Datadog, Inc. Q4 FY2025 Earnings Call

Good day, and welcome to the Q4 2025 Datadog Earnings Conference Call. At this time, all participants are in a listen-only mode. After the speakers' presentation, there will be a question and answer session. If your question has been answered and you would like to remove yourself from the queue, please press 11 again. As a reminder, this call may be recorded. I would now like to turn the call over to Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead.

Thank you, Michelle. Good morning, and thank you for joining us to review Datadog's fourth quarter 2025 financial results, which we announced in our press release issued this morning. Joining me on the call today are Olivier Pomel, Datadog's Co-Founder and CEO, and David Obstler, Datadog's CFO. During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the first quarter and fiscal year 2026, and related notes and assumptions, our product capabilities, and our ability to capitalize on market opportunities. The words anticipate, believe, continue, estimate, expect, intend, will, and similar expressions are intended to identify forward-looking statements or similar indications of future expectations. 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 a discussion of the material risks and other important factors that could affect our actual results, please refer to our Form 10-Q for the quarter ended 09/30/2025. Additional information will be made available in our upcoming Form 10-K for the fiscal year ended 12/31/2025 and other filings with the SEC. This information is also available on the investor relations section of our website along with a replay of this call. We will discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial measures in the tables in our earnings release, which is available at investors.datadoghq.com. With that, I would like to turn the call over to Olivier.

Thank you, Yuka, and thank you all for joining us this morning to go over what was a very strong Q4 and overall a really productive 2025. Let me begin with this quarter's business drivers. We continue to see broad-based positive trends in the demand environment. With the ongoing momentum of cloud migration, we experienced strength across our business, across our product lines, and across our diverse customer base. We saw a continued acceleration of our revenue growth. This acceleration was driven in large part by the inflection of our broad-based business outside of the AI-native group of customers we discussed in the past. And we also continue to see very high growth within these AI-native customer groups as they go into production and grow in users, tokens, and new products. Our go-to-market teams executed to a record $1.63 billion in bookings, up 37% year over year. This included some of the largest deals we have ever made. We signed 18 deals over $10 million in TCV this quarter, of which two were over $100 million, and one was an eight-figure land with a leading AI model company. Finally, churn has remained low, with gross revenue retention stable in the mid to high nineties, highlighting the mission-critical nature of our platform for our customers. Regarding our Q4 financial performance and key metrics, revenue was $953 million, an increase of 29% year over year, and above the high end of our guidance range. We ended Q4 with about 32,700 customers, up from about 30,000 a year ago. We also ended Q4 with about 4,310 customers with an ARR of $100,000 or more, up from about 3,610 a year ago. These customers generated about 90% of our ARR. And we generated free cash flow of $291 million with a free cash flow margin of 31%. Turning to product adoption, our platform strategy continues to resonate in the market. At the end of Q4, 84% of customers used two or more products, up from 83% a year ago. 55% of customers used four or more products, up from 50% a year ago. 33% of our customers use six or more products, up from 26% a year ago. 18% of our customers use eight or more products, up from 12% a year ago. As a sign of continued penetration of our platform, 9% of our customers use 10 or more products, up from 6% a year ago. During 2025, we continued to land and expand with larger customers. As of December 2025, 48% of the Fortune 500 are Datadog customers. We think many of the largest enterprises are still very early in their journey to the cloud. The median Datadog ARR for our Fortune 500 customers is still less than half a million dollars, which leaves a very large opportunity for us to grow with these customers. So we are landing more customers and giving more value, and we also see that with the ARR milestones we are reaching with our products. We continue to see strong growth dynamics with our core three pillars of observability: infrastructure monitoring, APM, and log management. As customers are adopting the cloud, AI, and modern technologies, today, infrastructure monitoring contributes over $1.6 billion in ARR. This includes innovations that deliver visibility and insights across our customers' environments, whether they are on-prem, virtualized servers, containerized hosts, serverless deployments, or parallelized GPU fleets. Meanwhile, log management is now over $1 billion in ARR. This includes continued rapid growth with FlexLogs, which is nearing $100 million in ARR. And our third pillar, the end-to-end suite of APM and DEM products, also crossed $1 billion in ARR. This includes an acceleration of our core APM product into the mid-thirties percent year over year, currently our fastest-growing core pillar. We have now enabled our customers with the easiest onboarding and implementation in the market while delivering unified deep end-to-end visibility into the application. Now remember that even with these three pillars, we are still just getting started, as about half of our customers do not buy all three pillars from us, or at least not yet. Moving on to R&D and what we built in 2025, we released over 400 new features and capabilities this year. That's too much for us to cover today, but let's go over just some of our innovations. We are executing relentlessly on a very ambitious AI roadmap, and I will split our AI efforts into two buckets: AI for Datadog and Datadog for AI. So first, let's look at AI for Datadog. These are AI products and capabilities that make the Datadog platform better and more useful for customers. We launched the AI SRE agent for general availability in December to accelerate root cause analysis and incident response. Over 2,000 trial and paying customers have run investigations in the past month, which indicates significant interest and showed great outcomes with BCAI. Sorry. And we are well on our way with DeepAI DevAgent, which detects code-level issues, generates fixes with production context, and can even help release the monitor fix. And BigAI Security, which autonomously charges SIEM signals, conducts investigations, and delivers recommendations. The Datadog MCP server is being used by thousands of customers in preview. Our MCP server responds to AI agent and user prompts and uses real-time production data and rich data context to drive troubleshooting, root cause analysis, and automation. And we are seeing explosive growth in MCP usage, with the number of tool calls growing 11-fold in Q4 compared to Q3. Second, let's talk about Datadog for AI. This includes capabilities that deliver end-to-end observability and security across the AI stack. We are seeing an acceleration in growth. Over 1,000 customers are using the product, and the number of spans sent has increased 10 times over the last six months. In 2025, we broadened this product to better support application development and integration, adding capabilities such as experiments, LLM playground, LLM analysis, and custom as a judge. And we will soon release our AI Agents console to monitor usage and adoption of AI agents and coding assistance. We are working with design partners on GPU monitoring, and we are seeing GPU usage increase in our customer base overall. And we are building into our products the ability to secure the AI stack against prompt injection attacks, model hijacking, and data poisoning, among many other risks. Overall, we continue to see increased interest among our customers in Next Gen AI. Today, about 5,500 customers use one or more Datadog AI integrations to send us data about their machine learning, AI, and LLM usage. In 2025, our observability platform delivered deeper and broader capabilities for our customers. We reached a major milestone of more than 1,000 integrations, making it easy for our customers to bring in every type of data they need and engage with the latest technologies, from cloud to AI. In node management, we are seeing success with our consolidation motion. During 2025, we saw an increasing demand to replace a large legacy vendor with significant success in nearly 100 deals for tens of millions of dollars of new revenue. And we improved log management with notebooks, reference tables, log patterns, calculated fields, and an improved lifestyle among many other innovations. We launched data observability for general availability. Data is becoming even more critical in the AI era. With data availability, we are enabling end-to-end visibility across the entire data life cycle. We launched storage management last month, providing granular insights into cloud storage and recommendations to reduce spend. We delivered Kubernetes auto-scaling, enabling users to quickly identify which over-provisioned clusters and deployments to right-size. In the digital experience monitoring area, we launched product analytics to help product designers make better design decisions with clear data about user experience and behavior. We also delivered run without limits, giving front-end teams full visibility into user traffic and performance and dynamically choosing the most useful sessions to retain. In security, we are seeing increasing traction as we displace existing market-leading solutions with cloud SIEM in logs to private. This year, our engineers shipped many new capabilities, including tripling the number of content packs in the product. Most importantly, we’ve tightly integrated the Bit.ai security agent, which has already shown promise as a strong differentiator in the market. We launched code security, enabling customers to detect and remediate vulnerabilities in their code and open-source libraries, from development to production. We continue to advance our cloud security offering, adding infrastructure as code (IAC) security, which helps detect and resolve security issues with Terraform. We launched our security graph to identify and evaluate attack paths. In software delivery, in January, we launched feature flags that combine with our real-time observability to enable Canary rollouts so teams can deploy new code with confidence. We expect them to gain importance in the future, as they serve as a foundation for automating the validation and release of applications in an AI-enabled development world. We are also building out our internal developer portal, which includes software catalog and scorecards, to help developers navigate infrastructure and application complexity, provide rich context for AI development agents, and ultimately enable a faster release cadence. In cloud service management, we launched on-call, supporting over 3,000 customers with their incident response processes. I already mentioned the AI SRE agent, which pairs with on-call to accelerate our customer incident resolution. As you can tell, we have been very busy, and I want to thank our engineers for a highly productive 2025. Most importantly, I am even more excited about our plan for 2026. So let's move on to sales and marketing. I want to highlight some of the great deals we closed this quarter. First, we landed an eight-figure annual deal, our biggest new logo deal to date with one of the largest AI financial model companies. This customer had a fragmented observability stack and cumbersome monitoring workflows leading to poor productivity. This is a consolidation of more than five open-source, commercial, hyperscaler, and in-house observability tools into the unified Datadog platform. This change has returned meaningful time to developers and enabled a more cohesive approach to observability. This customer is experiencing very rapid growth. Datadog allows them to focus on product development, supporting their users, which is critical to their business success. Next, we welcome back a customer, a European data company, in a nearly seven-figure annualized deal. This customer's log-focused observability solution had poor user experience and integrations, which led to limited user adoption and gaps in coverage. By returning to Datadog and consolidating seven observability tools, they expect to reduce tooling overhead and improve engineering productivity with faster incident resolution. They will adopt nine Datadog products as stock, including some of our newer products, such as FlexLog, observability pipeline, self-cost management, data observability, and on-call. Next, we signed an eight-figure annualized expansion with a leading e-commerce and digital payments platform. These customers' products have an enormous reach in commercial APM solutions but had scaling issues, lacked correlation across silos, and had a pricing model that was difficult to understand or predict. With this extension, they are standardizing on Datadog APM using OpenTelemetry so their teams can correlate metrics, tracing, and logs to detect and resolve issues faster. They have already seen meaningful impact, estimating a 40% reduction in resolution times. This customer has adopted 17 products across the Datadog platform. Next, we signed a seven-figure annualized expansion for an eight-figure annualized deal with a Fortune 500 food and beverage retailer. This long-time customer uses the Datadog platform across many products but still has over 30 other observability tools and embarked on consolidating for cost savings and better outcomes. With this expansion, Datadog log management and flex logs will replace the legacy logging product for all ops use cases, with expected annual savings in the millions of dollars. This customer is expanding to 17 Datadog products. Next, we signed a seven-figure annualized expansion with a leading healthcare technology company. This company was facing reliability issues that impacted clinicians during critical workflows and put customer trust at risk. The customer will consolidate six tools and adopt seven Datadog products, including LLM observability to support their AI initiative, as well as Big AI SRE agents to further accelerate incident response. Next, we signed an eight-figure annualized expansion, more than quadrupling the annualized commitment, with a major Latin American financial services company. Given its successful tool consolidation projects and rapid adoption of Datadog products across all its teams, the customer renewed only with us while expanding to additional products, including data observability, CI visibility, database monitoring, and observability pipelines. With Datadog, this customer has shown measurable improvements in cost efficiency, customer experience, and conversion rates across multiple lines of business. This proof of value led them to broaden their commitment with us and firmly establish Datadog as their mission-critical observability partner. Last but not least, we signed a seven-figure annualized expansion for an eight-figure annualized deal with a leading fintech company. With this expansion, the customer is moving their log data onto our unified platform, so teams can correlate telemetry in one place and save between hours and weeks in time to resolution for incidents. This customer has obtained 19 Datadog products across the platform, including all three pillars, visual experience, security, software delivery, and service management. And that's it for our wins. Congratulations to our entire go-to-market team for a great 2025 and a record Q4. It was inspiring to see the whole team last month and really exciting to embark on a very ambitious 2026. Before I turn it over to David for the financial review, I want to say a few words on our longer-term outlook. There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. We continue to extend our platform to solve our customers' problems from end to end across their software development, production, data stack, user experience, and security needs. Meanwhile, we are moving fast in AI by integrating AI into the Datadog platform to improve customer value and outcomes, and by building products to observe, secure, and act across our customers' AI stacks. In 2025, we executed very well and delivered for our customers against their most complex mission-critical problems. Our strong financial performance is the result of that effort. We are even more excited about 2026 as we are starting to see an inflection in AI usage by our customers within applications. As our customers begin to adopt AI innovation, such as the Big AI SRE agent, to hear about all that in detail and much more, I welcome you all to join us at our next Investor Day this Thursday in New York between 1 and 5 PM. I'll be joined by our product and go-to-market leaders, sharing how we are serving our customers, how we innovate to broaden our platform, and how we are delivering better value with AI. For more details, refer to the press release announcing the event or head to investors.datadoghq.com. And with that, I will turn it over to our CFO, David.

Thanks, Olivier. Our Q4 revenue was $953 million, up 29% year over year, and up 8% quarter over quarter. Now to dive into some of the drivers of our Q4 revenue growth. First, overall, we saw robust sequential usage growth from existing customers in Q4. Revenue growth accelerated with our broad base of customers, excluding the AI natives, to 23% year over year, up from 20% in Q3. We saw strong growth across our customer base, with broad-based strength across customer size, spending bands, and industries. We have seen this trend of accelerated revenue growth continue in January. Meanwhile, we are seeing continued strong adoption among AI-native customers, with growth that significantly outpaces the rest. We see more AI-native customers using Datadog, with about 650 customers in this group. These customers are also rapidly growing with us, including 19 customers spending $1 million or more annually with Datadog. Among our AI customers are the largest companies in this space. As of today, 14 of the top 20 AI-native companies are Datadog customers. Next, we also saw continued strength from new customer contributions. Our new logo bookings were very strong again this quarter, and our go-to-market teams converted a record number of new logos. Average new logo land sizes continue to grow strongly. Regarding retention metrics, our trailing twelve-month net revenue retention percentage was about 120%, similar to last quarter. Our trailing twelve-month gross revenue retention percentage remains in the mid to high nineties. Now moving on to our financial results. First, billings were $1.21 billion, up 34% year over year. Remaining performance obligations or RPO was $3.46 billion, up 52% year over year. Current RPO growth was about 40% year over year. RPO duration increased year over year as the mix of multi-year deals increased in Q4. We continue to believe revenue is a better indication of our business trends than billing and RPO. Now let's review some of the key income statement results. Unless otherwise noted, all metrics are non-GAAP. We have provided a reconciliation of GAAP to non-GAAP financials in our earnings release. First, our Q4 gross profit was $776 million with a gross margin percentage of 81.4%. This compares to a gross margin of 81.2% last quarter and 81.7% in the year-ago quarter. Q4 OpEx grew 29% year over year, versus 32% last quarter and 30% in the year-ago quarter. We continue to grow our investments to pursue our long-term growth opportunities, and this OpEx growth is an indication of our successful execution on our hiring plans. Our Q4 operating income was $230 million or a 24% operating margin compared to 23% last quarter and 24% in the year-ago quarter. Now turning to the balance sheet and cash flow statements. We ended the quarter with $4.47 billion in cash, cash equivalents, and marketable securities. Cash flow from operations was $327 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $291 million for a free cash flow margin of 31%. Now for our outlook for the first quarter and the full fiscal year 2026. Our guidance philosophy overall remains unchanged. As a reminder, we base our guidance on trends observed in recent months and apply conservatism on these growth trends. For the first quarter, we expect revenues to be in the range of $951 to $961 million, which represents a 25% to 26% year-over-year growth. Non-GAAP operating income is expected to be in the range of $195 to $205 million, which implies an operating margin of 21%. Non-GAAP net income per share is expected to be in the $0.49 to $0.51 per share range, based on approximately 367 million weighted average diluted shares outstanding. For the full fiscal year 2026, we expect revenues to be in the range of $4.06 to $4.1 billion, which represents 18% to 20% year-over-year growth. This includes modeling within our guidance that our business, excluding our largest customer, grows at least 20% during the year. Non-GAAP operating income is expected to be in the range of $840 million to $880 million, which implies an operating margin of 21%. Non-GAAP net income per share is expected to be in the range of $2.08 to $2.16 per share, based on approximately 372 million weighted average diluted shares. Finally, some additional notes on our guidance. First, we expect net interest and other income for the fiscal year 2026 to be approximately $140 million. Next, we expect cash taxes in 2026 to be about $30 million to $40 million, and we continue to apply a 21% non-GAAP tax rate for 2026 and beyond. Finally, we expect capital expenditures and capitalized software together to be in the 4% to 5% of revenue range in fiscal year 2026. To summarize, we are pleased with our strong execution in 2025. Thank you to the Datadog teams worldwide for a great 2025. I am very excited about our plans for 2026. Finally, we look forward to seeing many of you on Thursday for our Investor Day. With that, we will open up our call for questions.

Thank you. Our first question comes from Sanjit Singh with Morgan Stanley. Your line is open.

Thank you for taking the questions and congrats on a strong close to the year and a successful 2025. Olivier, I wanted to get your updated views in terms of where observability is headed in the context of a lot of advancements when it comes to agentic frameworks, agentic deployments, and the stuff that we have seen from Anthropic and new frontier models from OpenAI. Just in terms of, like, what this means for observability as a category, the visibility of it, in terms of, can customers use these tools to build homegrown solutions for observability. Just get your latest comments on the defensibility of the category and how Datadog may potentially have to evolve in this new sort of agentic era.

Yeah. I mean, look. There are a few different ways to look at it. One is there's going to be many more applications than there were before. People are building much more, and they're building much faster. We have covered that in previous calls, but we think that this is nothing but an acceleration of the increase of productivity for developers in general, allowing them to build a lot faster. As a result, you create a lot more complexities because you build more than you can understand at any point in time. You move much of the value from the act of writing the code, which now you do not own anymore, to validating, testing, ensuring it works in production, making sure it's safe, and verifying it interacts well with the rest of the world and the end users, ensuring it does what it's supposed to do for the business, which is where we come in with observability. So we see a lot more volume there, and we identify this as an area where observability can help. The other part that's interesting is that a lot more happens within these agents and applications, and much of our human effort is now aligning with observability. We are here to understand, to make sure the machines are behaving in accordance with our expectations and that the outputs match what we anticipated when we began. We believe this trend will expand the definition of observability to domains that did not necessarily cover it before. Thus, we see these developments as accelerants, and we feel observability's role in correlating code, applications, and user experiences is increasingly important in the current AI development lifecycle.

And maybe just one follow-up on that line of thinking. In a world where there's a greater mix between human SREs and agentic SREs, is there any sort of evolution we need to think about in terms of UI or how workflows work in observability and how, maybe Datadog sort of aligns with that evolution that's likely to come in the next couple of years?

Yeah. Because there's going to be an evolution, that's certain. There will be a lot more automation, as we see today. The signs point to everything moving faster. We are experiencing more data, more interactions, more systems, more releases, more breakages, more resolutions for those breakages, more bugs, and more vulnerabilities. All of this contributes to the change. At the end of the day, the humans will still need some form of UI to interact with all that. A lot of the interaction will be automated by agents. So we are building the product to satisfy both conditions. We have numerous UIs and create UIs that allow humans to understand their world, present options, navigate problems, and help model their environment. We also expose much of our functionality directly to agents. As I mentioned on the call, our MCP server is currently in preview and experiencing explosive customer usage. Thus, a likely future is that part of our functionality is delivered to agents via MCP servers or similar solutions, while other functionality will be available to humans through UIs.

Understood. Thank you, Olivier.

Our next question comes from Raimo Lenschow with Barclays. Your line is open.

Congrats from me as well. Staying on a little bit on that AI theme. Olivier, the eight-figure deal for a model company is really exciting. I assume they tried to do it with some open-source tooling, etcetera. But they actually went from paying very little to paying you more. What drove that thinking? What do you think they saw that kind of convinced them to do that? It's now the second one after the other very big model provider. Clearly, that whole debate in the market about doing it on the cheap is not valid. Could you speak to that, please? Thank you.

The situation is very similar to where every single customer will land. Every customer we land typically has some homegrown solution or is running some open source. The idea that it is cheaper to do it yourself is usually not the case. Your engineers are typically the most highly compensated individuals within those companies. Their velocity is what limits all other operations in the business. Consequently, when we enter a conversation with customers, we can demonstrate value quickly. This pattern is consistent among our AI cohort customers. They are a who's who of companies shaping the AI space and they have all adopted our product for similar reasons. The volumes may vary due to differences in scale among these companies, but the rationale holds across the board.

Thank you. Our next question comes from Gabriela Borges with Goldman Sachs.

Hi, good morning. Congratulations on the quarter and thank you for taking my question. Olivier, I wanted to follow up on Sanjit's question on how to think about where the line is between what an LLM can do longer term and the domain experience you have in observability. If I think about some of Anthropic's recent announcements, they're talking about LLMs as a broader anomaly detection tool on the security vulnerability management side. How do you think about the limiting factors in utilizing LLMs as such a tool, and how does Datadog have a moat that offers customers a better solution relative to where the roadmap and LLMs can go long-term? Thank you.

Yeah. That's a very good question. We see that LLMs are improving, and we bet on them getting significantly better, month after month, as we have observed in the past couple of years. As a result, they are proficient at analyzing large data sets, which could yield valuable insights. However, when thinking about our offering, there are two components to consider. One is how we can provide relevant context to feed into these intelligence engines. We aggregate all the data we receive, parse dependencies, and understand how we can present that data effectively to an LLM. For example, today we expose various functionalities through our MCP server, enabling customers to recombine functionalities with different intelligence tools. The second part we ponder is the future of observability. Right now, we respond to incidents and conduct post-mortem analysis afterward. However, as complexity increases, we cannot afford to wait for incidents to arise before conducting analysis. We need to be proactive, running analysis in real-time as data flows continuously. Detection and resolution will need to happen before outages materialize, requiring us to become fully integrated in the data pipeline. This transition will challenge many competitors in the space, particularly generalized AI platforms without the specialized capabilities we are developing.

That makes a lot of sense. Thank you. My follow-up is about the real-time data planes we're discussing. These operations involve much larger data flows than what you typically feed into an LLM. So it's a somewhat different problem to solve. Super interesting. Thank you. My follow-up for you, Olivier and David, you've mentioned a couple of times now some of the conversations you have with customers regarding value creation within the Datadog platform. Can you tell us a bit about how those conversations evolve when a customer sees that, in order to observe for more AI usage, they may see their Datadog bill increase? What are some of the steps you can take to ensure that the customer still feels they are receiving value from the Datadog platform despite the increased costs? Thank you.

There are a few things to consider. First, the age-old rule of software applies: there are only two reasons customers purchase your product — to generate more revenue or to decrease costs. When customers engage with a new product, they need to recognize a cost-saving in some capacity or understand that they will attract customers they otherwise would not have. We consistently prove this point whenever a customer decides to go with a new product, ensuring they experience tangible benefits. Furthermore, when customers choose to expand within our platform rather than bringing in an alternative vendor or product team, they often find they spend less through our systems.

Thank you. Our next question comes from Ittai Kidron with Oppenheimer and Company. Your line is open.

Thanks and congrats on quite an impressive finish for the year. David, I wanted to delve a bit into your 2026 guidance. I want to ensure I understand your assumptions clearly. Could you discuss the level of conservatism you factored into the guidance for the year? You mentioned at least 20% growth for the core, excluding the largest customer. What should we assume for this large customer? Additionally, when you consider the AI cohort, excluding this large customer, are there any evolving concentrations within that group given your success there?

Certainly. The first question relates to guidance. We approached our revenue guidance by considering organic growth rates, attach rates, and new logo acquisition rates and adjusted those figures appropriately. Thus, we noted that with guidance being set for 18% to 20%, while the non-AI or heavily diversified business is expected to grow by 20% or more. This indicates that the growth rate for our core business assumed in the guidance is higher than that of the large customer. However, this does not imply that the large customer isn’t showing growth in various forms; it simply means we do not have control over their consumption model. We also have a very diversified AI sector, with around 650 names, and it mirrors the diversity of our overall business, so we are seeing strong growth without significant customer concentration risks.

Okay. That's great. And can you provide the percentage of revenue attributed to the AI cohort this quarter?

We haven't specified that at this time.

Thank you. Our next question comes from Todd Coupland with CIBC.

Thank you and good morning. I wanted to ask you about competition. How is the rise of LLMs impacting market share shifts? Can you discuss that and how Datadog will be affected? Thanks a lot.

In terms of customer dynamics, we have not observed any significant changes in our competitive landscape. The key players remain the same, and we continue to gain market share from larger competitors. Despite some noise related to recent M&A activity, these weren't companies we have competed against strongly or lost deals with. Thus, we do not foresee these events changing competitive dynamics for us in the short term. Competing in observability requires constant innovation, and we know precisely what steps we must take to maintain our traction going forward. With the rise of LLMs comes a plethora of new functionalities and approaches to serve our customers. While we have some products developed for this purpose, it is still early in the lifecycle and the market has not yet fully differentiated. We anticipate this will evolve over time. Importantly, we argue that observability for LLMs should not differ fundamentally from observability for any other system, as LLMs do not operate in isolation. Their operations often depend on tools, applications, and established systems. Therefore, we believe we are firmly positioned to address these challenges.

Thank you.

Thank you. Our next question comes from Mark Murphy with JPMorgan. Your line is open.

Thank you. Olivier, Amazon is targeting $200 billion in CapEx this year. If you include Microsoft and Google, that CapEx is going to exceed $500 billion this year for the big three hyperscalers. It's growing 40% to 60%. I’m curious if you've gathered enough signals from the prior years of CapEx growth to estimate how much of that is training-related and when it might convert to inference, where Datadog might be required. In other words, are you looking at this wave of CapEx and able to predict how it might drive LLM observability revenue? Can you share the inning we’re in?

I think it's overly simplistic to link that solely to LLM availability. It points toward an increase in applications, intelligence, and overall complexity in the future. However, determining a direct correlation between the CapEx spending from those companies and what infrastructure will generate value two or three years from now is difficult. Nonetheless, this increase is indicative of significant growth in the complexity of systems and infrastructures, which we believe will significantly benefit our business.

Great help. And as a follow-up, there is an expectation that Anthropic may soon become a strong competitor to OpenAI, potentially equalizing revenue in the next couple of years. You mentioned the eight-figure deal with an AI model company. Do you see an opportunity to diversify your customer concentration with these larger AI providers, whether that’s through direct customer relationships or products like Claude Code being adopted globally, thus creating a more substantial surface area for Datadog?

We have not built our business model around a few concentrated customers. Rather, our goal has always been to ensure that our platforms are essential for all customers in the AI sector. We have seen considerable success with our current customers in this area, and more inquiries are coming in from various players, including major hyperscalers. We anticipate a broader base of business in the future, as indicated by the increasing number of AI-native customers we are acquiring and the growth of this segment.

Thank you. Our next question comes from Matt Hedberg with RBC. Your line is open.

Congrats from me as well. David, question for you. Your prior investments clearly are paying off with another quarter of acceleration. It appears you will continue investing in future opportunities. Margins appear down maybe 100 basis points from your initial guidance. Could you comment on gross margin expectations this year and how you might realize incremental OpEx synergies using more AI internally?

On the gross margin, what we have indicated is that it will hover around the 80% mark. We continually seek efficiency opportunities, and where the opportunities arise, we capitalize on them. We want to balance that with our investments in the growth of our platform and business. At present, we feel we are aligned with our target, but there may be future opportunities. We also prioritize heavy investments in R&D, and we see substantial productivity gains from AI in our operational processes.

Expectation is that we will continue heavily investing in R&D in the short to mid-term. We’re experiencing great productivity gains through AI, allowing us to tackle more challenges for our customers. We are actively pursuing the adoption of AI within our organization as well.

Thank you. Our next question comes from Koji Ikeda with Bank of America.

Yeah. Hey, guys. Thanks so much for answering my question. Olivier, maybe a question for you. A year ago, you discussed how while some customers desire to manage observability internally, it really is a cultural decision. It may not be rational unless you possess tremendous scale, talent access, and innovation bandwidth. Most companies do not meet those criteria. Given the changes in the industry and ecosystem, I wanted to get your updated views on this topic. Have your thoughts shifted over the past year?

No, my position remains largely the same. It is typical for clients to start with some form of homegrown solution or attempts to resolve issues themselves. Typically, after some time, they transition to a product, and eventually to our product. Some might optimize their setups along the way, but the prevailing trend is to rely on us increasingly for their more complex problems. While there are cases of companies opting to manage this on their own, it is primarily for cultural reasons. Economically speaking, for the vast majority of companies, that choice often does not make sense. We frequently see teams at hyperscalers, who have access to extensive tooling and substantial financial resources, still choosing to utilize our products because they offer a more straightforward path to resolving issues.

Thank you. Our next question comes from Peter Weed with Bernstein Research. Your line is open.

Hello. Can you hear me this time?

Yes, John.

Okay. Thank you. You’re on. Yep. Apologies for the last time. Great quarter. Looking ahead, one of your most interesting opportunities lies around Bits AI; I would love to hear how you think that opportunity develops. How do you get compensated fairly for the productivity you're bringing to the SRE and broader operations team? How do you perceive competition evolving in this space? We have certainly seen a number of startups emerge, with questions surrounding Anthropic and their trajectory. How can Datadog capture value in this area, and how does it ensure protection for your business?

The way we currently market many of these products is by demonstrating the difference in time spent. When the alternative requires dealing with an outage, where twenty individuals may spend three hours searching for a root cause, that's a significant business loss. If our solution is to resolve this within five minutes with just three targeted personnel, we see less customer disruption and greater productivity, which is cost-effective. We believe that is the argument we can present to clients. Longer term, we predict that systems will become proactive. They will analyze and diagnose issues preemptively, and the analysis will need to occur in real-time as data flows. This will require us to accumulate and evaluate vast amounts of data continuously and be able to pass quick and accurate judgments between normal and abnormal conditions. Effectively, we're looking at managing hundreds, thousands, or millions of data points in real time and deciding what actions to take. That's where we believe we will maintain a competitive advantage, particularly against generic AI platforms.

Thank you. Our next question comes from Brent Thill with Jefferies. Your line is open.

Thanks, David. I think many gravitate back to that mid-20% margin you put up a couple of years ago. I know the last couple of years, including the guidance, are showing low 20%. Can you discuss your true north in this, and how you're framing the bottom line?

The plan is to ensure cautious revenue projections while being ready to invest incrementally as results exceed expectations. Thus, we create guidance based on conservative revenue predictions. The process reflects ongoing investment strategies, which have been yielding strong returns, fueled by our commitment to platform R&D and employing AI in our operations. As we see success, it translates into margin improvements over time, which we will reinvest into further growth.

Any major go-to-market changes you perceive as necessary this year, especially in response to the evolving AI landscape?

The focus will remain consistent with our previous years — we aim to collaborate closely with clients to demonstrate value over time. This reflects in our account management and customer success efforts within the enterprise segment. We expect to continue enhancing our capacity, including geographical expansion and evolving engagement strategies with customers.

That will conclude our call for today. On that note, I would like to thank all of you for listening, and I look forward to seeing many of you at our Investor Day on Thursday. Thank you all. Goodbye.

Thank you.

Thank you for your participation. You may now disconnect. Everyone, have a great day.