Making our way through the morning session. We are thrilled. We've had a really good run of companies here. Thanks, everybody, for coming here. We are continuing the momentum with Datadog. David Obstler, I've known you for when was the IPO? It was six, I think, five and a half years ago. I've been at Datadog for seven years. My anniversary was November 1, 2018. Oh, it's time flies. Yeah. Having fun. It's been unbelievable. Yeah. Yeah. I was just talking to Yuka. This last quarter was certainly an inflection from a numbers perspective. I think, you know, the point is you guys, you know, in some regards, it's just business as usual. You guys continue to do what you're doing. Maybe from your perspective, obviously, the non-AI piece, obviously that and your largest customer were kind of the two hallmark parts of the quarter. Just walk us through sort of like the strength in the quarter, and I guess specifically the non-AI piece. Yeah, definitely. We can go through. There's been three real factors. One, we've been successfully expanding quota capacity to cover more accounts. That's translated into some successive quarters of more new logos and more dollars of new logos, larger new logos. This has been building. Next, the platform and the investments in the product and the continued ability to have clients adopt the products. We have a number of metrics we did in our earnings. That is the three pillars, plus I think we said Digital Experience Monitoring got over $300 million. We also talked about some of the successes we're having in Cloud SIEM. The last part is that we don't have the headwind as in weighted average intensity of optimization. That doesn't mean there's no optimization. When you look across the customer base, we have customers who have done a good job. I think we've helped them in terms of the platform, the product expansion, the way we've dealt with customers so that we have, you know, we don't have that kind of pushback in optimization that we had. It is a more conducive environment. Yeah. That's what's happening. That's been building. This didn't happen like. Overnight. On a certain day. This has been building for a number of quarters. Hopefully, we've communicated that we are seeing those trends over time. Yeah. I mean, I guess, you know, wanted to double-click on the non-AI piece, the non-AI customer cohort. I mean, is there any way to put your finger on what—and it's probably hard to answer what changed because it doesn't feel—the answer is like, it's hard to say. Like, I think a lot of us felt the AI natives cohort was leading the way towards implying that as customers adopt more AI, more workload, more monitoring. Are you seeing this as the sustainability of this non-AI piece is like the flip of a switch that more AI workloads are going to production from this non-AI native cohort? Because it just feels like there could be some durability to that element. Yeah, we think so. I think it's a boring answer. Remember, a very large percentage, you know, 70%-75% of workloads are legacy workloads that haven't been modernized, rewritten, and they're on-premise or legacy. This probably AI is causing customers to focus on the fact that if you want to get utility out of applications, you have to work on rewriting them. We also believe that some of the coding tools and other AI is going to accelerate the creation of new applications. I think that's what we're seeing. We're seeing more companies focus on the modernization of their tech stack. As our platform has increased in sort of value, we're seeing, you know, our market share increase in the amount of ARR that's being put on our platform relative to other choices. The answer is it's not one thing. No. It's, you know, it could be trying to think differently about on-premise workload. It could certainly be AI workload. It could be Cloud SIEM. There's a variety of things there. Yeah. In the portfolio, when you get to our position, it's the performance in a quarter is sort of like you have a number of different opportunities. The more of them that are going in a certain direction, they're going to add up to this. Because we're large enough geographically, diversified enough geographically by customer and all of that and product, you basically have a lot of efforts. If they are heading in the same direction, they amplify themselves and produce what happened. Yeah. You know, we don't have the answers of, you know, one of the questions that we get about AI is, are we in an AI bubble? That's not for us to sort of like think through. Like, how important is AI workload growth within that non-AI cohort? I mean, are we at an inflection point where like banks, insurance companies are all of a sudden like, you know, with the answer like, I know there's more than just that driving result. Are we at a tipping point now where AI from a broader economy is starting to? I don't think we're in a position to call that. We're sort of, as you know, we're a follower, meaning we set ourselves up to monitor workloads. Yeah. We're setting ourselves to monitor workloads, whatever they are. You know, what we can control, you know, from we can talk about it more. We're putting more AI in our platform. It is still a small percentage of the value that we're able, that we're conveying to customers. I think we're still early on. I do not think this is an application of software. I do not think this is, like we said, like inflection, flip the switch. I think it is part of the overall modernization of the tech stack and a positive effect of that on that. OK. Maybe just one last point here. You'd said that you'd felt the momentum was building. You'd seen some of the early indicators previously that some of this stuff was coming around the concept of the durability of this. I mean, are some of these trends that you're seeing now, I mean, obviously, the macro can change in the future. The durability of some of these trends that you're seeing. Yeah, the forward-looking things that we've communicated are, one, we saw a persistence and continuation of the trends in October. We also said we have a very large and good pipeline in the fourth quarter and going into early next year. That's what we can see. You know, again, we're not in a position to call durability or economic cycles. It looks like, you know, things are continuing. You know, the other, obviously, you guys renewed and expanded your largest AI customer, which was obviously a huge talking point for a lot of investors. What was really surprising, too, was, you know, the growth in all of the rest of the AI natives. I mean, you're seeing some significant, you know, progress there with six-figure, seven-figure customers. Yes. What are, you know, could you pinpoint what those customers are using you guys for, like specifically? Is it, you know, monitoring known workloads, customer-facing side? Like, what are? Yeah, exactly. Customers, same thing. Essentially, we've all created this AI native and non-AI native. Really, they're called modern software companies. Yeah. What do they have? They don't have a legacy infrastructure. They don't have on-premise, you know, legacy. Everything about their business model is, you know, a modern digital application. They're experiencing a rapid demand cycle. Like all of our other customers, we're monitoring their workloads in their client-facing applications. What you'd see there would be metrics, traces, logs. Infrastructure, APM logs. Then you would see digital experience, which we said we've got over $300 million. You would see database monitoring and some security. Essentially, the difference, the only difference here would be you have, in some cases, a faster ramp to significant scaled revenue than you might have seen in other software cycles. They've been releasing their revenue numbers, you know, and their funding, but also their revenues. That would be a good indication. If they're getting a lot more revenues, it's very likely they have more workloads. If they have very more workloads and Datadog is the choice for these, that Datadog's revenues would be growing. That's our model. Yeah. Yeah. Nothing different, nothing different. You know, one of the narratives really for the past couple of years is, could AI kill software? And you guys, certainly from a pricing perspective, are immune to seat-based pressure. We are. When you look at how AI is impacting the infrastructure stack, and when you think about open-source technologies and things like that, I mean, how do you see AI evolving how customers think about the infrastructure or the monitoring layer in the future? You know, how do you, because there's, you know, you're unique in that there's no seat-based issue that you guys have to deal with. Yeah, I mean, it's also we're probably in a good position in that we're infrastructure. Yeah. Essentially, it really does as long as we invest in our, you know, in our R&D cycle, as long as we and we set ourselves up to monitor whatever it is. That's what we've always done, whether it's serverless or containers or anything. Basically, we, as long as software is going to be delivered, if it's more agentic, if it's got whatever it is, we're setting ourselves up to be able to meter and monitor that. Yeah. That's a good position to be in because essentially, we're not seat-based, but we're also agnostic to what's being, what's in the software. We're just going to make sure that we're able to monitor whatever the composition of that software is. Yeah. I guess, you know, with open-source alternatives or DIY, you know, from a customer lens perspective, you know, there's always, you know, and forgetting your largest customer, what are you hearing from customers in terms of the criticality of like, oh, yeah, sure, I could go do this in an open-source model here or there. Like, is it really, why bother, you know. No, they don't want to do it. I mean, that's how we got to our size. That's why we're, you know, we're creating versus the, we're creating, like, there's other companies. We're creating those other companies in every month or quarter because they don't want to. There's tremendous value in having everything in a single pane of glass and a platform. It costs a lot less. Yes. You're able to get the best in class. You don't have to re-put your resources that you're doing in your own business. That's why there isn't much in-sourcing. Sorry, outsourcing has been the trend, which is one of the things that's made Datadog. With few exceptions, that's really what is driving the market. It's been moving since we went public more towards a purchase of a service or a software than in-sourcing. Yeah. And then maybe just one other kind of AI question. You sort of alluded that, you know, the AI native disclosure served a purpose when you guys gave it. Yeah. I forget when, how long ago it was first disclosed. Effectively, implying that, are we nearing the end of that? Is it because you think about just the broader economy now? Is that something that we should kind of be prepared to? I mean, we're saying we're, like I said, it's a made-up thing because it's a software company. It was done, one, to show that, to talk about our growth drivers. In other words, to talk about our acceleration, you'd have to talk about the growth of the AI native companies. We would say that anyway, right? That's one of the growth. There was the factor of the largest company that we wanted to make sure we helped everybody along. As everything evolved, we could have a discussion about traditional enterprise, AI native, and the larger company. Eventually, we believe that all of this is going to be part of the overall software cycle. It won't be as meaningful. We haven't given when we stop, we're going to stop disclosing. We're trying to help everybody understand the drivers of the business. Yes. That's the utility of it. We'll look at that, you know, in future quarters and see if there's utility. OK. The other thing that struck us is the acceleration in your security business, which was a long time coming as well. Yep. Can you talk, the question is like, why now? Yeah. I'm sure the answer is it's been building. Can you help us think through sort of the why now piece? Yes. I think most of the original, and it's not surprising, security revenues were created in SMBs or smaller growth companies in DevSecOps, where essentially security was much closer to development. And that's what we did. That makes sense because, you know, as you're developing your product, and that's where one of our core strengths are. I think we really didn't have the enterprise selling motion. What happened was we got on in Cloud SIEM, we got capability. We got, you know, a parity in capabilities. A lot of that had to do with the work in taking logs and making sure logs could be used economically for various use cases. We already had done an unbelievable job in observability logs. Observability logs are very close to the real-time production environment. What we did was with Flex Logs and Frozen Logs and all that, we were able to modify the product. We basically saw that in the competitive environment, someone like Splunk, and this has been from a number of different perspectives. There are a number of companies, essentially there was a whole set of workloads where you could get better visibility at a lower cost from a Datadog product. We got, I think, better over time about selling in enterprises and attaching to our logs customers. That includes essentially program management. We have been better about buyouts in exchange for long-term contracts, which has not changed our profitability. It is just spreading the discount in a different way. There are a lot of other things like migration or from Splunk Query Language that we had to conquer. I think we just got better programmatically. We're not there yet. We still have a lot more things to do. For instance, we set up a channel motion. That takes a long time. A lot of what's being created is direct. Some of it is starting to come through channel. There are a lot more things that we have that we're going to be doing or we've done that have a longer investment cycle. That's kind of what happened in security. Yeah. And, you know, SIEM seems to be a big part of that. When Ollie and the team think about the white space and kind of the shift-left mentality, DevSecOps, broadly speaking, what, you know, what sort of excites the team about future opportunities in cyber? Yeah, I mean, I think you have code security as well, where I think we're starting to do well looking at, you know, the security in the code as it gets put in production. I'd say that has tended to be a smaller market than the other two, than Cloud SIEM and cloud security. Usually, I would say developer-led market, where you start, you are the developer. Most of what we sell is into the production and the SREs. You just have a little bit of a different, you know, TAM size. I think we're going to be good at it. And we're getting fully capability there. I think that's another opportunity for us. The, you know, the thing you guys talked about it, you launched it earlier this year. You talked about it a little bit on this last earnings call. Bits AI seems to be resonating. Yep. Talk about that opportunity. How additive is it to, you know, a customer spend? And sort of like, where do you see that, you know, driving additional customer value? Yeah. Datadog has always been an aggregator of data, the organizer. You see the picture. It is very good at diagnosing where problems are. We've increasingly moved into what are you going to do about that? How are you going to root cases or make recommendations? I think AI is a great, has been and is a great opportunity for us to sort of revolutionize that. That's part of the service management suite, where you can diagnose problems more quickly and then have and then route them for resolution. Right now, we're still in private beta. I think we haven't said to everybody where we, when we're going to be in GA. We're getting a lot of pull-through, meaning we're getting a lot of customers who are using it. This is how we launch products. We are getting a lot of excitement about it. We think and their customer's paying for it. We think it is a good opportunity. We do not know yet whether it is going to be monetized through increase of win rates, staving off other entrants to the market, increased workloads, or through a SKU. We know it is important to the economics of the company. Yeah. Yeah, that's great. Yeah. The new engine flywheel of products has been really a hallmark of the company. I guess the other thing that stood out in addition to some of the broader acceleration that we talked about was new logos. Yeah. Logo ads were up dramatically. Yeah, yeah. Dramatically. Yeah, yeah. Dramatically. I mean, this I assume it's a similar answer. You've seen building momentum here. Could you put your finger on like, you know, why now? And sort of like because obviously, there's a lot of turmoil out there. Definitely a combo of successfully ramping quota capacity. So we've been able to maintain productivity, maintain strong CAC return, and ramp. I think we're landing larger. It's also a size because of the platform. You know, we're basically going in the places where I think we're better at, and we're getting more lands that are larger. Remember, we used to say that, oh, we don't try for large lands, we're bottoms up. We are bottoms up, but we also are increasingly getting more of a top-down and more of a here's the whole platform or more of the platform. It's that combination, I would say, capacity and platform adoption that are causing that, which are good things because those are things that, you know, can be sustainable. Yeah. Yeah. You know, I wanted to, I guess, sort of in the same lens, you guys have been such an organic engine over the time. And you've made some smart tuck-in deals. Yep. I think you've got about $4 billion in CAC now. Yeah. How do you think about, broadly speaking, deploying that? I mean, could as you guys get bigger, could the size of M&A deals, you know, increase? How do you kind of think about how M&A is part of the kind of the broader R&D? Yeah. I mean, at the heart, M&A for us is a part of product management. It's taking a look at the products we want to create in the platform and/or the talent needed to create it. And for the most part, we've acquired teams that have some commercial adoption but, you know, aren't going to have the platform or the go-to-market machine that we have. That has been, you know, the majority. That could get larger. It could be that companies that are a little farther along. We're open to larger acquisitions. I think we answered some rumors by saying we're not working on anything major. Yeah. Yeah. Yeah. Yeah. The bar for anything major is really high because we essentially we're not a consolidator. We don't take companies that are lagging in technology. We don't want to do that. It basically takes us off what we're concentrating on. We're open. I think that that amount of money is good to have. It's not that much money that you can't do a couple larger deals and, you know, not use parts of it. I think for the most part, the center of our acquisitions, whether it be slightly larger companies or larger companies, is really about that product map. Yeah. It's really led by product management. I assume part of the high bar means deeply and natively integrated into the platform. You do not want to necessarily have a Frankenstein model where there are different bits hanging off the side. Tech leaderships are not decaying. Modern software consistent with what Datadog. Here's another one that's kind of hard. A team that wants to stay and be part of the leadership of Datadog. That is a really important one. We're not interested in acquiring a company to make the founders rich and go away. We're interested in them being part of Datadog. Yeah. That's another bar. Those are some of the. Yeah. Yeah. You know, in terms of U.S. Fed, it could certainly be more for you guys. Yeah. How do you think, you know, when you think about FedRAMP, elevated levels there, how should we think about that as a contributor to kind of the future growth algorithm? It's small right now. I think we get asked sometimes, the government shutdown them now. Yeah. Non-shutdown. It doesn't really affect us. Yeah. I mean, we do not have that much government. So it is an opportunity. It is an opportunity that I think we are going for FedRAMP high. I think that will enable us to go into Department of Defense and others. The government does not move particularly fast. Yeah. What you call for Datadog, you need modernized software stack and cloud. You know, that's slow. I think it's a long-term opportunity and one that will be, you know, augment our business. We're certainly under-punching our weight. You know, it has a lot to do, I think, mainly with the end market. Yeah. You know, we're investing behind it. You know, we think eventually, eventually the government will modernize more. And we want to be there when it does with the right capabilities. Yeah. If you've been to the post office or driver's license, it certainly could use a. Yeah, could use. Yeah. I mean, you know, essentially, if there's we want to have it set up so that we can handle the most important government workloads, including defense, where a lot of dollars, where the budget's going. And then we have to follow, you know, the trends of what they're doing in their tech stack. Sorry. Yeah. I mean, that's right. Yeah, so that's it. I think it's an opportunity that's small for us and could be a lot bigger. We think it will be a lot bigger. It takes time. Yeah. The, you know, when we think about going into Q4, you guys are, you know, such a predictable subscription model that you don't, you know, budget flushes don't really impact you guys. They don't really impact us. When you think about how customers are thinking through kind of like AI budgets, I think one of the prior questions, you know, concerns was, you know, is there just confusion around how customers are thinking about AI dollars and deployment? Do you get a sense that customers are more comfortable now in kind of that AI spend envelope? That could help open up opportunities for increased usage of Datadog in the future? Yeah. I mean, I think we said our pipelines are strong. And they're, you know, diversified across a number of very interesting and traditional industries. Yeah, I mean, I think that I think that it will. I think that when you're talking about a business and an application, it's not all, it's not like 100, it's like no one, it's not like everyone's waiting for whatever the AI thing is. They're still doing their business. AI will be part of the application increasingly. But I think we're, you know, I think we're communicating that we're in a good buying environment. And it's due to a number of factors. Yeah. I'm going to ask you one more. And then we'll see if this is a big group. We'll just keep this a question out there. You know, in terms of you and I've had this conversation about pricing and packaging. And I think you feel that you guys have the right framework to get you to, you know, multiple billions of dollars more than you are now. And obviously, there was some talk about your largest customer and, you know, some maybe some bespoke things going on there. But do you think, you know, as you get these larger customers over time, that the pricing and packaging has to evolve for a tier of customers that are growing at a rapid pace and consuming a lot more of the product? Or do you think, no, like this is the model that will get us to, you know, customer spending, you know, $100? I think broadly speaking, it's the right package. It's volume and term discounts. I think there may be in the future, and I think we said it, different types of workloads, whether it be, let's say, on-premise workloads that need to be priced differently. You know, I think we said we're working on it. I think when you think about Frozen Logs, Flex Logs, Log Management without limits, metrics, I think this is all what you're saying, which is it's all evolving the tech stack to be able to do differentiated pricing to address this. I think that's what's been most important. We think broadly. We're working, we work on it all the time. Yeah. We have new SKUs. We have new, we divide those SKUs. We have costs delivered if you do not need the logs. You know, it is a different price. We are pretty disciplined on looking at the gross margin and making sure if we can lower the cost, if it is not gross pricing, if you do not need the logs, you know, that, you know, and we do not have to retrieve them or store whatever, then it is a different price. Yeah. We're working on that. Yeah. Great. Yeah. Is there, we may have time for one hour here. Great. Yeah. Great. Yeah. If there's anybody who has a question for David, otherwise we can keep rolling. We'll scan for logs. All right. We're being thorough here. We're being thorough. Yeah. You know, when you think about some of the building blocks for next year, both and when you think about growth and profitability, obviously, you've done a great job balancing both as the company has scaled. You know, what are some of the things that you would sort of like, you know, when you think about some of the big moving pieces? And without, you're not going to guide. I understand that. Like, when we, how should we, you know, think about the evolution of this model as you continue to scale? And profitability matters. But also, you know, you're seeing growth inflect at the same time. Definitely. It is bottoms up. In terms of go-to-market investment, we're looking at territories and target accounts. We're looking at accounts that could buy Datadog. You know, we're sort of making bottoms up investment. I would say we're not saturated. We still have lots of accounts that could use additional coverage. We're looking at the go-to-market in a holistic way, whether that be enterprise marketing or channel investment and things like that. Looking at data centers. That would be, do we have enough of a TAM in a market that needs data residency? We look at that. When it gets to R&D, we're looking at what the platform investments and product investments are and how we have to sort of staff them. That's how we're sort of balancing all that. You know, I think as to remind everybody, we've given a long-term EBIT target of 25%. As you know, we've been past it. We've been under it. But, you know, essentially, you know, we've been in the low 20s to the mid-20s overall. And we use that discipline to help us to calibrate the level of investment. It is definitely easier as you get scale. You can put a lot of money to work productively because, for instance, you know, in R&D, 30% of our ARR is over $1 billion. Yeah. It's a lot of money. Yeah. I think it becomes increasingly the dollars are there. The impetus is increasingly on what to put them behind. Yep. Maybe then just to wrap, when you, you know, you and Ali and the executive team sit around and you think about some of these, you know, the big, big opportunities for Datadog, you're going to sit here and say, this is a moonshot for Datadog. Yeah. I mean, do you have any sort of like bold, like not financially, but just like longer-term predictions on, you know, wow, this is. Yeah. Our vision is to have a platform that addresses our core user group and all of those around it with increasing functionality. I think service management is an example of that. You actually move towards resolution in the platform. I think security is an example of that where you get additional buyer groups or user groups so that you do what we've been doing along, which is you expand the TAM by having more and more parties in the platform using the platform and creating value. I think service management, security, the AI, the AI, the Bits AI, and making the platform AI and covering AI workloads are probably at the top of the list. Maybe I said security and classic. Yeah. At top of the list of what could be breakout opportunities for us. Cool. Well. Yeah. Out of time, unfortunately. Yeah. Thank you. David, from all of us. Thank you. Thank you everybody for coming. Thanks for coming. Really appreciate it. OK. Thank you. Thanks. Thanks. Thanks. Thanks. Awesome. Good job.