Modern product teams ship features constantly. Every change—whether it’s a new onboarding flow, pricing tweak, or UI adjustment—raises the same question: Did this improve the product? AI has changed the stakes entirely: As release cycles accelerate and code generation scales across every team, the volume of changes has outpaced most teams’ ability to measure their true value.
In noisy, real-world environments, randomized experiments (A/B tests) are the only reliable way to know which changes affect a metric. The problem is that running those experiments is often slow and fragmented: Teams open tickets to define metrics, wait for dashboards to be built, and depend on data scientists to validate results. Until now, understanding whether a change improved the product meant stitching together a product analytics vendor, business intelligence tools, a standalone experimentation tool, and a monitoring platform, which creates fragmented workflows and blind spots between product changes and business impact.
Datadog Experiments helps product and engineering teams run trustworthy experiments directly within the Datadog platform. By combining behavioral analytics, application performance telemetry data, and warehouse-native business metrics in a single workflow, teams can design, launch, and analyze experiments without waiting on specialists, making experimentation something the whole organization can own.
