plausible/analytics

Open source, privacy-first web analytics. Lightweight, cookie-free Google Analytics alternative. Self-hosted or cloud.

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Summary Information

Updated 51 minutes ago
Added to GitGenius on September 1st, 2026
Created on December 4th, 2018
Open Issues & Pull Requests: 67 (+0)
GitHub issues: Enabled
Number of forks: 1,844
Total Stargazers: 28,941 (+2)
Total Subscribers: 156 (+0)

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Detailed Description

Plausible Analytics is a privacy-first web analytics platform that serves as a lightweight, cookie-free alternative to Google Analytics, available for self-hosting or cloud deployment.

The tool addresses the tension between wanting website traffic insights and respecting visitor privacy. It collects analytics data without cookies, fingerprinting, or personal data retention, eliminating the need for cookie consent banners. The platform is built on Elixir and Phoenix, using ClickHouse for data storage and PostgreSQL for application data, enabling it to remain lightweight while handling analytics queries efficiently.

Teams should consider Plausible if privacy compliance and simplicity matter more than exhaustive feature parity with Google Analytics. It suits projects where the operator controls the analytics infrastructure or prefers a managed service without vendor lock-in concerns. The tool is positioned as an alternative to Google Analytics rather than a feature-for-feature replacement, so organizations heavily dependent on advanced segmentation, custom events, or complex funnels may find it limiting. Self-hosting appeals to those wanting full data control; the cloud option removes infrastructure overhead for teams comfortable with a third-party host.

The project maintains steady development activity with regular commits across its core components. Work spans the analytics engine, dashboard interface, and infrastructure tooling, indicating ongoing refinement rather than feature churn. The codebase shows consistent attention to both backend systems and user-facing functionality. Development appears distributed across multiple areas of the stack simultaneously, suggesting a team capable of addressing issues across the full application rather than bottlenecking on single maintainers.