Audyense·
Side-by-side record

Mode vs. Quantum Metric

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 Quantum Metric at Custom pricing. Quantum Metric carries the higher aggregate rating (4.6/5 vs 4.5/5).

Full comparison

Modefrom Free
Quantum Metricfrom Custom pricing
Audyense Score56AS*Fair42AS*Low
PositioningAnalyst-first BI combining SQL, Python, R, dashboards, and advanced analytics.Digital experience analytics platform
Free tierYesNo
DeploymentCloud / SaaSCloud / SaaS
Best fitSMB, Mid-market, EnterpriseMid-market, Enterprise
Pricing plans
  • StudioFree
  • BusinessCustom pricing
  • 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
Session replayYes
AutocaptureYes300+ metrics without manual tagging
Agentic AI insights (Felix AI)Yes
Page performance monitoringYes
Mobile app analyticsYes
Integrations verified
Aggregate rating4.5 · 330 reviews4.6 · 267 reviews
Integrations
SnowflakeAmazon RedshiftGoogle BigQueryPostgreSQLMySQLMicrosoft SQL Server+6 more
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.
  • Enterprise features
  • AI-powered insights
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.
  • Enterprise pricing
  • Implementation complexity
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