FeatBit is a self-hosted feature flag and experimentation platform designed for developers who need full control over feature releases, A/B testing, and user targeting without relying on third-party SaaS. It solves the risk of production deployments by decoupling code shipping from feature activation, allowing incremental rollouts, targeted releases, and instant rollbacks. Built with .NET 8 and Python, it supports server and client SDKs across multiple languages and integrates with observability tools like OpenTelemetry, DataDog, and Grafana.
FeatBit’s architecture supports deployment via Docker Compose, Kubernetes, Helm charts, and Relay Proxies for private environments. It includes a web portal for managing flags, segments, and experiments, with APIs and CLI for automation. The platform is open core — core features are MIT-licensed, while enterprise capabilities like SSO and audit logs require a paid license.
What You Get
- Multi-language SDKs - Official SDKs for .NET (C#), Python, Node.js, Java, Go, React, React Native, and OpenFeature providers to integrate feature flags into any stack.
- Targeted User Segmentation - Define user segments using attributes (e.g., location, plan tier) to roll out features to specific groups with precision.
- A/B Testing & Experimentation - Run feature-level A/B tests with built-in analytics to measure impact on user behavior and business metrics.
- Audit Logs & Change Tracking - Track every flag and segment modification with timestamps, users, and changes for compliance and debugging.
- Relay Proxy / Agent - Deploy lightweight agents in customer environments to reduce latency, ensure offline availability, and maintain data sovereignty.
- Webhooks & Integrations - Connect to Slack, DataDog, New Relic, Grafana, Growthbook, and more to trigger alerts or sync metrics on flag changes.
- FeatBit CLI - Manage feature flags via command line for CI/CD automation and infrastructure-as-code workflows.
- SSO & IAM/RBAC - Integrate with OAuth 2.0, Azure AD, Okta, and Auth0; enforce role-based access control across projects and environments.
- Release Decision AI Copilot - Experimental AI agent that suggests flag configurations and reduces technical debt using ChatGPT and VSCode integration.
- OpenTelemetry Integration - Instrument feature flag usage with distributed tracing, logs, and metrics for observability across microservices.
- Helm Charts & Kubernetes Support - Deploy FeatBit on any Kubernetes cluster with configurable persistence, scaling, and high availability options.
- Multi-Project & Multi-Environment Management - Organize flags across development, staging, and production environments with isolated configurations.
Common Use Cases
- Running a phased feature rollout - A SaaS company uses FeatBit to release a new UI to 5% of users, monitor error rates, then gradually expand to 100% without redeploying.
- Running A/B tests on pricing pages - An e-commerce team tests two pricing models using FeatBit’s built-in experimentation engine and measures conversion lift in real time.
- Self-hosting for compliance - A healthcare startup deploys FeatBit on-premises to keep user data within EU boundaries while still using feature flags for rapid iteration.
- AI-assisted flag management - A DevOps team uses the FeatBit AI Copilot to auto-generate flag names and default values from code comments, reducing technical debt.
- Enterprise-grade feature governance - A financial services firm enforces approval workflows and audit logs before any flag change is applied to production.
- Reducing SaaS costs by 20x - A startup replaces a commercial feature flag service with self-hosted FeatBit, cutting monthly infrastructure costs while retaining full control.
Under The Hood
Architecture
- Clear microservice architecture with distinct services handling feature flag serving, evaluation logic, and data analytics as separate concerns
- Event-driven design using Kafka and PostgreSQL to decouple API services from analytics and evaluation systems
- Modular provider system allows interchangeable data stores, message queues, and caching layers via environment configuration
- Handler pattern enforces command/query separation with dedicated validation and execution layers for feature operations
- UI and backend services are cleanly separated, enabling independent deployment and scaling
Tech Stack
- Python backend powered by Flask with integrated libraries for persistence, scheduling, and caching
- Frontend built with Next.js and Handlebars, deployed via Docker with Vercel for seamless demo environments
- Multi-environment database support with PostgreSQL, MongoDB, and ClickHouse, dynamically configured at runtime
- Layered messaging and caching with Kafka and Redis enabling scalable, asynchronous workflows
- Containerized deployment using Docker Compose with environment-specific configurations for lightweight to enterprise topologies
Code Quality
- Extensive test coverage across unit, integration, and end-to-end scenarios with clear isolation and fixture management
- Strong type safety enforced through nullable references and TypeScript interfaces, reducing runtime errors
- Consistent naming conventions and behavior-driven test naming improve readability and maintainability
- Robust error handling via HTTP status codes and structured responses, validating authentication, CORS, and protocols systematically
- Comprehensive linting and CI-ready structure promote modularity and reduce code duplication
What Makes It Unique
- Real-time feature flag comparison UI with visual diffs and lineage tracking across environments
- Integrated policy and group-based permissions tightly coupled with user segments for attribute-driven targeting
- Client-side evaluation engine that renders complex flag logic directly in the UI, minimizing server round-trips
- Interactive segment-to-flag visualization that unifies user targeting and permission management in a single drawer
- Extensible variation system supporting custom data structures with native rendering and preview capabilities without backend changes