Datadog Tops $1 Billion in Quarterly Revenue, Raises 2026 Outlook

Datadog cleared a milestone that investors have been waiting for: quarterly revenue topped $1 billion for the first time, and the company raised its outlook for 2026 as demand grew across cloud monitoring, security and AI-related tooling.

The company reported first-quarter revenue of $1.006 billion, up 32% from a year earlier, with adjusted earnings of 60 cents per share. Datadog’s shares jumped 31% on Thursday, May 7, their biggest one-day gain since the company went public in 2019, as investors responded to both the sales acceleration and a higher full-year forecast.

Datadog now expects 2026 revenue of $4.30 billion to $4.34 billion, up from a prior range of $4.06 billion to $4.10 billion. It also lifted adjusted earnings guidance to $2.36 to $2.44 per share from $2.08 to $2.16. The company said total annual recurring revenue surpassed $4 billion, a mark that signals its business has moved well beyond early adoption into the kind of scale that tends to make platform spending stickier.

The quarter also showed how AI is changing the shape of enterprise software spending. Datadog said it had about 4,550 customers with at least $100,000 in annual recurring revenue as of March 31, up from about 3,770 a year earlier, while its overall customer count reached about 33,200. Free cash flow was $289 million, operating cash flow was $335 million, and cash, cash equivalents and marketable securities totaled $4.8 billion. The company added new products and features including MCP Server, Bits AI Security Agent, GPU Monitoring and Experiments, and it earned FedRAMP High certification for Datadog for Government, a step that could broaden its reach inside the U.S. federal government and other regulated sectors.

Datadog’s own State of AI Engineering 2026 report pointed to why the category may have staying power. The company said operational complexity, not model intelligence, is becoming the main barrier to reliable AI at scale. Nearly 5% of AI model requests fail in production, and close to 60% of those failures are tied to capacity limits. Datadog said nearly seven in ten companies now use three or more models, a sign that the market is becoming more fragmented and harder to manage, not simpler.

That complexity helps explain why monitoring and observability vendors are drawing more attention as the AI boom matures. Datadog said its DASH conference will take place in New York City on June 9 and 10, where it plans to showcase its latest AI observability and security products. For investors trying to separate durable AI spending from speculation, Datadog’s latest quarter offered one of the clearest examples yet.