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Self-Hosted DevOps Feature Flags A/B Testing Product Analytics Data Science Experimentation

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Last Updated: 08-21-2026 01:40 PM (UTC)

GrowthBook

Used by over 3,000 organizations including Dropbox, Khan Academy, Sony, Pepsi, Wikipedia, and Mistral, GrowthBook is the open-source experimentation platform that handles over 100 billion feature flag evaluations daily across its cloud and self-hosted deployments. Feature flags support advanced targeting with user attributes, gradual percentage rollouts, prerequisite dependencies, and scheduled launches, with both client and server SDKs evaluating flags locally from a cached payload for sub-millisecond performance with zero network calls per check. The warehouse-native architecture queries experiment results directly from 11 data sources including BigQuery, Snowflake, Databricks, Redshift, ClickHouse, Mixpanel, and PostgreSQL, eliminating duplicate data pipelines. The statistics engine implements CUPED variance reduction, sequential testing, Bayesian analysis, post-stratification, multi-armed bandits, and sample ratio mismatch checks with fully open-source methodology. Product analytics provides dashboards with funnels, retention cohorts, and event-based tracking shareable across teams. Twenty-four SDKs cover React, Next.js, Node.js, Python, Go, Ruby, PHP, Java/Kotlin, Swift, C#/.NET, Rust, Elixir, Angular, and edge runtimes including Cloudflare Workers, Fastly Compute, and Lambda@Edge. The MCP server and REST API enable AI coding agents to create features, start experiments, and manage stale flags programmatically. Self-hosting deploys via Docker Compose with MongoDB, or via Helm charts on Kubernetes, with the same codebase powering both open-source and managed cloud offerings. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

GrowthBook
GrowthBook
GrowthBook
GrowthBook
GrowthBook

Benefits

  • Warehouse-Native Experiment Analysis
  • Queries experiment results directly from BigQuery, Snowflake, Databricks, Redshift, ClickHouse, and PostgreSQL — no duplicate data pipelines or separate analytics infrastructure required.
  • Sub-Millisecond Feature Flag Evaluation
  • Client and server SDKs evaluate flags locally from a cached payload with zero network calls per check, delivering sub-millisecond performance across 24+ language and edge runtime SDKs.
  • World-Class Statistics Engine
  • Open-source stats engine implements CUPED variance reduction, sequential testing, Bayesian analysis, post-stratification, multi-armed bandits, and sample ratio mismatch detection for rigorous results.
  • Self-Hosted with Full Control
  • Same codebase as the managed cloud offering deploys via Docker Compose with MongoDB or Helm on Kubernetes, with unlimited feature flags, experiments, and traffic at zero licensing cost.

Features

  • Advanced Flag Targeting
  • Target users by attributes, percentages, prerequisites, and schedules with gradual rollouts, kill switches, and environment-specific configurations across development and production.
  • Built-In Product Analytics
  • Dashboards with funnels, retention cohorts, and event-based tracking let teams measure product metrics alongside experiment results in a single unified platform.
  • MCP Server and REST API
  • AI coding agents create features, start experiments, and manage stale flags through the MCP server, while the REST API supports webhooks and custom integration workflows.
  • Multi-Armed Bandits
  • Automatically allocate traffic to winning variations using Bayesian bandit algorithms that maximize conversions while the experiment runs without manual intervention.
  • Visual Editor
  • Non-technical users create front-end A/B tests by visually selecting page elements and defining variations without deploying code changes or modifying the application codebase.