GrowthBook is an open source platform designed to help teams implement feature flagging and A/B testing without relying on expensive SaaS tools or building custom solutions from scratch. It provides a unified interface to manage experiments, target users with precision, and analyze results using advanced statistical methods—all while integrating with your existing data infrastructure like BigQuery, Redshift, and Mixpanel. Built for developers and data teams, GrowthBook bridges the gap between product experimentation and data engineering by enabling real-time analysis without requiring data movement or complex pipelines.
The platform supports open core licensing, with the majority of its functionality available under MIT license, making it accessible for startups and enterprises alike. With built-in support for multiple SDKs across web and mobile platforms, GrowthBook enables consistent experiment deployment and tracking regardless of your tech stack.
What You Get
- Feature flags with advanced targeting - Apply granular rules to roll out features to specific user segments based on attributes like location, device, or custom properties, with support for gradual rollouts and percentage-based exposure.
- Multi-platform SDKs - Official libraries for React, JavaScript, PHP, Ruby, Python, Go, Android (Kotlin), and iOS (Swift) to integrate feature flags and experiments directly into your applications with minimal code changes.
- Advanced A/B test analysis - Built-in statistical methods including CUPED, Sequential Testing, Bayesian inference, and SRM (Sample Ratio Mismatch) detection to ensure experiment validity and reduce false positives.
- Integration with existing data warehouses - Connect directly to BigQuery, Redshift, Snowflake, ClickHouse, Google Analytics, and Mixpanel to pull real user behavior data for analysis without exporting or duplicating datasets.
- Drill-down analytics by custom attributes - Analyze experiment results segmented by browser, country, user tier, or any other custom attribute tracked in your data source.
- Jupyter Notebook export - Export full experiment reports as downloadable Jupyter Notebooks for deeper data analysis, sharing with data science teams, or reproducible research.
- GitHub Flavored Markdown documentation - Embed screenshots, notes, and context directly within experiments to maintain transparency and knowledge sharing across teams.
- REST API and webhooks - Automate experiment creation, update feature flag states, or trigger external systems using a comprehensive REST API and configurable webhooks.
Common Use Cases
- Building a multi-tenant SaaS dashboard with targeted feature rollouts - Use GrowthBook to gradually enable new UI components for specific customer segments based on subscription tier or usage patterns, reducing risk of widespread regressions.
- Creating a mobile-first e-commerce platform with 10k+ SKUs - Run A/B tests on product recommendations or checkout flows using real purchase data from BigQuery, while applying feature flags to test new payment methods without redeploying apps.
- Problem: Inconsistent experiment results due to data silos → Solution: Connect GrowthBook directly to your data warehouse - Eliminate the need to export and re-ingest user behavior data by querying live datasets in Redshift or Snowflake for accurate, up-to-the-minute conversion metrics.
- DevOps teams managing microservices across multiple cloud providers - Centralize feature flag management with a single API and SDKs for polyglot services, enabling coordinated releases across Node.js, Python, and Go backends without vendor lock-in.
Under The Hood
GrowthBook is a feature flagging and experimentation platform designed to empower developers with flexible, scalable tools for managing product releases and A/B testing. It supports remote evaluation, extensive data source integrations, and real-time analytics through a modular architecture.
Architecture
GrowthBook follows a monorepo structure with well-defined packages for backend, frontend, and shared components. This organization enables reuse and clear separation of concerns.
- The backend uses a layered architecture with API routers, models, and services to support complex experiment configurations
- Modular design includes dependency injection via configuration files and strategy patterns for data source adapters
- Middleware-based request handling and event-driven communication support real-time feature flag evaluation
- Shared libraries and API gateways facilitate integration across diverse deployment environments
Tech Stack
GrowthBook leverages a modern JavaScript/TypeScript ecosystem with support for multiple backend and frontend frameworks.
- Built with TypeScript, Node.js, and Express for the backend, and React/Next.js for the frontend
- Integrates with MongoDB, PostgreSQL, and cloud data warehouses through dedicated connectors
- Employs Docker, PM2, and Helm charts for containerized deployment and orchestration
- Includes Jest, ESLint, Prettier, and Python-based statistical testing for comprehensive quality assurance
Code Quality
Code quality in GrowthBook shows a balanced mix of strong testing practices and some structural inconsistencies.
- Extensive test coverage with unit, integration, and statistical tests across modules
- Consistent use of error handling patterns and type safety through TypeScript
- Linting and formatting tools are consistently applied across the codebase
- Some technical debt is present, particularly in core implementation files that lack full analysis
What Makes It Unique
GrowthBook stands out as a developer-centric platform that combines flexibility with deep integration capabilities.
- Offers remote feature flag evaluation and real-time experiment tracking across multiple platforms
- Provides extensive SDK support and framework integrations for seamless adoption
- Supports flexible deployment models including self-hosted, cloud, and hybrid configurations
- Combines A/B testing with feature flagging in a unified platform for streamlined product development