Looker vs PostHog: Analytics Comparison (2026)
Looker and PostHog both offer analytics platforms with free tiers. Looker utilizes LookML for SQL-based modeling and integrates with Google Cloud and Data Studio, catering to users within the Google ecosystem. PostHog focuses on product analytics, session replay, and feature flags with usage-based pricing and generous free tiers for specific features.
AI Citation Scorecard
How often each is cited by major AI engines when buyers ask analytics questions. Last 90 days across ChatGPT, Perplexity, Gemini, Claude, and Copilot.
Probes run hourly; each (engine × query) combo retests every ~3 days.
Pricing
Key Features
- ✓LookML for SQL-based modeling
- ✓Integration with Data Studio
- ✓Cloud infrastructure on Google Cloud
- ✓Prebuilt and custom integrations
- ✓Product Analytics: 1M events/mo free
- ✓Session Replay: 5K recordings/mo free
- ✓Feature Flags: 1M requests/mo free
- ✓Managed Warehouse: 1M rows/mo free
- ✓Usage-based pricing
- ✓No sales calls required
- ✓Generous free tiers
- ✓Single source of truth for customers
When to choose Looker
Looker is suitable for users who require a strong modeling language like LookML, operate within the Google Cloud environment, and need extensive customization for reports and dashboards. It is beneficial for organizations already using Google Cloud services and Data Studio.
When to choose PostHog
PostHog is suitable for data and product teams looking for a platform with transparent, usage-based pricing and generous free tiers for specific functionalities like product analytics, session replay, and feature flags. It appeals to users who prioritize ease of getting started for free and a single source of truth for customer data.
Frequently Asked Questions
AI visibility profiles
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