Count vs Metabase
A detailed comparison to help you choose between Count and Metabase.
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Verdict: Metabase wins
Metabase scores 4.3/5, edging out Count at 3.8/5. Small and mid-size teams wanting self-hostable, easy-to-use BI where AI answers are inspectable and governed by their own metric definitions.
Count Collaborative data analysis notebooks | Metabase Open-source BI with AI query assistance | |
|---|---|---|
| Rating | 3.8 (1 review) | 4.3 (1 review) |
| Pricing Model | freemium | freemium |
| Starting Price | Free tier available | Free tier available |
| Best For | Data teams whose colleagues act on AI-generated numbers and need every claim traced to a source before it reaches a decision. | Small and mid-size teams wanting self-hostable, easy-to-use BI where AI answers are inspectable and governed by their own metric definitions. |
| Free Tier | ||
| API Access | ||
| Team Features | ||
| Open Source | ||
| Tags | free tierteam features | open sourcefree tierteam features |
| Visit Count → | Visit Metabase → |
Count
Pros
- + Citation labelling is real: in my tests, sourced claims showed their source, inferences were tagged as judgements, and an unsourced claim was flagged 'check needed' instead of being invented
- + Multiplayer canvas beats notebooks for concurrent work — three of us edited SQL, annotations and Python on one board with no lock conflicts
- + Per-editor pricing with free viewers keeps costs tied to producers, not audience
- + Live reports recompute from the warehouse rather than freezing snapshots, and automations push them on schedule
Cons
- - Free tier is a taste only: 3 canvases, 1 project, 1 database connection
- - Pro's 4GB compute cap on the Python VM and dataset extracts is awkward for wide extracts; 32GB requires Enterprise
- - 'Unlimited AI credits' is fair-use with an unpublished daily ceiling — get the number in writing before signing
- - Governed semantic layer with admin approvals is Enterprise-only, so mid-tier teams can still ship conflicting metric definitions
Metabase
Pros
- + Semantic-layer-grounded AI: in my testing, questions resolved correctly almost every time where metrics were defined, and the docs openly state the failure mode elsewhere
- + Generated SQL is inspectable before execution and every chart drills through to its query — fixing one wrong definition corrected the answer for everyone
- + Open Source edition is free with unlimited users, questions and dashboards, self-hostable
- + Metabot answered a data question and posted the chart in Slack without a context switch in my test
- + Same price for cloud and self-hosted Pro ($575/month including 10 users), with an MCP server for governed external AI access
Cons
- - Not a machine-learning product — no forecasting, anomaly detection or predictive modelling
- - AI governance controls (token limits, usage analytics, system prompts) sit on Pro at $575/month, a large jump from free
- - Embedded viewer accounts count as billable users on paid tiers
- - Metabot works best in English and only inspects the first 100 tables when no table is specified
- - Complex chart layouts still require SQL or the API
Frequently Asked Questions
Which is better, Count or Metabase?▼
Metabase scores higher at 4.3/5 vs 3.8/5 for Count, making it the better-rated option in our comparison.
What is the difference between Count and Metabase?▼
Count: Count is a collaborative canvas for data analysis, combining SQL, Python and charts in a notebook several people can work in at once. Metabase: Metabase is open-source business intelligence software with a semantic layer, so questions asked in natural language resolve against defined metrics and the underlying query stays inspectable.
Is Count or Metabase more affordable?▼
Count starts at €0/mo, Metabase starts at €0/mo. Check the comparison table above for current pricing.
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