Datadog acquires Adaptive ML to boost $1B AI research push

Datadog's acquisition of Adaptive ML brings reinforcement learning operations to the observability giant's $1 billion annual research engine.

Datadog Inc. acquired Adaptive ML, a startup building what it calls the first reinforcement learning operations platform, to embed continuous AI improvement into its observability and security products. The deal folds Adaptive ML into Datadog AI Research, the company's lab focused on world models and agentic LLM post-training for infrastructure monitoring.

"We started Adaptive to give every enterprise the ability to perpetually improve its own AI. The missing piece was never the algorithm, the hardest part was production scale," Julien Launay, co-founder and chief executive officer of Adaptive ML, said in a statement. "With Datadog's unmatched access to real-world infrastructure, we can accelerate towards continuous intelligence."

Datadog has invested more than $1 billion in research and development annually, funding initiatives including the Toto 2.0 research project and a suite of AI agents — Bits Investigation, Bits Code, and Bits Security Analyst — that have conducted hundreds of thousands of automated investigations for customers. Adaptive ML's RLOps platform lets enterprises build, own, and deploy specialized AI agents that improve over time using production feedback, a capability Datadog plans to integrate into its monitoring stack.

The acquisition signals Datadog's bet that observability will shift from passive dashboards to autonomous systems that detect and fix issues before they affect customers. Datadog shares rose 3.2 percent to $247.45 on the announcement, extending a year-to-date gain of 85 percent, though the stock remains 10.8 percent below its May high of $277.49. Scotiabank raised its price target to $275 and Citi to $270, citing Datadog's widening competitive moat as AI infrastructure creates new demand for monitoring software.

What Adaptive ML brings to Datadog's lab

Adaptive ML developed the first dedicated reinforcement learning operations platform, a category designed to solve what Launay called the hardest part of enterprise AI: production-scale improvement. Most AI models are trained once and deployed statically; RLOps creates a feedback loop where real-world signals continuously refine model behavior. For Datadog, which processes telemetry data from thousands of enterprise customers, that feedback loop could turn raw observability data into what Chief Scientist Ameet Talwalkar described as "first-party intelligence."

"Our lab is focused on leveraging our data and domain expertise to build specialized agents and models, and to effectively turn our data into first-party intelligence," Talwalkar said. "Bringing Adaptive ML on board is a natural fit to enhance and augment the work we are already doing within our lab."

The deal also positions Datadog against rivals including Dynatrace Inc. and Cisco Systems Inc.'s Splunk unit, both of which are investing in AI-driven observability. Dynatrace's Davis AI and Splunk's AI assistant compete for the same enterprise monitoring budgets, but Datadog's advantage lies in the breadth of its data — the company monitors applications, infrastructure, data, models, and security from a single platform, giving it more training signals than either competitor.

Investor implications

Datadog trades at roughly 12 times forward sales, a premium to Dynatrace at about 9 times but below its five-year average of 16 times, reflecting the market's uncertainty about AI's impact on SaaS pricing after the February "SaaSpocalypse" selloff. The Adaptive ML acquisition is small relative to Datadog's $80 billion market capitalization, but it signals a strategic direction: turning observability data into continuously improving AI agents that justify higher per-seat pricing.

If Datadog can demonstrate that its Bits agents resolve incidents faster than human engineers, the company could expand its average revenue per customer without adding headcount — a margin story that would support multiple expansion. The risk is that AI agents commoditize monitoring itself, compressing the pricing power that has driven Datadog's 30 percent-plus revenue growth.

This article is for informational purposes only and does not constitute investment advice.