Amplitude Experiment combines feature experimentation with behavioral analytics in a unified platform, enabling teams to run experiments and measure impact using the same user data and cohorts without separate integrations.
Last updated Mar 7, 2026 by AI Enrichment
Integrated experimentation platform within a leading product analytics suite
Amplitude Experiment is a feature experimentation and A/B testing platform that operates as part of Amplitude's Digital Analytics Platform suite. The product enables product teams, engineers, and growth marketers to run controlled experiments, feature flags, and multivariate tests to optimize user experiences and product features. Amplitude Experiment differentiates itself by tightly integrating experimentation capabilities with Amplitude's behavioral analytics platform, allowing teams to leverage existing user data and behavioral cohorts for targeting and analysis without requiring separate data pipelines. The platform provides both client-side and server-side experimentation capabilities, supporting web, mobile, and backend implementations. Amplitude Experiment serves as a core component of Amplitude's broader product intelligence ecosystem, which includes analytics, session replay, and customer data platform capabilities. The integration between experimentation and analytics allows teams to measure experiment impact across the entire customer journey and leverage statistical analysis powered by Amplitude's data infrastructure. Amplitude Experiment competes in the feature management and experimentation space alongside platforms like LaunchDarkly, Optimizely, and Split.io. The product is positioned for mid-market to enterprise companies that prioritize data-driven product development and seek unified experimentation and analytics capabilities within a single platform.
Dynamic feature toggles for controlled rollouts and targeting specific user segments
Experimentation framework for testing product variations and measuring statistical significance
Testing multiple variables simultaneously to optimize complex user experiences
Ensures experiments don't interfere with each other by isolating user groups
Built-in statistical significance testing and sequential analysis capabilities