Google Data Studio vs Metabase: Free SaaS vs Free Open Source
Google Data Studio (now Looker Studio) and Metabase are both free BI tools — but they take opposite approaches. Data Studio is Google-managed SaaS with no setup. Metabase is open-source software you self-host for complete data control.
Note: Google rebranded Data Studio to "Looker Studio" in October 2022.
Key Differences
Feature: Price · Google Data Studio: Free (SaaS) · Metabase: Free (self-hosted) or Cloud from $85/mo
Feature: Hosting · Google Data Studio: Google-managed · Metabase: Self-hosted or Metabase Cloud
Feature: Data sources · Google Data Studio: 1,000+ API connectors · Metabase: SQL databases (Postgres, MySQL, BigQuery, etc.)
Feature: SQL access · Google Data Studio: No · Metabase: Yes — SQL Lab built in
Feature: Learning curve · Google Data Studio: Low · Metabase: Low-Medium
Feature: Data modeling · Google Data Studio: Calculated fields · Metabase: Models via SQL
Feature: Governance · Google Data Studio: Basic (3 roles) · Metabase: RBAC, data sandboxing
Feature: Vendor lock-in · Google Data Studio: Google ecosystem · Metabase: None (open source)
Feature: Setup time · Google Data Studio: Instant · Metabase: Hours (Docker/server setup)
When to Use Google Data Studio
- No engineering resources for hosting
- Data from Google ecosystem (GA4, Ads, Sheets)
- Non-technical users building dashboards
- Need 1,000+ API connectors
- Want zero setup and maintenance
When to Use Metabase
- Data lives in SQL databases
- Team has SQL skills
- Data sovereignty and self-hosting matter
- Want RBAC and data sandboxing
- Prefer open-source, no vendor lock-in
- Have engineering to maintain the deployment
A Third Option: Graphed
If Data Studio is too limited and Metabase requires too much engineering, Graphed connects to 350+ sources and builds dashboards from natural language. No SQL, no hosting, no drag-and-drop. AI handles the analysis. Setup in 15 minutes.
The Bottom Line
Data Studio for free managed dashboards. Metabase for free self-hosted analytics with SQL. Choose based on whether you have engineering resources and where your data lives.
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