PostHog
Open-source product analytics platform with AI-powered querying and self-hosted option - 50,000+ companies including Airbus and Y Combinator use it for user insights.
PostHog is an open-source product analytics platform that provides event tracking, session replay, feature flags, A/B testing, and surveys in a single product, with the option to self-host the entire platform for full data ownership. Founded in 2020 by James Hawkins and Tim Glaser (Y Combinator W20 batch) in San Francisco, PostHog raised $27 million at a $225 million valuation and serves 50,000+ companies on its cloud and self-hosted tiers. The platform's Max AI agent answers product analytics questions in plain English - querying events, building funnels, and generating insights without requiring teams to write SQL or learn a proprietary query language. PostHog's LLM observability tooling tracks token usage, latency, costs, and errors for AI-powered applications, making it a natural choice for engineering teams building LLM features who want user analytics and AI metrics in one tool. The fully open-source codebase is available on GitHub with 23,000+ stars, and self-hosted deployments have no usage limits.
Key Features
- Max AI agent answers natural language product analytics questions - builds funnels, cohorts, and event queries from a plain-text description
- LLM observability tracks token usage, model latency, error rates, and cost per AI call for teams building LLM-powered product features
- Session replay captures user interactions with privacy-masking, linked directly to the same event data as the analytics dashboard
- Feature flags enable percentage rollouts, A/B tests, and targeted feature releases with analytics measuring impact automatically per variant
- Self-hosted deployment on any Kubernetes cluster or AWS/GCP/Azure instance with no usage limits and full data residency control
- Event autocapture records clicks, page views, and form submissions automatically without manual instrumentation for every tracked element
- Surveys collect user feedback at specific in-product moments with targeting rules based on the same event data as analytics segments
Use Cases
- Privacy-conscious engineering teams self-hosting PostHog to keep all user event data inside their own infrastructure without third-party data sharing
- LLM product teams using PostHog to track both traditional user analytics and AI token usage and latency in a single dashboard per feature
- Startups consolidating analytics, session replay, feature flags, and A/B testing into one PostHog workspace instead of paying for four separate tools
- Growth teams using Max AI to ask questions about user behavior in plain English rather than learning SQL or a proprietary analytics query language
Pros
- Open-source self-hosting with no usage limits is unique among full-featured product analytics platforms - truly free for teams with the infra to run it
- LLM observability built into the same platform as user analytics positions PostHog ahead of competitors for teams building AI-native applications
- Max AI natural language querying lowers the analytics skill floor - non-technical product managers can explore data without analyst support or SQL training
Cons
- Self-hosting requires Kubernetes experience and ongoing infrastructure management - teams without DevOps capacity should use the managed cloud tier
- PostHog cloud pricing can become expensive for high-event-volume applications - teams with millions of daily events should calculate costs before migrating
- Some advanced analytics features like complex multi-touch attribution modeling are less mature than dedicated BI platforms like Amplitude or Mixpanel
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