Datadog Conference: CEO Says AI and Enterprise Growth Are Accelerating

Key Points

• Datadog’s growth is accelerating: CEO Olivier Pomel said the company achieved roughly 36% year-over-year growth in its latest quarter, with both AI-focused and broader enterprise customers contributing. Non-AI customer growth increased to the high-20% range from about 18% a year earlier.
• AI is expanding the observability opportunity: AI deployments require monitoring additional layers such as GPUs, models, agents and security interactions, while Datadog currently holds about 13%–14% of the observability market and serves roughly half of the Fortune 500.
• Datadog is broadening its AI and security strategy: Bits AI is expanding beyond alert investigations, with the company transitioning toward AI-credit pricing. Datadog is also investing heavily in R&D and targeting security, digital experience monitoring and synthetic testing as future growth areas.

Datadog NASDAQ: DDOG CEO and Founder Olivier Pomel said the company is seeing accelerating growth across both AI-focused customers and its broader enterprise customer base as organizations expand cloud and artificial-intelligence deployments.

Speaking with Citi enterprise software analyst Fatima Boolani, Pomel said Datadog has grown by roughly an order of magnitude in revenue since its 2019 initial public offering. He said the company reported approximately 36% year-over-year growth in its most recent quarter following several quarters of sequential acceleration.

Pomel said growth was not limited to companies primarily focused on AI. He said Datadog's non-AI customer segment accelerated from about 18% year-over-year growth a year earlier to growth in the high 20% range, reflecting broader adoption of AI-related infrastructure and applications.

Observability in the AI transition

Pomel compared the current AI transition with the earlier migration to cloud computing, saying both shifts require companies to operate more digital infrastructure, ship software faster and manage increasingly interconnected development, operations and security functions.

He said an estimated 70% to 80% of the technology stack used by AI companies resembles the infrastructure used by non-AI companies. However, AI deployments add new layers to monitor, including GPUs, models, agents, agent outcomes, security and interactions with external systems.

“The part of the business growing the fastest is the part that has to do with the buildup at the infrastructure and the application layer,” Pomel said. While Datadog is also seeing more traffic from AI-related workloads such as agent traces, he said those newer workloads point more to the future direction of the market than to the current primary driver of business.

Pomel said Datadog has roughly 13% to 14% share of the observability market, which he described as growing at annual rates ranging from 15% to 30%, depending on the source. He added that the company serves about half of the Fortune 500 and sees substantial room to expand its relationships with large customers.

Bits AI and evolving pricing

Pomel described Datadog’s AI strategy as “Datadog for AI” and “AI for Datadog.” The latter includes Bits AI, the company’s AI agent offering, which initially focused on investigating alerts and has since expanded to include a chatbot, code optimization and tools for building agents and applications within Datadog.

He said Bits AI is being used by a large number of customers, but its effects are not yet readily visible in the company’s financial results. Datadog is transitioning from a single investigations-focused stock-keeping unit to AI credits that can be used across several AI capabilities.

Pomel said the company is still evaluating the preferred industry approach to charging for AI agents. While model providers typically charge by token, he said pricing above that level remains unsettled. Datadog’s usage-based model gives it flexibility to alter or add products and pricing metrics, he said.

The company has also begun to decouple some AI capabilities from Datadog data ingestion. Pomel cited its security agent, which can work with data sources outside Cloud SIEM. For now, the company is packaging those capabilities as AI credits on top of other consumption, though it could later tie pricing to data volumes, hosts or other measures.

Open source, AI-native customers and competition

Pomel said open-source tools have long been part of the observability landscape and that Datadog works to integrate with them while giving customers an option to consolidate workloads onto its platform. He called OpenTelemetry a tailwind because it reduces the effort needed to instrument workloads and can make customers more comfortable expanding their use of Datadog.

Datadog has separately discussed AI-native customers because their growth patterns differ from those of other customers, Pomel said. These companies are building infrastructure and applications rapidly, though he said their core usage patterns remain similar to those of more established enterprises.

He acknowledged that Datadog is monitoring potential volatility among AI-native businesses, citing the company’s experience with cloud-native and digital-native customers that expanded rapidly during the pandemic and later reduced spending. However, he said Datadog’s current exposure to AI-native customers is smaller than its prior exposure to cloud-native customers, while the company’s overall business is more diversified.

Pomel also argued that the infrastructure investments made by AI-native companies are benefiting the broader customer base, pointing to the acceleration among non-AI customers.

R&D, security and platform strategy

Pomel said Datadog continues to invest about 30% of revenue in research and development, although the mix of spending is shifting toward token costs, GPUs and model training. He said the company is using AI internally in support functions and engineering, where teams are increasingly building with agents.

While AI can allow smaller teams to perform work that previously required more specialized personnel, Pomel said Datadog is still expanding its engineering organization. Higher productivity, he said, would enable the company to build more products, deepen existing offerings and address adjacent markets rather than reduce its R&D ambitions.

Security remains one of those adjacent opportunities. Pomel said the company has about a quarter of its customer base using security products and described the security opportunity as significant as AI reshapes the market. He said companies will need more integrated systems that can respond at “machine speed,” rather than relying on numerous separate products feeding alerts to human reviewers.

Pomel said Datadog’s differentiation rests on its integrated SaaS platform, broad customer reach and access to operational data including metrics, traces, logs and network topology. He said the company has released time-series models, Toto and Toto 2.0, and acquired Adaptive ML to accelerate development of customized models.

Looking ahead, Pomel identified digital experience monitoring and synthetic testing as a potential major growth area. He said Datadog will continue to consider acquisitions, primarily where they can accelerate product-market fit, add technical teams or broaden distribution, while maintaining an integrated platform strategy.

About Datadog (NASDAQ:DDOG)

Datadog, Inc NASDAQ: DDOG is a cloud-based software company that provides monitoring, observability, security and analytics tools for modern applications and information technology environments. Its platform helps organizations collect, correlate and analyze data from servers, databases, applications, containers, networks and cloud services in a unified environment.

The company's products include infrastructure monitoring, application performance monitoring, log management, real user and synthetic monitoring, database monitoring, cloud security, software delivery tools, incident management and cloud cost management.

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