Cloud monitoring as a service company Datadog Inc. today announced the launch of Datadog Experiments, a new product that allows teams to design, launch and measure product experiments and A/B tests directly within its platform.

The new offering is being pitched as giving teams the data and insights they need to understand how every change affects user behavior, application performance and business outcomes.

Modern product teams rely on experimentation to validate new features and optimize user experiences, but existing tools fall short. Datadog argues that today’s tools are typically disconnected from business data systems, forcing teams to stitch together multiple solutions, such as a product analytics vendor, a standalone experimentation platform and a monitoring tool. The result is fragmented workflows and blind spots between product changes and application performance.

Added to the mix is that artificial intelligence is accelerating feature development and release velocity, making the gap between workflows more pronounced. Datadog Experiments takes the issue head-on with an experimentation platform that combines business metrics from a customer’s data warehouse with product analytics events and application observability.

The new offering uses technology from Datadog’s acquisition of Eppo Inc. to provide statistical methods with real-time observability guardrails so companies can test what matters, move quickly and ship with confidence.

Key features of Datadog Experiments include accelerated decisions without the overhead, with experimentation self-serve and standardized so that teams can move from insight to decision without coordination overhead. Complementary built-in guardrails allow teams to run safer, higher-quality experiments with real-time feed and shared standards that help teams catch issues early, protect users and keep experiments valid.

Experiments also allows teams to make decisions that leaders trust, with results that are credible, reproducible and comparable by measuring impact directly against source-of-truth business metrics in native data warehouses

“AI has increased the pace and complexity of software releases exponentially,” said Chief Product Officer Yanbing Li. “Too often, though, teams are flying blind when it comes to measuring the efficacy of new code. That’s because they don’t have a uniform way to validate changes and monitor their impact.”

With Datadog Experiments, he added, “teams have the guardrails needed to safely validate AI-driven changes. By tying experiments to Real User Monitoring, Product Analytics, APM and logs, organizations can measure both business impact and performance implications to reduce risk without slowing innovation.”

Image: Datadog