GrowthBook provides the most feature-complete open-source alternative to LaunchDarkly for teams that need both feature flags and experimentation capabilities. Its feature flag system supports boolean, multivariate, and JSON flags with targeting rules based on user attributes, percentage-based rollouts, and prerequisite flags. The experimentation engine runs statistical analysis using Bayesian, frequentist, sequential, and CUPED methods, giving product teams rigorous A/B testing without third-party analytics dependencies.
What sets GrowthBook apart from simpler feature flag tools is its warehouse-native analytics architecture. Instead of collecting its own event data, it connects directly to your existing data warehouse — BigQuery, Snowflake, Databricks, Redshift, ClickHouse, Mixpanel, or Postgres — to compute experiment results. This means teams use their existing event tracking and data pipelines rather than implementing a parallel analytics stack. The visual experiment editor lets non-technical users create and manage experiments through a web interface.
With over 7,600 GitHub stars and active development under an MIT license, GrowthBook has become the default choice for engineering teams that want self-hosted experimentation infrastructure. SDKs cover JavaScript, React, Python, Ruby, Go, Java, PHP, Swift, Kotlin, Flutter, and more. The platform includes a REST API, Webhooks for CI/CD integration, and recently added an MCP server that enables AI agents to query and manage feature flags programmatically.