Feature Flag Management and A/B Testing platform, Bucketeer offers an enterprise-grade, self-hosted solution for managing feature releases and running experiments. It targets software teams from startups to large enterprises, providing advanced capabilities typically found in expensive SaaS products but with complete infrastructure control and zero licensing costs. Bucketeer enables sophisticated feature rollouts, data-driven decision-making through Bayesian A/B testing, and a polished user experience accessible to both technical and non-technical users.

How It Works

Bucketeer combines advanced feature flagging with robust experimentation. Its core approach leverages unique targeting rules, allowing one flag's evaluation result to condition another's, and split audience rollouts for nested experiments. For A/B testing, it employs Bayesian statistical analysis, which provides more accurate results with smaller sample sizes and shorter experiment durations compared to traditional methods. The platform utilizes an event-driven pipeline with Redis caching for sub-millisecond flag evaluation responses, ensuring high performance and scalability.

Quick Start & Requirements

• Primary install:
  • Lite Version (Local Dev/PoC): docker-compose up -d or make docker-compose-up. Requires adding entries to /etc/hosts for dashboard access.
  • Production (Kubernetes): helm install bucketeer ./manifests/bucketeer -n bucketeer --values ./manifests/bucketeer/values.prod.yaml.
• Prerequisites: MySQL and Redis are used in the Docker Compose lite version. Kubernetes deployment details are provided.
• Links:
  • Online Demo: Try Bucketeer online (Note: Actual URL not provided in README, using placeholder)
  • Documentation: Official Documentation (Note: Actual URL not provided in README, using placeholder)
  • Community (Slack): Join Slack (Note: Actual URL not provided in README, using placeholder)
• Setup Time/Footprint: Docker Compose setup is described as "minutes". Production Kubernetes deployment is designed to scale to 100M+ users and billions of evaluations per month.

Highlighted Details

• Advanced Targeting: Flag-based targeting rules (using one flag's result as a condition for another) and flag prerequisites enable complex, dynamic user segmentation.
• Bayesian A/B Testing: Offers faster, more accurate experimentation with smaller sample sizes and real-time statistical significance calculations.
• Split Audience Rollouts: Allows nested experiments, such as running a 50/50 A/B test on only 5% of traffic.
• AI-Powered Workflows: Integrates with AI assistants (Claude, ChatGPT) via Model Context Protocol (MCP) servers for managing flags and querying documentation.
• OpenFeature Compatible: Supports the vendor-neutral OpenFeature standard for maximum SDK flexibility.

Maintenance & Community

The project has a community channel (Slack) and acknowledges contributors. While specific roadmap or sponsorship details are not explicitly detailed in the provided text, the mention of "growing community" suggests active development and user engagement.

Licensing & Compatibility

Bucketeer is released under the Apache License 2.0. This license is permissive and generally compatible with commercial use and linking within closed-source projects.

Limitations & Caveats

PostgreSQL support for the lite version is noted as "coming soon." While designed for production scale, the complexity of a Kubernetes deployment for high availability and performance requires significant infrastructure expertise.