
Traditional feature management and experimentation tools often force teams to export data to third-party vendors, driving up costs and creating security risks. GrowthBook takes a warehouse-native approach by running analysis directly on top of existing data sources like Snowflake, BigQuery, and Databricks. This architecture keeps sensitive user data inside private infrastructure while lowering subscription fees compared to legacy vendors.
Designed for growth engineers, data scientists, and product leaders, this open-source platform replaces expensive alternatives like LaunchDarkly and Optimizely. Teams can start with a free cloud plan or run a fully self-hosted instance to retain total control over their deployment pipelines.
For individuals or small teams to launch features and experiment quickly.
For small and mid-sized teams with advanced statistics and analytics.
For teams running experimentation and releasing features at scale.
Pricing is sourced from GrowthBook's website and may be out of date — check their site for current pricing.
GrowthBook connects directly to our data warehouse, so experiments are measured using metrics we already trust rather than sending everything into another closed system. Feature flags and test results sit neatly together, and the open-source, self-hosted option gives us plenty of control. Setup requires solid data and engineering input, but it is excellent value for a technically capable team.
Ask GrowthBook's team and people who've used it.
No questions yet
Be the first to ask a question about GrowthBook — the team and other users can weigh in.
+4 more
+4 more
+4 more
+4 more
+2 more