PostHog vs Statistics for Strava
| Tagline | All-in-one product analytics, session replay, feature flags, and A/B testing | Self-hosted statistics dashboard for your personal Strava activity data |
| Category | Product & Web Analytics | Product & Web Analytics |
| Replaces | Mixpanel, Amplitude, Hotjar, Google Analytics | Google Analytics, Hotjar |
| GitHub stars | 35k | 1.8k |
| Language | Python | Docker |
| License | MIT | AGPL-3.0 |
| Self-host difficulty | 5/5 Advanced | 3/5 Moderate |
| Deploy options | Docker Compose Kubernetes Manual | Docker Docker Compose |
| Managed hosting | ||
| Last updated | today | 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.
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.
Statistics for Strava
- Limited to Strava as a data source; no support for Garmin, Wahoo, or other fitness platforms
- Read-only analytics — no goal setting, training plans, or social features
- No mobile app; dashboard is web-only
- Requires a valid Strava API OAuth application to be configured before first run
Bottom line
Choose Statistics for Strava 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.
PostHog
All-in-one product analytics, session replay, feature flags, and A/B testing
Statistics for Strava
Self-hosted statistics dashboard for your personal Strava activity data