk try to do this. You've seen ServiceNow try to do this. You've seen the birth of all sorts of point solutions. Generally, they have not captured a significant share of the market. They've ended up being all the way back to the Cisco acquisition of AppDynamics. They've ended up being acquired. When they get acquired, this is the past, they tend to be swallowed up and there's less innovation and we lick our chops. In this case for Chronosphere, this has been going on for four years. The same question, which is, they're out there, they're saying they're doing this, that, this, they're, you know, cutting price, et cetera, and a couple things, it hasn't worked. What they have is they don't have an observability solution. They have essentially a metric store plus, which is the way you look at infrastructure. They've been trying to do this, which is, you know, cut the price, but it's basically bought on functionality. We've self-innovated. So we have the exact same product. We have a much bigger sort of product line. So clients can use us or can use us and Chronosphere. It's observability is not a category where for in a company or enterprise or even a cloud native necessarily, you get 100% of wallet share. So we now have Palo Alto that has, that is not really an observability. They've acquired a company that doesn't have an observability platform, but has a piece. If we have 30 SKUs, they have a couple of them. And they're going to continue to try to sell this, but again, they don't have more than that. So I think we're in a situation where it's the same thing, that's been going on for with a number of point solution companies, but also the last few years. Now, is this going to benefit or weaken this, because it's been going on and it remains to be seen. You have the Palo Alto distribution, very strong position in security, but not in observability. How do you compete with someone who has this installed base, this sales team, with what they've said they're going to do, which is run it as an independent entity? And what happens with the investment? We don't know, but this isn't new. I think it just is a flare-up of a discussion that's happened a number of times in the last years. Yeah, there's definitely been a pattern there. When we think about, well, just a comment, I mean, your point that you don't typically get 100% of wallet share. I mean, there's been studies out there, and particularly a couple of years ago, I mean, the average enterprise had like over a dozen different monitoring tools. Let's talk a little bit about the security business. So some interesting disclosures you guys provided. It's now crossed 100 million ARR, growing mid-50% year over year. That's an acceleration versus Q2. Where are you winning most in security and what are the top two or three why you win proof points versus like your competitors? So I think what we've done so far is we have for cloud natives, which are mainly SMS, which tend to have more closely related DevOps and security. It's called DevSecOps. We've been able to successfully bundle our products and sell through. That's not as big a market as we know security is. So what's happened more recently? What's happened more recently, and the reason for the acceleration is that we've gotten to product maturity in a product, which is Cloud SIEM, where we've invested in the underlying logs. We are a very strong player in observability logs. When you say logs, you have to have a word in front of it. You have to have IT logs, security logs, observability logs. And so we've basically broadened our capabilities in logs and put them in Cloud SIEM to create a very compelling product with synergies. And then, it's against a backdrop where we have some disruption in the market with Cisco's acquisition of Splunk, the sort of disintermediation of Splunk, which we've already done in the observability. And that's created, and we've gotten better, I said, at enterprise. So I think we have complementary SMB DevOps. We have this enterprise Cloud SIEM. That's probably the most proximate opportunity. But what's going to happen next? Everything from how do you continue to develop to what I mentioned before is we've really not had a channel led, a CISO led motion here. And we've invested in our channel partnerships. And literally in the last you know months or so, we brought on our first specialty security salespeople. Wow. Small. So So everything we've done so far has been product led or strong proximate synergies to products we've already had installed. And if you read the earnings script, you'll see if you go and you look at the names of the companies, what you see is we have a lot of products in this bank or this automotive company, and they've added a Cloud SIEM. And I think it's important to think about the fact that we're not using the word SIEM. We're using Cloud SIEM, meaning we're not trying to displace on-premise Splunk SIEM for various users. So we're focused on the most proximate use cases and trying to improve the way we're able to attach and get market. So I think that's the big change you've seen in that we have sort of another motion going on, still early days, but we're really focusing on the places we have the most synergy and the most proximate type of workflows. That's awesome. Exciting to think about going into next year. About time, David. Thank you for spending 30 minutes. Great discussion. Thank you, everybody. Thanks. Thank you.