$DDOG Datadog Q3 2024 Earnings Conference Call

Thanks, Yuka, and thank you all for joining us this morning. We are pleased to report a strong Q3 as we continue to execute against our goals to help our customers grow faster, safer, and more efficient as they modernize their applications. We kept broadening our platform in observability and beyond, including in AI, where interest continues to rise, and we added new customers while expanding with existing ones as they grow into the cloud. Let me start with a review of our Q3 financial performance. Revenue was $690 million, an increase of 26% year-over-year and above the high end of our guidance range. We ended the quarter with about 29,200 customers, up from about 26,800 a year ago. We had about 3,490 customers with ARR of $100,000 or more, up from about 3,130 a year ago, and these customers generated about 88% of our ARR. We generated free cash flow of $24 million, with a free cash flow margin of 30%. Turning to platform adoption, our platform strategy continues to resonate in the market. As of the end of Q3, 83% of customers were using two or more products, up from 82% a year ago. 49% of customers were using four or more products, up from 46% a year ago. 26% of our customers were using six or more products, up from 21% a year ago. And 12% of our customers were using eight or more products, up from 8% a year ago. We continue to execute on growth across the three pillars of observability, and we are pleased to report that infrastructure monitoring, APM suite, and log management together represent more than $2.5 billion in ARR. As a reminder, within the APM suite we include core APM, synthetics, real user monitoring, and continuous profiler. We also want to call out our newer products, which are increasingly contributing to our business. Of our 23 products, 15 now exceed $10 million in ARR. These include our cloud security product, as well as CI visibility and cloud cost management. So we have many products beginning to contribute to our revenue growth, and we're continuing to build greater capabilities within those products for our customers. Now let's discuss this quarter's business drivers. Overall, the business environment for Datadog has remained stable and similar to what we have seen throughout 2024. Our customers overall are growing their cloud usage, while some are continuing to be cost conscious. In Q3, we continue to see existing customer usage growth broadly in line with our expectations. Our usage growth with existing customers continued to be higher than in the year-ago quarter, and we saw healthy growth across our product lines, with newer products growing faster and more mature products off a smaller base. Finally, churn continues to be low, and gross revenue retention was stable in the mid-90s, highlighting the mission-critical nature of our platform for our customers. Moving on to R&D, in the AI space, customers continue to experiment with new AI technologies, and as they do, they want to get visibility into their AI use. At the end of Q3, about 3,000 customers used one or more Datadog AI integrations to send us data about their AI, machine learning, and LLM usage. As some of these experiments start turning into production AI applications, we are seeing initial signs of traction for our LLM observability product. Today, hundreds of customers are using LLM observability, with more exploring it every day, and some of our first paying customers have told us that they have cut the time spent investigating LLM latency, errors, and quality from days or hours to just minutes. Our customers don't only want to understand the performance and cost of their LLM applications; they also want to understand the LLM model performance within the context of their entire application, so they are using APM alongside LLM observability to get fully integrated end-to-end visibility across all their applications and tech stacks. Meanwhile, we continue to work to make the Datadog platform the best place for customers to monitor, secure, and take action on their systems, no matter where they deploy. In September, we launched Datadog monitoring for Oracle Cloud Infrastructure for general availability. With this launch, our customers can get visibility into their OCI stack and manage in real time the performance of OCI cloud services, servers, VMs, databases, containers, and apps in Datadog, and customers can now unify their monitoring across OCI, other clouds, and on-prem environments. We also continue to extend our platform in new ways to bring value to our customers. At our Dash conference this summer, we announced Datadog On-Call, our newest product in the cloud service management space. As you know, our customers use Datadog extensively during their workdays for alerting and troubleshooting, whether that's for observability or security use cases. Now with Datadog On-Call, we are bringing a modern paging experience directly into our unified platform, and we now offer a completely integrated solution that covers incidents from end to end, from detection, alerting, and paging to incident management, troubleshooting, and resolution. Even though On-Call is still in limited availability, we are already seeing very strong reception for the product, and we are beginning to see customers request On-Call as part of their deals. In particular, new customers are interested in including paging as part of their land with Datadog. So we're working hard to deepen and broaden our platform, and our innovations are rightfully being recognized by independent research firms. We're pleased to see that for the fourth year in a row, Datadog has been named a Leader in the 2024 Gartner Magic Quadrant for Observability Platforms. We believe that this validates our approach to deliver a unified platform which breaks down silos across teams. And Datadog has also been named a Leader in Gartner's very first Magic Quadrant for Digital Experience Monitoring, which includes Datadog products across synthetic testing, real user monitoring, product analytics, session replay, and error tracking. Now let's move on to sales and marketing. Our sales team continued to execute this quarter, and we added some exciting new customers while expanding with many more. So let's go through a few examples. First, we landed a seven-figure annualized deal with a leading e-commerce company in India. With its previous observability vendor, the customer saw quickly increasing costs while lacking the enterprise-grade observability they needed. By switching to Datadog, they expect to support their scaling goals and will rely on our tracing, granular profiling, and cloud integration support. I will note that we're pleased to have landed a large new logo customer in India, and we are continuing to invest to grow our presence and our opportunities there. Next, we signed a six-figure annualized land with a major US federal agency. This agency is beginning to move some of its workloads to the cloud and is expanding the services it offers to every single US citizen through cloud applications. They have chosen Datadog to observe and secure their cloud environment and deliver a faster, better experience to their users. This deal includes eight products on the Datadog cloud, including Cloud SIEM and Cloud Security Management. Next, we landed a seven-figure annualized deal with a large American financial services company. This customer has a very seasonal business and experiences dozens of major incidents during the annual peak season, with an average downtime per incident of about five hours, and they estimate millions of dollars of lost revenue for each hour of downtime. By replacing its clock providers' monitoring tools with Datadog, and in particular using our real user monitoring product, this customer targets substantial reductions in downtime. They are starting with five Datadog products and now trialing network monitoring, database monitoring, cloud security, and cloud cost management products as they look to consolidate dozens of homegrown and commercial tools. Next, we signed a seven-figure annualized expansion with a major airline in Europe. This customer has adopted Datadog for its customer-facing website. They are now moving hundreds of applications from on-prem to AWS, and they want to derisk their cloud migration. They estimate that each incident can cost tens of millions of dollars in lost revenue and customer impact. By using Datadog across five products, this customer expects to significantly improve mean time to resolution, and they have already seen progress in that respect during their evaluation period with Datadog. Next, we signed a seven-figure annualized expansion with a division of a hyperscaler delivering next-gen AI models. This customer is very technically capable and already has a homegrown observability solution which requires time-consuming customization and manual configuration. They will be launching new features for their large language model soon and need a platform that can scale flexibly while supporting proactive incident detection. By expanding their use of Datadog, they expect to efficiently onboard new teams and environments and support the rapidly increasing adoption of their LLMs. Next and last, we signed a seven-figure annualized expansion with a leading online food delivery company in Latin America. Before Datadog, this customer suffered from excessive alerting noise, siloed teams, and lack of visibility, with each minute of downtime resulting in thousands of lost orders. By using Datadog, this customer has experienced meaningful reductions in mean time to resolution and false alerts, while saving on hard costs in their cloud environment. This customer is expanding to 10 products in the Datadog platform. And that is it for another productive quarter for our go-to-market. Now let me say a few words on a longer-term outlook. Overall, we continue to see no change to the multi-year trends towards digital transformation and cloud migration, which we continue to believe are still in early days. We are seeing continued experimentation with new advances such as AI, and we believe this is one of the many factors that will drive greater use of the cloud and other modern technologies. So we are helping our customers every day to observe, secure, and act on their business-critical applications and workloads. With that, I will turn it over to our CFO, David.

I mean, this is definitely a very interesting period, to say the least. We see tons of innovation across the customer base, still largely more around experimenting and testing new applications. Though as we reported, we are seeing some customers move into production, and we are seeing our production-minded LLM product, for example, being used by real paying customers with real volumes and real applications in real production workloads. So that's exciting and healthy. I think it's a great trend for the future. In general, in terms of the workflows, you're right that we're starting to see more inference workloads, but they still tend to be more concentrated across a number of API-driven providers, so there are a few others both on AWS and other kinds of models. So this is where I think most of the usage in production at least is today. We expect that to diversify more over time as companies get further into production with their applications and they start customizing more on their own models.

Yeah, so on the security side, I think there's quite a bit of focus right now on Cloud SIEM, as we see a number of very exciting opportunities there. I think when you look at the landscape competitively, what other companies are using, because most companies have a SIEM already at least for some part of their business, I think there's a very interesting opportunity for us there. The product is mature enough to win in best-of-breed situations across the existing product. So we are making quite a bit of a push, and we're still investing quite a bit in the rest of the platform to bring all the products together to be a unified platform for security. But I would say the tip of the spear for this quarter is Cloud SIEM. I think there's a very specific opportunity there. On the service management side, we actually see very exciting trends from customers. We mentioned on the call our On-Call product, which puts us directly into the paging loop and the start of many incidents. The product is getting stronger reception than we initially thought, to the point where customers are basically claiming to buy even though it's still in preview. So we feel good about handling the full loop for them, starting with when we detect something in observability all the way to full incident resolution and automation of a big part of it. So we think On-Call can be a bit of a watershed for us to do quite a bit more on that. So we're also excited and we're doing what we can to accelerate the roadmap there because we think there's a very good opportunity. We have a few other building blocks on the service management side that we released over the past year, and they're all growing quite nicely from small bases today. But we're very excited in the way they all come together to form an integrated platform where we can fully automate resolution for our customers. So that's again something we're building largely from those building blocks, but something that we think is going to be very exciting in the quarters and years to come.

There's always been innovation and new things. You're right that at least for the deals we talk about in the earnings, there tends to be more consolidation, but that's the nature of it. Innovation typically happens gradually as opposed to being a big bang, like a customer switching $5 billion from five vendors into another vendor. So we talk about these less on calls, but that's been happening throughout. We definitely see room for a lot more consolidation moving forward, both in terms of existing customers and new logos. And at the same time, we are excited to see what's happening with AI innovation as it gets further down the pipeline, away from testing and experimenting and more into production applications. We have some signs that it's starting to happen. We see that with our LLM observability product, we see that also with some of the workloads we monitor from our customers on the infrastructure side. But I would say it's still very early days in terms of customers being in production with their AI applications.

Yeah, I mean, the key thing to remind everyone of is we think this general move of workload growth in the cloud or cloud-like environments — might be public cloud-based, but they might also be private clouds — this growth and move to the cloud is going to last for a very long time and it's going to be at a fairly high level for a very long time. So you're right that when we look at the numbers from the hyperscalers and when we try to factor out what GPU-driven growth is, it looks stable-ish. We think that growth is still high and is going to last for a very long time, and that's one of the big underpinning trends that we're going to ride for the years to come. Now on the edge of that, you're right that the workloads could have grown maybe instead of growing 20%, they could grow 25%. Maybe some of that 5% instead is being invested both in terms of infrastructure budget or innovation time and innovation budget, all that is going into AI, and that's largely right now in experimentation and model training and that sort of thing. That's probably right. But we see that also as a precursor to further workload growth in the future, like more traditional production application workload growth. So for us, it doesn't change the equation. It does create a bit of a decorrelation between our numbers and the numbers you might see reported from the hyperscalers, where a lot of their short-term growth rides more on the capacity of GPUs they bring online for those experimentation and training workloads.

I can go GPU. So look, there are two parts of it. There is what can you do for GPU that's different from a CPU. I think in general, there are quite a few things that are the same, in terms of understanding how that machine sits with the rest of the application, or code system performance, that sort of stuff. And there's quite a bit that's new and differentiated around profiling in general and understanding how you maximize the usage of the GPU bandwidth, which is usually what it's about. I would say right now we're working with a number of customers that have real-world large inference workloads on how we can help on the GPU profiling side for inference. We're doing less on the training side, mostly because the training jobs tend to be more bespoke and temporary, and there's less of an application that's attached to those. These are just very large clusters of GPUs, so it's closer to HPC in a way than it is to traditional applications. Though we are also experimenting with what we can do there. There is a world where maybe in a durable fashion, 60% of workloads are inference and 40% are training. If that's the case, there's going to be a lot of value to be had by having repeatable training and repeatable tuning for that. So we are also looking into that. Today, as of today, we really don't monetize GPU instances all that well compared to CPU instances. A GPU instance is many times the cost of a CPU instance, and we charge the same amount for it. That doesn't have to be the case in the future if we do things that are particularly interesting, if we can have a real impact and deliver value to help customers make the best of their GPUs and in the end save money. But today, that's the current situation. And to your other question, in the period since the closing of Q3, we've seen very similar trends to the year-to-date in Q3, in terms of customer growth, strength in enterprise, stability in SMB, and we have a strong pipeline for Q4 that we're working hard on harvesting as we get towards year-end.

And as to the fourth quarter, I echo the comments we make overall, which is we take conservative assumptions relative to usage growth that are lower than we've seen in providing our guidance. Of course, where we are in the year, the effect on Q4 is more limited than it would be on next year. And as we report Q4 and give guidance, we will update everybody on any trends that we do see in this cohort at that time. We didn't change guiding principles for this. We made an extra comment on it because we want to be transparent. We've seen that, and we see some customers are well over their commits, but we didn't bake anything specific in the guidance.

Look, it's a huge opportunity for us, and we are quite early with some interesting successes. We have some exciting logos there, like this one we mentioned this quarter, which is an agency we all interact with and which has tremendous opportunity for growth and upsell with us. There's another agency that already has a very large multi-million dollar ACV footprint with Datadog that we didn't talk about this quarter, but that's been a long-term customer. So there's plenty of opportunity. We're still very early in terms of capacity building, channel building, and go-to-market in general in government. So I would say still a small part of overall business, but we see that there's tons of upside in it, and we're working hard on the product side so that we can capture that fully. Over the past years, we worked on FedRAMP compliance, and we are working further on getting into more regulated, even tougher to get into workloads with FedRAMP and IL5 and other certifications like that. So we have a long roadmap there and big plans, and a lot of upside.

Yeah, so on gross margins, in general we're happy with where they are, and there are some small moves in it. I wouldn't read too much into the moves. There are a number of things that underpin that. We keep releasing new functionality at the same time we keep optimizing our code and our usage. We actually use our products to optimize that quite a bit, and then we also keep getting better and better agreements with our cloud providers as we scale. So the combination of all that is what you see in the gross margin number. In general, we have to make a call always on whether we direct more effort at building new functionality or optimizing. The way we manage that is when gross margins get a little bit low, we put more effort on optimizing, and when we are in a happy zone, we redirect more effort on new products and new functionality. There's not a lot of change from product mix; it doesn't really matter all that much from a gross margin perspective. So there's no particular worry there. We think long term there are plenty of opportunities to improve margins, but right now the focus is on really shipping enough products to enough customers, making sure those products provide them with as much value as possible while staying in a certain happy zone on the gross margin. In terms of the growth of workloads, look, we see growth across the customer base pretty much with the growth of classical workloads in the cloud. We see large growth on the AI side. We think that the one big catalyst for future acceleration will be those AI-native applications, or those AI applications going into production for non-native companies, for a much broader set of customers than the customers that are deploying this kind of application in production. And as they do, they will also look less like just large clusters of GPUs and more like traditional applications, because the GPU needs a database, it needs an application in front of it, it needs layers to secure it, authorize it, and all the other things. So it's going to look a lot more like a normal application with some additional more concentrated computing on GPUs.

Look, we'll take it where the demand takes us. We initially built it as a way for customers within our ecosystem to have a fully integrated experience, and really as a stepping stone towards full automation of incident resolution, full ownership from end to end. The part that strategically is the most interesting to us is the automation of the resolution, not physically paging customers. That being said, the response from customers has been so strong that there is very high demand for integrating with many different other sources and plugging ourselves into incident resolution loops that we might not have been a part of before. So that's definitely something that we're going to build for customers, and we're just very happy to see the demand there.

Well, I again don't want to single out any product for which we didn't disclose metrics in particular. But look, we mentioned in previous calls there are products that are growing very fast that we think will reach scale. We talked about database monitoring, for example, as a product that has been growing very fast, very clear value. And we actually forgot which number we disclosed last time, but we did disclose a number for it. So this one is clearly headed for north of $50 million. There are a number of other products across security, user experience, that are definitely going to get there very soon. So we feel good about all that. Basically, pretty much every single one of those products should be about $50 million. Some of them are going to get there faster than others. Some of them will cross $100 million, some of them will cross a billion maybe. So I think we feel good about the product set.

Look, I think the only thing I would say is I won't get into second-guessing or guidance or things like that. In general, it's also very hard to guess usage ahead of time, because that's actually very different or can be very different from the intents that are being manifested by customers or their understanding of what the next year is going to look like. But the one thing I will say is we're investing. We're building sales capacity, we're definitely investing heavily in engineering. I think it's a great investment in the industry. Unlike many others, we don't expect at this point to have outsized investments in compute. We're not building large GPU clusters, but we are building engineering capacity and we are building sales capacity. So we should expect that in the numbers we will give for next year.

Well, thank you all for joining the call. I want to give a few shout-outs. First, to the product and engineering team for building great products this quarter. To the customers that actually spent all this time with us on getting those products to work right, especially for the new products — I'm talking about LLM observability, for example, or On-Call — I know we spend a lot of time with customers to get those right. And I also want to give a special shout-out to the go-to-market team. We have a very, very loaded fourth quarter, a very full slate for everyone in the next month and a half, and I know everybody is super hard at work. So thank you everyone. And on this, I think we'll wrap the call. Thank you for your participation.