Google Analytics and PostHog solve different jobs, even though both get called 'analytics.' Google Analytics is a free, event-based platform built for…
| Google Analytics | posthog | |
|---|---|---|
| Primary category | Analytics | Analytics |
| Rating | Not documented | Not documented |
| Pricing model | Freemium | Freemium |
| Starting price | Free (Analytics 360 enterprise tier starts around $50,000/year) | Free (generous usage limits); paid plans are usage-based after free allowances |
| Free plan | Yes | Not documented |
| Free trial | Not documented | Not documented |
| Platforms | Web, iOS, Android | Not documented |
| Team collaboration | Not documented | Not documented |
| AI features | Yes | Not documented |
| Public API | Yes | Not documented |
Core use case
Google Analytics: Event-based platform for tracking website/app traffic, conversions, and marketing performance
posthog: All-in-one product analytics platform combining event tracking, feature flags, session replay, and A/B testing
The two tools are optimized for fundamentally different questions — marketing attribution versus in-product user behavior and experimentation
Pricing model
Google Analytics: Free GA4 Standard tier with all core reporting, plus a custom-priced Google Analytics 360 enterprise tier
posthog: Freemium pricing model, though specific plan tiers and pricing are not documented here
Buyers need to know if there's a genuinely free tier and what upgrading costs before committing to a tool
Feature flags
Google Analytics: Not documented as a Google Analytics capability
posthog: Native feature flags to toggle features for specific users or segments
Feature flags let engineering teams roll out and control features safely without a separate flagging tool
Session replay
Google Analytics: Not documented as a Google Analytics capability
posthog: Session replay to watch recordings of real user sessions
Session replay shows exactly how users interact with a product, which aggregate reports can't reveal
A/B testing / experimentation
Google Analytics: Not documented as a dedicated A/B testing feature; GA offers audience building for remarketing and segmentation instead
posthog: Built-in A/B testing to run and measure experiments
Native experimentation tooling lets teams validate product changes with real user data without a separate testing platform
Data export and warehousing
Google Analytics: BigQuery Export links a property to BigQuery for raw, unsampled event-level data and custom SQL analysis
posthog: Not documented
Raw data export matters for teams that need long-term retention or custom analysis beyond a tool's default dashboards
Ads and marketing integration
Google Analytics: Google Ads Integration connects conversion data and audiences directly to Google Ads for attribution and remarketing
posthog: Not documented
Direct ad-platform integration is essential for closing the loop between marketing spend and conversions
AI and predictive features
Google Analytics: Predictive Metrics and AI Insights surface machine-learning-based metrics like purchase and churn probability
posthog: Not documented
Predictive signals can help teams prioritize retention and revenue efforts before problems become visible in standard reports
Cross-platform measurement
Google Analytics: Combines web and mobile app data from the same business into a single GA4 property via the Firebase SDK
posthog: Not documented
Unified web-and-app reporting avoids stitching together separate analytics setups for each platform
Learning curve
Google Analytics: Documented as having a steep learning curve for teams coming from Universal Analytics' session-based reports
posthog: Not documented
A steeper learning curve affects how quickly a team can get value from a tool without dedicated analytics expertise
| Feature | Google Analytics | posthog |
|---|---|---|
| Event tracking | Available | Available |
| Automatic/enhanced event capture | Available | Not documented |
| Funnel and path analysis | Available | Available |
| Feature | Google Analytics | posthog |
|---|---|---|
| Feature flags | Not documented | Available |
| A/B testing | Not documented | Available |
| Audience/segment building | Available | Not documented |
| Feature | Google Analytics | posthog |
|---|---|---|
| Session recording/replay | Not documented | Available |
| Feature | Google Analytics | posthog |
|---|---|---|
| Raw data export (BigQuery) | Available | Not documented |
| Ads platform integration | Available | Not documented |
| Custom dimensions, metrics, conversions | Available | Not documented |
| Feature | Google Analytics | posthog |
|---|---|---|
| Predictive metrics (e.g. purchase/churn probability) | Available | Not documented |
| Feature | Google Analytics | posthog |
|---|---|---|
| Unified web + mobile app measurement | Available | Not documented |
| Feature | Google Analytics | posthog |
|---|---|---|
| Free tier | Available | Available |
| Custom/enterprise pricing tier | Available | Not documented |
Starting price reflects the lowest paid tier, not the full cost for every team size or usage level.
Pros
Cons
Pros
Cons
Google Analytics has a clearly documented free tier (GA4 Standard) covering core reporting, with a custom-priced Google Analytics 360 tier for enterprise needs; PostHog's pricing model is documented as freemium, but specific plan pricing isn't available in the data here, so a direct cost comparison beyond 'both offer a free entry point' can't be made.
Not exactly — PostHog is a product analytics platform that adds feature flags, session replay, and A/B testing on top of event tracking, while Google Analytics is focused on website/app traffic, marketing attribution, and conversion reporting; both track events, but they're built for different jobs.
It has a documented steep learning curve, particularly for teams used to the older Universal Analytics' session-based reports, since GA4's event-based model changed report names, default metrics, and how funnels and conversions are set up.
PostHog can cover in-product event tracking and behavior analysis, but Google Analytics offers documented capabilities — like native Google Ads integration, BigQuery export, and cross-platform web/app measurement — that aren't confirmed as part of PostHog's feature set, so replacing GA entirely would leave those marketing-attribution capabilities uncovered.
PostHog documents both feature flags (to toggle features for specific users or segments) and session replay (recordings of real user sessions) as built-in capabilities; neither is documented as part of Google Analytics.
Google Analytics is built around marketing and traffic questions — acquisition, conversions, and Google Ads attribution — while PostHog is built around product questions — in-app behavior, feature rollout, and experimentation — so the better fit depends on which team and use case is driving the decision.
Read the full Google Analytics review · Read the full posthog review