Open Analytics

Product Introduction

1. Definition: Open Analytics is an open-source, self-hostable web analytics platform built on a modern, columnar database (ClickHouse). It is a technical alternative to traditional, cookie-based analytics suites like Google Analytics 4 (GA4).
2. Core Value Proposition: It exists to provide privacy-first, real-time website and product analytics without the complexity, data privacy concerns, or consent banner requirements of legacy tools. Its AI-native architecture, featuring a Model Context Protocol (MCP) server, allows both humans and AI agents to query and interact with analytics data directly.

Main Features

1. Cookieless, Privacy-First Tracking: The platform uses a lightweight (~2 KB) asynchronous JavaScript script that sets no cookies and collects no personally identifiable information (PII). It generates a daily-rotating anonymous hash for visitor identification, ensuring no user profile persists across days or sites. It fully respects browser privacy signals like Global Privacy Control (GPC) and Do Not Track (DNT).
2. Real-Time Dashboard & Live Globe: Provides a real-time view of active visitors, pageviews, and referrers that updates without manual refresh. The live globe visualization geolocates current visitors in real-time on an interactive 3D globe, offering immediate geographical insight.
3. Revenue Attribution & Funnel Analysis: Directly integrates with Stripe via a read-only API key to attribute payments back to the original visit, campaign, and page. Allows creation of multi-step funnels (e.g., sign-up flows) to visualize drop-off points, with counting available per visitor or per session.
4. AI-Native Querying via MCP: Includes a built-in Model Context Protocol (MCP) server, enabling AI assistants and agents (e.g., within Claude Desktop or Cursor) to connect directly to the analytics database. Users can ask questions in plain English ("Which page brings the most signups?") and receive precise answers, moving beyond static reports.
5. Visitor Journey Replay: Tracks and displays the complete, anonymized session path for any visitor, showing every pageview in chronological order along with referral source and device information, enabling deep behavioral analysis.
6. Core Web Vitals Monitoring: Automatically measures key user experience metrics—Largest Contentful Paint (LCP), Interaction to Next Paint (INP), Cumulative Layout Shift (CLS), and Time to First Byte (TTFB)—from real user visits. Data can be segmented by device type to isolate performance issues.

Problems Solved

1. Pain Point: Compliance complexity and user experience degradation caused by mandatory cookie consent banners under regulations like GDPR. Many analytics tools either require banners or have contested compliance status.
2. Pain Point: Data silos and slow, complex reporting in traditional analytics. Marketing and product teams struggle to connect revenue directly to marketing efforts or to get quick answers from their data.
3. Target Audience: Privacy-conscious SaaS founders, product managers, and marketing teams who need accurate, actionable data without legal overhead. Developers and tech companies seeking a self-hosted, open-source analytics solution for data sovereignty. Teams integrating AI workflows who need analytics data accessible to LLMs.
4. Use Cases: A bootstrapped SaaS company needing to track which blog post or campaign led to paid subscriptions without implementing complex e-commerce tracking. A development team requiring a performant, self-hosted analytics tool for internal dashboards. A product team using AI agents to routinely analyze user behavior and generate weekly performance summaries.

Unique Advantages

1. Differentiation: Unlike Google Analytics, it is cookieless and privacy-first by design, eliminating consent banners. Versus other privacy tools (Plausible, Fathom), it offers superior technical depth: full visitor journey replay, built-in revenue attribution, Core Web Vitals, and AI-native access via MCP, often at a competitive price-per-event.
2. Key Innovation: The integration of the Model Context Protocol (MCP) directly into the analytics stack is a pioneering move. This transforms the platform from a passive reporting tool into an active data layer that AI can query and reason over, fulfilling the "AI-native" promise and differentiating it from all major competitors.

Frequently Asked Questions (FAQ)

1. Is Open Analytics GDPR compliant without a cookie banner? Yes. Because it sets no cookies and collects zero personally identifiable information (PII) by design, it falls outside the strictest requirements for prior consent under GDPR. User privacy is enforced technically, not just by policy.
2. How does the self-hosting option for Open Analytics work? The entire platform is open-source (available on GitHub). You can deploy it on your own infrastructure (e.g., AWS, DigitalOcean) using Docker, maintaining full control over your data. The cloud-hosted version offered by the creators is the same software, managed for you.
3. Why might my visitor count be lower than in Google Analytics? This is typically due to Open Analytics' strict adherence to privacy signals. It automatically filters out visits from browsers with GPC or DNT enabled, whereas cookie-based tools often count these visits. The difference represents accurate respect for user opt-outs, not data loss.
4. How does the 7-day free trial work? The trial requires no credit card and provides full access to all features of the chosen plan (Starter, Growth, Pro) for 7 days. If no payment method is added, the trial simply ends—there is nothing to cancel. This contrasts with freemium models where the free tier is limited.
5. Can I import historical data from Google Analytics or Plausible? Yes. The platform supports importing historical event data from exports provided by Plausible, Fathom, and other compatible tools via a ZIP file upload. You can review the data before finalizing the import, and the process is reversible.