GoatCounter vs PostHog
| Tagline | Easy, privacy-friendly web analytics with no tracking of personal data | All-in-one product analytics, session replay, feature flags, and A/B testing |
| Category | Product & Web Analytics | Product & Web Analytics |
| Replaces | Google Analytics | Mixpanel, Amplitude, Hotjar, Google Analytics |
| GitHub stars | 6k | 40k |
| Language | Go | Python |
| License | EUPL-1.2 | MIT |
| Self-host difficulty | 2/5 Easy | 5/5 Advanced |
| Deploy options | Docker Manual | Docker Compose Kubernetes Manual |
| Managed hosting | ||
| Last updated | 5 days ago | yesterday |
| View repo | View repo |
Where each falls short
The honest trade-offs — what you give up with each, versus the proprietary tools they replace.
GoatCounter
- Minimal by design: no funnels, cohorts, heatmaps, or session replay.
- Event/custom-property tracking is limited compared to product-analytics tools.
- Single-maintainer project, so release cadence can be slow.
PostHog
- Self-hosting the full ClickHouse + Kafka + Postgres + Redis stack is heavy; the project actively steers smaller users toward PostHog Cloud.
- Some enterprise features live under a separate proprietary
eelicense, not pure MIT. - The all-in-one breadth means it is more complex to operate than a focused tool like Mixpanel.
Bottom line
Choose GoatCounter if you want the lower-effort setup; choose PostHog for the larger community and ecosystem. PostHog has seen more recent development. Open each guide below for deploy steps and the full feature gap.
GoatCounter
Easy, privacy-friendly web analytics with no tracking of personal data
PostHog
All-in-one product analytics, session replay, feature flags, and A/B testing