Best Open-Source A/B Testing Tool for Web Applications
Open-source A/B testing tools compared: running experiments, targeting users and reading the results, with live GitHub data on each.
Last updated Repo data refreshed 25 Oct 20264 options
Our pick · growthbook
Our pick is GrowthBook because it is purpose-built for A/B testing and experimentation, offering robust statistical analysis and a modern UI that makes it highly effective for data-driven product decisions. While it requires integration with an existing data warehouse for full analysis, this approach allows for greater flexibility and leverage of existing data infrastructure. This stands apart from PostHog, which is a heavier all-in-one solution that might be overkill if the primary need is A/B testing, or Flagsmith and Unleash, which require more manual work and external tools for comprehensive experiment analysis.
GrowthBook vs PostHog vs Flagsmith vs Unleash: side by side
An open-source platform specifically designed for feature flags, A/B testing, and data-driven experiment analysis.
Best for: Product and engineering teams that prioritize robust, statistically sound A/B testing and feature flagging, and have an existing data warehousing solution.
What's good
Experimentation-focused: built from the ground up for robust A/B testing with strong statistical rigor.
Integrates with data warehouses: directly connects to your existing data stack (Snowflake, BigQuery, Postgres, etc.) for analysis.
Modern UI/UX: offers an intuitive interface for setting up experiments, viewing results, and making data-backed decisions.
Open-source core: provides full self-hostability for the core platform and client SDKs without hidden features.
Watch out for
Requires external data warehouse: not an all-in-one solution; relies on your existing data stack for full analysis.
Smaller community: newer compared to some established tools, which means a smaller ecosystem and fewer third-party integrations.
Analysis might require some data expertise: while guided, interpreting statistical results still benefits from a foundational understanding.
An open-source product analytics suite that integrates powerful feature flags and A/B testing capabilities for web and mobile applications.
Best for: Teams seeking a comprehensive, self-hostable product analytics platform that includes robust A/B testing and experimentation capabilities from the ground up.
What's good
Comprehensive suite: combines product analytics, session replays, heatmaps, and A/B testing into a single platform.
Rich UI: offers a visually engaging interface for defining experiments and analyzing results directly within the platform.
Strong community & rapid development: benefits from significant commercial investment and an extremely active developer base.
Flexible deployment: supports both cloud and robust self-hosting options with various database backends for full data ownership.
Watch out for
Resource-intensive: self-hosting requires significant server resources (Kafka, ClickHouse, Postgres, Redis) and operational overhead.
Can be overkill: its broad feature set might be excessive if *only* A/B testing is the primary requirement.
Initial setup complexity: higher barrier to entry for self-hosting due to the numerous interconnected components.
★ 6,616 starsLicence: BSD-3-ClauseLast commit 24 Oct 2026Latest release 20 Oct 2026Python
A lightweight, open-source feature flag and remote configuration platform that can be used to implement A/B testing by controlling user segments.
Best for: Developers and small teams primarily needing a reliable feature flagging system with the ability to conduct basic A/B tests by integrating with their existing analytics stack.
What's good
Excellent feature flagging: very strong for managing feature rollouts, remote configurations, and dynamic user segmentation.
Simple and fast: easy to integrate client and server-side SDKs with minimal overhead and clear API.
Cloud or self-hosted: offers flexibility in deployment options, including a managed cloud service and full self-hosting.
Clear pricing and open-source distinction: easy to understand what's truly open-source versus cloud-only advanced features.
Watch out for
A/B testing is secondary: built upon feature flags; lacks dedicated in-depth experiment analysis tools and dashboards.
Requires external analytics: you'll need a separate system to track metrics and analyze test results effectively, adding integration work.
Less visual for experiments: the UI is primarily for flag management, not for visual comparisons of experiment variants and performance.
★ 13,901 starsLicence: AGPL-3.0Last commit 23 Oct 2026Latest release 22 Oct 2026TypeScript
A highly scalable open-source feature toggle service optimized for performance and enterprise needs, enabling controlled rollouts and A/B testing.
Best for: Large enterprises and teams prioritizing robust, scalable, and reliable feature flag management, who have an existing analytics infrastructure for A/B test metric collection and analysis.
What's good
Highly performant and scalable: built for large-scale, high-traffic environments with strong emphasis on low latency.
Enterprise-grade features: offers robust user management, roles, audit logs, and change history within its core open-source offering.
Extensive SDKs: provides client and server SDKs for many programming languages, ensuring broad compatibility.
Strong focus on reliability: designed for critical production environments with high availability and resilience.
Watch out for
A/B testing is infrastructural: primarily a flag management system; direct A/B test analysis and statistical evaluation are not built-in.
Requires significant integration: necessitates connecting to external analytics for experiment data collection and analysis, which adds complexity.
UI is feature-centric: the dashboard is focused on flag states, rollouts, and strategies, not visual experiment results or performance metrics.
"Dormant" means no commit pushed to the default branch in the last 12 months. "Archived" means the maintainers have marked the repository read-only on GitHub. "Deprecated" means the project's README says it is deprecated or no longer maintained. Stars, licence, commit and release dates come from the GitHub API (GitLab for projects developed there) and are cached for up to 24 hours. On licences: a custom licence is a licence file that isn't a single standard licence; open-core means the core is open-source and the enterprise code is licensed separately. Check the linked licence file before use. The written analysis is AI-assisted and checked against that data.
Need feature flags rather than experiments?
If the goal is to ship and switch features safely rather than to measure them, the feature flag tools compared page covers rollouts, targeting and kill switches.
Frequently asked questions
Are GrowthBook, PostHog, Flagsmith and Unleash actively maintained?
GrowthBook, PostHog, Flagsmith and Unleash are active: each has had a commit in the last 12 months (most recent: GrowthBook on 25 Oct 2026, PostHog on 24 Oct 2026, Flagsmith on 24 Oct 2026, Unleash on 23 Oct 2026). Status is checked against GitHub daily.
What licences do GrowthBook, PostHog, Flagsmith and Unleash use?
GrowthBook: MIT core + commercial enterprise code (open-core); PostHog: MIT core + commercial ee/ (open-core); Flagsmith: BSD-3-Clause; Unleash: AGPL-3.0. Check each project's licence file before use.
Which is easiest to learn: GrowthBook, PostHog, Flagsmith or Unleash?
By our estimate (1 = easiest, 5 = steepest): GrowthBook 3/5, PostHog 4/5, Flagsmith 2/5, Unleash 3/5. Flagsmith has the gentlest learning curve. This is ExploreGit's AI-assisted estimate, not a measured figure.
