Experimentation & feature flags · GrowthBook
GrowthBook
Open-source, warehouse-native experimentation and feature-flag platform offering both Bayesian and frequentist sequential analysis with CUPED.
GrowthBook is an open-source (MIT-licensed) experimentation platform that reads results directly from a customer's own data warehouse rather than requiring data to flow through a separate event pipeline, alongside a feature-flagging system for controlling rollouts. It supports two statistical engines chosen at the org or project level: a Bayesian engine using an improper uninformative prior by default (or an optional weakly-informative Normal prior), reporting "chance to win" and full uplift distributions as violin plots; and a frequentist engine running two-sample t-tests with optional sequential testing to guard against inflated false positives from peeking. Both engines support CUPED variance reduction, and the platform includes automatic checks for sample ratio mismatch and suspicious uplifts. It can be self-hosted for free or run on GrowthBook's managed cloud, appealing to teams that want warehouse-native experimentation without vendor lock-in.
At a glance
| Vendor | GrowthBook |
|---|---|
| Pricing model | Free tier + paid plans |
| Free tier | Yes |
| Deployment | Cloud, Self-hosted |
| Open source | Yes (MIT) |
| Best for | Teams wanting warehouse-native, open-source experimentation with a choice of statistical engine. |
Pricing
Free self-hosted open source, or free Starter cloud plan (3 users); paid Cloud Pro is $40/seat/month; Enterprise is custom for cloud or self-hosted.
| Plan | Price | Notes |
|---|---|---|
| Starter (Cloud) | $0 | up to 3 users, 1 project, unlimited flags and experiments |
| Pro (Cloud) | $40/seat/month | up to 30 users, 3 projects, AI visual editor, multi-armed bandits |
| Open Source (self-hosted) | $0 | 1 project, unlimited flags/experiments, community support |
| Enterprise | custom | cloud or self-hosted, SSO/SCIM, approval workflows, 99.99% SLA |
Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.
Features
- Warehouse-native experiment analysis (Snowflake, BigQuery, etc.)
- Choice of Bayesian or frequentist statistical engine
- Sequential testing in the frequentist engine
- CUPED variance reduction on both engines
- Feature flagging with targeting rules
- Sample ratio mismatch and data-quality checks
- Self-hosted (MIT license) or managed cloud
Integrations
Profile last reviewed September 21, 2026