PostHog is an open source, all-in-one platform designed for product teams and developers who need deep insights into user behavior without vendor lock-in. It unifies product analytics, web analytics, session replays, feature flags, A/B testing, error tracking, surveys, data warehousing, and CDP capabilities into a single stack. Built with Python and React/TypeScript, PostHog gives teams full control over their data by allowing self-hosting while offering a generous free tier on PostHog Cloud. Unlike fragmented SaaS tools, it eliminates the need to juggle multiple platforms — everything from event capture to LLM analytics runs in one place, with all data owned by the user. This makes it ideal for startups, mid-sized product teams, and enterprises prioritizing data privacy, cost efficiency, and developer autonomy.
What You Get
- Product Analytics - Autocapture or manually instrument events to analyze user behavior with visual funnels, retention charts, and SQL-based exploration. No need to pre-define events; track anything post-deployment.
- Web Analytics - GA-like dashboard with real-time metrics for page views, conversions, web vitals (LCP, FID, CLS), and revenue tracking without external dependencies.
- Session Replays - Watch real user sessions with mouse movements, clicks, and scroll behavior to diagnose UX issues, bugs, or drop-off points across web and mobile apps.
- Feature Flags - Safely roll out features to specific users or cohorts using targeted rules, with rollback and gradual rollout capabilities directly in the UI.
- Experiments (A/B Testing) - Set up and measure statistical significance of product changes using no-code experiment builders or custom SQL-based goal metrics.
- Error Tracking - Automatically capture frontend and backend exceptions, group them by type, and receive alerts with stack traces to reduce MTTR.
- Surveys - Deploy no-code surveys (NPS, CSAT, feedback) or build custom forms to collect user input directly within the product interface.
- Data Warehouse & CDP - Sync data from Stripe, HubSpot, and other sources; query external data alongside product events using SQL. Transform and route data to 25+ destinations via real-time or batch pipelines.
- LLM Analytics - Capture traces, latency, cost, and generation output for AI-powered features to monitor performance and optimize LLM usage.
- Workflows - Automate actions like sending Slack messages, triggering emails, or updating CRM records based on user events or conditions without writing code.
- Self-hosting with Docker - Deploy the full platform in one line using
curl and Docker, with full control over data residency and infrastructure.
Common Use Cases
- Building a multi-tenant SaaS dashboard with real-time analytics - Teams use PostHog to track feature usage per tenant, set up cohort-based retention analysis, and visualize revenue attribution using SQL queries against synced Stripe data.
- Creating a mobile-first e-commerce platform with 10k+ SKUs - Developers implement PostHog’s React Native and iOS/Android SDKs to track product views, cart additions, and checkout drop-offs — then use session replays to identify UI friction points.
- Problem: High churn due to unexplained user drop-offs → Solution: PostHog’s session replays and event autocapture reveal users get stuck on a form field, prompting UI fixes that reduce churn by 30%
- DevOps teams managing microservices across multiple cloud providers - Engineers use PostHog’s Python and Node SDKs to capture backend errors, correlate them with frontend events, and trigger alerts — all while syncing cloud billing data to analyze cost per feature.
Under The Hood
PostHog is an open-source platform designed to unify analytics, feature flags, and product experimentation into a single, extensible system. It supports a wide range of data processing and observability workflows through modular architecture and cross-language integration.
Architecture
PostHog follows a layered, modular architecture that enables seamless interaction across frontend, backend, and infrastructure domains.
- The system uses a layered approach with clear separation between UI, API, and data processing layers
- Modular components such as hogvm, hogql-parser, and plugin-transpiler handle distinct functional domains
- Design patterns like command, strategy, and factory are consistently applied to manage complexity
- Cross-technology integration is supported through TypeScript, Python, Rust, and WebAssembly
Tech Stack
The platform is built using a multi-language tech stack with strong emphasis on modern web and backend frameworks.
- Built primarily with Python 3.12 and TypeScript, leveraging Django and React for core functionality
- Relies on PostHog’s internal packages and popular libraries like kea, temporalio, and react for robust development
- Employs Docker, pnpm, Parcel, and turbo for scalable builds and efficient deployment workflows
- Comprehensive testing ecosystem includes Jest, pytest, and snapshot testing for broad code coverage
Code Quality
PostHog maintains a mature approach to testing and error handling, with consistent code style and structure.
- Extensive test coverage across frontend, backend, and integration components ensures reliability
- Error handling is implemented with graceful failure mechanisms and clear exception management
- Code consistency is enforced through established style guides and linting configurations
- Some technical debt exists in duplicated logic and nested structures, indicating room for refactoring
What Makes It Unique
PostHog stands out through its modular design and innovative use of domain-specific tools for performance and extensibility.
- Extensible core components allow for easy integration of custom data pipelines and observability tools
- Asynchronous selector-based querying enables flexible filtering and caching in data warehouse models
- Rust CLI tools are leveraged for performance-critical operations such as sourcemap handling and migration
- Developer-centric tooling supports local environments and validation workflows for seamless development