Fathom Analytics vs PostHog
Fathom Analytics and PostHog both answer the same question: how do you know what users actually do? Privacy-first simple analytics with strong EU data isolation and a bypass for ad blockers via your own domain. Product analytics with session replay, feature flags, experiments and surveys bundled into one open source platform. The real split is ownership: PostHog runs inside your project and leaves the operational work with you, while Fathom Analytics runs the hard parts as a service and takes a dependency in exchange.
Verdict
The real split is ownership: PostHog runs inside your project and leaves the operational work with you, while Fathom Analytics runs the hard parts as a service and takes a dependency in exchange.
Pick Fathom Analytics if
- ✓Unlimited websites on one subscription suits people running many small projects.
- ✓Custom domain proxying recovers traffic that ad blockers would otherwise hide.
- ✓EU isolation option keeps European visitor data out of other jurisdictions.
Pick PostHog if
- ✓Funnels, retention and cohorts answer product questions page views never could.
- ✓Session replay lets you watch the exact path a confused user took.
- ✓Feature flags and experiments share the same identity data as the analytics.
| Comparison | Fathom Analytics | PostHog |
|---|---|---|
| Pricing shape | Paid monthly plans by page view volume, with unlimited sites included on every tier. | Generous free monthly event allowance, then usage-based pricing per event, replay and flag request. |
| Frameworks | Next.js, SvelteKit, Nuxt, Laravel, Django, Rails | Next.js, SvelteKit, Nuxt, React Native, Expo, Flutter |
| In one line | Privacy-first simple analytics with strong EU data isolation and a bypass for ad blockers via your own domain. | Product analytics with session replay, feature flags, experiments and surveys bundled into one open source platform. |
Pricing described qualitatively because published plans change often. Checked 2026-08-23. Confirm current terms on Fathom Analytics and PostHog.
Fathom Analytics
Strengths
- ✓Unlimited websites on one subscription suits people running many small projects.
- ✓Custom domain proxying recovers traffic that ad blockers would otherwise hide.
- ✓EU isolation option keeps European visitor data out of other jurisdictions.
- ✓Uptime monitoring is included rather than sold as a separate product.
Tradeoffs
- ✗Closed source, so you cannot audit or self-host the collection code.
- ✗Reporting stays deliberately shallow, with no funnels or cohort analysis.
- ✗Data export options are limited if you want your own warehouse copy.
- ✗Page-view pricing gets expensive for high-traffic content sites.
PostHog
Strengths
- ✓Funnels, retention and cohorts answer product questions page views never could.
- ✓Session replay lets you watch the exact path a confused user took.
- ✓Feature flags and experiments share the same identity data as the analytics.
- ✓Self-hostable and open source, so the data can stay on your infrastructure.
Tradeoffs
- ✗Full event tracking makes it a privacy and consent conversation, not a free pass.
- ✗The client library is heavy compared to a simple page-view script.
- ✗Breadth of features means real onboarding time before anyone gets value.
- ✗Event-based billing rewards discipline and punishes noisy instrumentation.
Frequently asked questions
Is Fathom Analytics or PostHog better?
Neither is better in the abstract. The real split is ownership: PostHog runs inside your project and leaves the operational work with you, while Fathom Analytics runs the hard parts as a service and takes a dependency in exchange. Pick the one whose downside you can absorb, because both upsides are real.
What is the main drawback of Fathom Analytics?
Closed source, so you cannot audit or self-host the collection code. Reporting stays deliberately shallow, with no funnels or cohort analysis.
What is the main drawback of PostHog?
Full event tracking makes it a privacy and consent conversation, not a free pass. The client library is heavy compared to a simple page-view script.
Can you switch from one to the other later?
Usually, at a cost that grows with how much of your product leans on the analytics layer. Plan for it in the data model, not in the framework, and the switch stays survivable.