We're excited to announce Bucketeer 2.0, a major update to our feature flag management platform. This release is all about usability, speed, and flexibility — making it easier than ever to manage flags, collaborate with your team, and roll out changes with confidence.

Over the past months, we've listened closely to feedback from the community. Many of you wanted a smoother workflow, faster flag management, and more intuitive tools. With Bucketeer 2.0, we've reimagined the console from the ground up to deliver just that.

Bucketeer has always been a powerful open-source platform for feature flag management and A/B testing.
But until now, self-hosting Bucketeer required Google Cloud services like BigQuery and Pub/Sub. That meant local testing was difficult, and teams who preferred to host outside GCP had limited options.

With Bucketeer Lite, we're changing that.

Now you can run Bucketeer in just a few minutes using Docker Compose, with Redis Streams for pub/sub and MySQL for data storage.
No GCP required — just Docker, MySQL, and Redis.

The Bucketeer Demo Site provides a simple way to try out Bucketeer in a sandbox environment. This guide walks through how to access the demo site, create a demo organization, and start using the system.

What does the Bucketeer Demo Site Offer?​

• We want to make it easy for new users to explore Bucketeer without needing to set up their own environment.
• The demo site allows you to quickly test features like feature flags, progressive rollout, and more, all in a safe, isolated environment.

All the coolest Bucketeer features are available and ready to explore. So let's hop in and see how to get started!

Feature flags have revolutionized how we deploy and test software, but managing them often requires context switching between multiple tools and interfaces. Today, we're excited to announce the Bucketeer MCP Server, a Model Context Protocol implementation that brings comprehensive feature flag management directly into your AI-assisted development workflow.

Improving a website or application inevitably involves releasing new features and changing designs. However, every change carries inherent risks: "Will this break something?" or "Will this negatively impact the user experience?"

This article explains how to use a powerful technique called feature flags to manage these risks and effectively run A/B tests that are both safe and data-driven, complete with practical examples.

Feature flags have become essential for modern software development, enabling teams to deploy code safely, test features with specific user segments, and roll out changes gradually. To address the challenge of vendor lock-in when switching between different feature flag providers, we're excited to announce OpenFeature support for the Bucketeer Go SDK.

Feature flags are a powerful way to control how users experience your product. But over time, flags that were once useful can become technical debt if they aren't cleaned up properly. That's why we're excited to introduce Code References in Bucketeer, a new feature that helps you track where feature flags are used in your codebase.

Whether you're a developer managing dozens of toggles or an open-source contributor maintaining a large project, Code References gives you the visibility and tools to keep your flags clean, useful, and safe.

As feature flag management becomes increasingly crucial for modern software development, having quick access to comprehensive documentation can make the difference between smooth deployments and troubleshooting headaches. Today, I'm excited to share how I built a locally running Model Context Protocol (MCP) server that brings Bucketeer's entire documentation directly into AI assistants like Claude and Cursor.

Reducing latency is crucial when delivering a better user experience. Bucketeer has just made a game-changing update for developers using its Node.js and Go server SDKs. This new version allows end-user evaluations to happen locally within the SDK, significantly reducing latency.

As our users base expands worldwide, Bucketeer noticed that latency was becoming an issue for our users who operate in different regions. In response, we decided to implement a multi-region architecture on Google Cloud Platform (GCP) to reduce latency and improve the overall performance of our platform.