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Mode vs. Quantum Metric

Elaborado a partir del registro investigado y revisado de cada herramienta. Las cifras se contrastan con las páginas públicas de precios en el momento de la investigación — confirma siempre el precio actual con el proveedor antes de comprar.

La versión corta

Mode parte de Free por usuario, frente a Quantum Metric con Custom pricing. Quantum Metric tiene la valoración agregada más alta (4.6/5 frente a 4.5/5).

Comparación completa

Modedesde Free
Quantum Metricdesde Custom pricing
Audyense Score56AS*Aceptable42AS*Bajo
PosicionamientoAnalyst-first BI combining SQL, Python, R, dashboards, and advanced analytics.Digital experience analytics platform
Plan gratuitoNo
ImplementaciónNube / SaaSNube / SaaS
Mejor encajePYME, Mediana empresa, EmpresaMediana empresa, Empresa
Planes de precio
  • StudioFree
  • BusinessCustom pricing
  • EnterpriseCustom pricing
Python and R notebooks
Interactive dashboards
Reusable datasets
Governed metricsSupports dbt Semantic Layer integration.
Custom data apps
Advanced analytics
Role-based access controlEnterprise feature.
SQL editor
Session replay
Autocapture300+ metrics without manual tagging
Agentic AI insights (Felix AI)
Page performance monitoring
Mobile app analytics
Integraciones verificadas
Valoración agregada4.5 · 330 reviews4.6 · 267 reviews
Integraciones
SnowflakeAmazon RedshiftGoogle BigQueryPostgreSQLMySQLMicrosoft SQL Server+6 más
Seguridad y cumplimiento
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
Contras
  • 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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