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Mode vs. Supaboard

Built from each tool’s researched, reviewed record. Figures are checked against public pricing pages at research time — always confirm current pricing with the vendor before buying.

The short version

Mode starts at Free per seat, versus Supaboard at $85. Supaboard carries the higher aggregate rating (5.0/5 vs 4.5/5).

Full comparison

Modefrom Free
Supaboardfrom $85 per user/month (monthly billing; $99/user/month billed monthly, cheaper with annual)
Audyense Score56AS*Fair52ASLow
PositioningAnalyst-first BI combining SQL, Python, R, dashboards, and advanced analytics.AI-Powered Business Intelligence, Governed Self-Serve Analytics
Free tierYesNo
DeploymentCloud / SaaSCloud / SaaS
Best fitSMB, Mid-market, EnterpriseStartup, SMB, Mid-market, Enterprise
Pricing plans
  • StudioFree
  • BusinessCustom pricing
  • EnterpriseCustom pricing
  • Individual$85
  • Business$199
  • EnterpriseCustom pricing
Python and R notebooksYes—
Interactive dashboardsYes—
Reusable datasetsYes—
Governed metricsYesSupports dbt Semantic Layer integration.—
Custom data appsYes—
Advanced analyticsYes—
Role-based access controlYesEnterprise feature.—
SQL editorYes—
Integrations verified—700+
Aggregate rating4.5 · 330 reviews5.0 · 2 reviews
Integrations
SnowflakeAmazon RedshiftGoogle BigQueryPostgreSQLMySQLMicrosoft SQL Server+6 more
SlackMicrosoft TeamsClaudeCursorChatGPT
Security & compliance——
Pros
  • Native SQL, Python, and R workflow with results flowing into dashboards and reports.
  • Strong bridge between ad hoc analysis, advanced analytics, and self-serve reporting.
  • Reusable datasets and governed metrics reduce repeated analyst work.
  • Custom data apps and embedded analytics expand the use case beyond static dashboards.
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Cons
  • The platform is optimized for analyst-led teams rather than pure business-user self-service.
  • Enterprise identity, access, and support capabilities are not part of the free Studio tier.
  • Pricing and packaging are less transparent for Business and Enterprise.
  • Python/R environments and warehouse performance still require data-team administration.
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