Open Analytics 1.0

Product Introduction

1. Definition: Open Analytics 1.0 is a privacy-first, AI-native web analytics platform. Technically, it is a server-side analytics solution that operates without client-side cookies, using a lightweight JavaScript script for data collection and ClickHouse for high-performance data querying.
2. Core Value Proposition: It exists to provide a simple, powerful, and privacy-compliant alternative to complex platforms like Google Analytics 4 (GA4). Its primary value is delivering actionable insights—especially revenue attribution from tools like Stripe and Polar—through a real-time dashboard and an AI chat interface, all without requiring cookie consent banners.

Main Features

1. Cookieless, Privacy-First Tracking: The platform uses a daily-rotating anonymous hash to identify sessions, collecting no personal data (names, emails, IP addresses). It honors browser privacy signals like Global Privacy Control (GPC) and Do Not Track (DNT), discarding those requests entirely. This architecture makes it inherently GDPR-compliant and eliminates the need for intrusive cookie consent pop-ups.
2. AI-Powered Analytics & MCP Server: A core innovation is the integrated AI chat assistant. Users can ask questions about their data in plain English (e.g., "Which page brings the most signups?") and receive direct answers. Furthermore, it provides a Model Context Protocol (MCP) server, allowing direct integration with AI assistants like ChatGPT and Claude, enabling querying of analytics data from within those chat interfaces.
3. Revenue Attribution & Funnel Analysis: Open Analytics directly integrates with payment platforms (Stripe, Polar) using read-only API credentials. It attributes revenue events back to the specific website visit, source, and campaign that generated them. Combined with custom funnel creation, users can visualize the complete conversion path and identify precise drop-off points where potential revenue is lost.
4. Real-Time Dashboard & Live Globe: The dashboard updates in real-time as visitors arrive, showing current sessions, top pages, and referrers without manual refresh. A distinctive "Live Globe" visualization plots active visitors on a 3D globe in real-time, geolocated to their country.
5. Open Source & Self-Hostable: The entire codebase is licensed under AGPLv3 and available on GitHub. Users can choose to self-host the platform on their own infrastructure for full data sovereignty or use the managed cloud service hosted in Helsinki, Finland. This includes the ability to import historical data from tools like Umami, Plausible, and Fathom.

Problems Solved

1. Pain Point: The complexity and privacy compliance burden of traditional analytics. Google Analytics 4 is notoriously difficult to configure and interpret, while also requiring cookie consent management under regulations like GDPR. Open Analytics simplifies the interface and removes the compliance headache by design.
2. Target Audience: SaaS founders, product managers, marketing teams, and developers at small to medium-sized businesses who need clear insights into traffic quality and revenue impact without becoming data analysts. It's also ideal for privacy-conscious organizations and those in regulated industries.
3. Use Cases: A SaaS company can connect its Stripe account to see which blog post or Product Hunt launch actually drove paying customers. A marketing team can build a funnel for a sign-up flow to see where prospects abandon the process. A developer can self-host the platform to maintain complete control over their user data.

Unique Advantages

1. Differentiation: Unlike GA4, it is simple and privacy-first. Unlike other privacy-focused tools (Plausible, Fathom), it natively includes revenue attribution, AI chat, and a self-hostable option with an MCP server. The comparison table highlights its all-inclusive approach, where features like funnels, user journeys, and Core Web Vitals are included on all plans, not offered as expensive add-ons.
2. Key Innovation: The combination of AI-native querying (via chat and MCP server) with cookieless, server-side architecture and direct revenue attribution. This creates a closed-loop system where users can not only see traffic data but also directly ask which actions led to business outcomes, all within a privacy-by-design framework.

Frequently Asked Questions (FAQ)

1. Is Open Analytics GDPR compliant without a cookie banner? Yes. Because it uses a cookieless tracking method and does not collect personal data, it falls outside the scope of cookie consent requirements under GDPR. It also respects GPC and DNT signals, providing a privacy-first default.
2. How does the revenue attribution feature work technically? You connect your Stripe or Polar account using a read-only API key. Open Analytics then correlates payment events with anonymous visitor sessions stored in its ClickHouse database, matching them based on timestamps and user identifiers you can pass via the tracking script, attributing the revenue to the original traffic source.
3. Can I migrate my historical data from Google Analytics to Open Analytics? While direct GA4 import is not specified, Open Analytics supports importing history from other privacy-focused tools like Plausible, Fathom, and Umami via a ZIP file upload. This allows you to bring in benchmark data when switching.
4. What is the performance impact of the Open Analytics tracking script? The script is minimal, approximately 2 KB when compressed (gzipped). It loads asynchronously, meaning it does not block page rendering, and it sends the first pageview beacon before its own configuration is fully fetched, minimizing any impact on site speed or Core Web Vitals.
5. What happens if I exceed my monthly event limit? The pricing is tiered based on event volume (e.g., 50K for Starter). The documentation implies you can upgrade plans at any time. For self-hosted instances, limits are defined by your own server infrastructure. The cloud service likely requires upgrading to the next tier to continue tracking.