Druid vs PostHog
| Tagline | Distributed, column-oriented real-time analytics data store for high-throughput queries | 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 | Mixpanel, Amplitude, Hotjar, Google Analytics |
| GitHub stars | 14k | 35k |
| Language | Java | Python |
| License | Apache-2.0 | MIT |
| Self-host difficulty | 5/5 Advanced | 5/5 Advanced |
| Deploy options | Docker Docker Compose Kubernetes Manual | Docker Compose Kubernetes Manual |
| Managed hosting | ||
| Last updated | yesterday | today |
| View repo | View repo |
Where each falls short
The honest trade-offs — what you give up with each, versus the proprietary tools they replace.
Druid
- No built-in session analytics, funnel analysis, or retention cohorts compared to Mixpanel/Amplitude
- Requires significant infrastructure knowledge (ZooKeeper, deep-storage, coordinator/broker/historical nodes)
- No out-of-the-box user-facing dashboarding — must pair with Superset or Grafana
- Operational cost and cluster management overhead is very high for small teams
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
Both are a similar lift to self-host; 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.