Backed by 4,800+ GitHub stars and trusted by teams replacing LaunchDarkly and Split with a self-hosted solution, Flipt v2 delivers the first truly Git-native feature management platform that treats feature flags as code stored in your own repositories. The architecture eliminates all database dependencies by building immutable in-memory snapshots from YAML flag definitions on every Git commit, delivering sub-millisecond evaluation latency with zero external runtime dependencies beyond the single Go binary. Multi-environment support maps directly to Git abstractions — separate repositories per environment, different directories within the same repository, or different branches — enabling teams to use their existing branching strategy, pull request workflows, and code review processes for flag changes. The evaluation engine supports boolean flags, multivariate string and numeric variants, segment-based targeting with constraint rules, percentage rollouts, and namespace isolation. Native SCM integration with GitHub, GitLab, BitBucket, Azure DevOps, and Gitea creates merge proposals directly from the UI with GPG-signed commits. Flipt implements the OpenFeature Remote Evaluation Protocol with official providers for Go, Node.js, Python, Java, C#, Ruby, and Web SDKs, enabling vendor-agnostic flag evaluation across all services. The gRPC API with REST HTTP gateway exposes flag management, evaluation, and analytics endpoints. Offline mode continues serving flags when the source repository is temporarily unavailable. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Fair Core License (server) / MIT (client SDKs).

Deployment decoupled from release: Unleash, the most popular open-source feature management platform on GitHub, is a Node.js server backed by PostgreSQL. Ship code dark, then control who sees it through activation strategies: gradual percentage rollouts, targeting by user ID, IP, hostname, or application name, custom constraints against your own context fields, and scheduled or time-limited releases. Strategies stack - a flag activates if any strategy matches - and strategy variants layer A/B versions on top of the on/off decision. Each flag carries per-environment configurations, so a feature can run at 100% in staging while canarying at 5% in production. Backend SDKs (Node.js, Java, Go, Python, Ruby, .NET, PHP, Rust, and more) fetch configuration and evaluate flags locally, so a flag check adds zero network latency to request paths; frontend SDKs for React, Vue, Svelte, iOS, Android, and Flutter evaluate through a proxy layer. Flag hygiene is built in: flags are typed (release, experiment, operational, kill-switch, permission) with expected lifetimes, and Unleash marks overdue flags as potentially stale and surfaces unknown flags your SDKs request but that don't exist. Self-hosting via Docker keeps flag data, targeting rules, and evaluation infrastructure entirely on your side.

With over 6,400 GitHub stars, 130 contributors, and 512 releases, Flagsmith is the open-source feature flag and remote configuration platform that gives development teams granular control over feature releases, remote configuration values, user segmentation, and A/B testing from a single self-hosted dashboard. Feature flags support boolean toggles and remote config values simultaneously — every flag carries both an enabled state and a configurable value, letting teams deploy functional and visual changes without code modifications or app store approvals. User segments target audiences by attributes, percentage rollouts, and custom rules, enabling beta testing, canary releases, and gradual feature rollouts with real-time toggle control. Multivariate flags split traffic across multiple variations with configurable percentage weights for A/B and multivariate testing with analytics integration. The flag evaluation engine runs server-side with local evaluation mode in SDKs for sub-millisecond performance without network calls, supporting 15+ languages including TypeScript, Python, Java, C#/.NET, Go, Ruby, PHP, Swift, Kotlin, Flutter, React, and Next.js. The REST API and webhooks enable integration with CI/CD pipelines, and pre-built connectors exist for Datadog, New Relic, Amplitude, Mixpanel, Segment, Heap, Rudderstack, and Slack. Built on Django with a React frontend, self-hosting deploys via Docker Compose with PostgreSQL, or via Helm charts and the OpenShift Operator for Kubernetes environments. Change history provides a complete audit trail of flag modifications. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. BSD-3-Clause licensed.

Engineering and product teams ship code to production daily with FeatBit, an enterprise feature flag and experimentation platform that controls exactly which user segments, regions, or percentage rollouts see each new capability. When a regression appears in production, operators toggle an instant kill switch, dial back canary weights from full traffic to one percent, or restrict access to internal QA accounts without redeploying containers. Reusable user segments group customers by subscription tier, device property, or custom attributes for consistent targeting across environments. Server-side and client-side SDKs for .NET, Java, Go, Python, Node.js, React, React Native, JavaScript, and OpenFeature synchronize flag states over WebSockets in under 100 milliseconds and evaluate rules locally in memory. Built-in A/B/n experimentation links flag variations to custom conversion events, click tracking, and pageview metrics so teams can validate product hypotheses with live telemetry. Scheduled rollouts, multi-step approval workflows, flag triggers, and webhook integrations with Slack, Datadog, Grafana, New Relic, and OpenTelemetry keep release operations auditable. Role-based access control and single sign-on govern every flag change. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

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.

With over 37,000 GitHub stars and used by teams at Y Combinator, Airbus, and Phantom, PostHog replaces an entire stack of paid analytics tools — Mixpanel, Amplitude, Heap, LaunchDarkly, Hotjar, and Google Analytics — with a single open-source platform where every tool shares a common event layer and user context. Product analytics captures events automatically or via manual instrumentation with HogQL (SQL) access for custom queries, while web analytics provides GA-like dashboards for traffic, conversions, and Core Web Vitals. Session replay records user interactions with DOM snapshots and network waterfall analysis, linking directly to errors and feature flag exposures. Feature flags safely roll out changes to specific cohorts with multivariate support and instant rollback, while experiments run A/B tests with automatic Bayesian significance calculations and revenue attribution. Error tracking captures stack traces linked to session replays and user properties for immediate reproduction context. AI observability monitors LLM generations, traces, token usage, latency, and costs across model versions. The managed data warehouse syncs 120+ external sources including Stripe, Postgres, Salesforce, and HubSpot alongside product events, queryable through a unified SQL editor. An MCP server enables AI agents in Cursor, Claude Code, or VS Code to query analytics and execute SQL directly. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.