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Comparison

Preset (AI-Native BI on Apache Superset) vs Metabase

Preset (AI-Native BI on Apache Superset) and Metabase both land on Depends.

Preset (AI-Native BI on Apache Superset) versus Metabase
ComparePreset (AI-Native BI on Apache Superset)Metabase
VerdictDependsDepends
Best forSQL-comfortable data teamsStartups wanting free BI on existing databases
Who it's not forSmall teams needing a simple dashboard toolTeams with nobody to own hosting and patching
PrivacyPreset is SOC 2 Type II with SSO/SAML, SCIM, RBAC, row-level security, and audit logging; the CVEs below affect upstream Apache Superset, which the managed service patches.11Active 2026 vulnerability stream . four critical CVEs including an unauthenticated SQL injection; prompt patching is non-optional.15
Support qualityNo support-quality evidence in sourcesNo support-quality evidence in sources
Public sentimentReviewers praise Superset's visualization breadth and low cost but say Preset forces SQL and lacks drill-downs.³Users like the query builder and SQL access; recurring gripes are exports, dashboard customization, and slow queries on big datasets.
Biggest gotchaPricing reports conflict: $25/user/mo vs AU$2,000-5,000/mo tiers. Get a written quote first.⁶Self-hosting means you own patching; 2026 fixes spanned 55.13, 56.3, 57.1 and later

Pick Preset (AI-Native BI on Apache Superset) when

  • SQL-comfortable data teams
  • Mid-size orgs replacing legacy BI
  • Product teams embedding analytics
  • Cost-sensitive teams wanting an open-source escape hatch

When Preset (AI-Native BI on Apache Superset) is not a fit

  • Small teams needing a simple dashboard tool
  • Non-technical users who can't write SQL
  • Teams wanting Metabase-style no-code exploration
  • Buyers needing built-in drill-downs today

Pick Metabase when

  • Startups wanting free BI on existing databases
  • Product teams embedding analytics in their app
  • SQL-comfortable data teams
  • Self-hosters with patching discipline

When Metabase is not a fit

  • Teams with nobody to own hosting and patching
  • Security-sensitive orgs handling regulated data
  • Non-technical teams needing polished exports and dashboards
  • Analysts on huge datasets where query speed matters

Sources

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  11. security
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  16. security