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Dataiku vs. Kyvos

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

Kyvos parte de Free por usuario, frente a Dataiku con Custom pricing.

Comparación completa

Dataikudesde Custom pricing
Kyvosdesde Free
Audyense Score22AS*Bajo84ASSólido
PosicionamientoCollaborative data science, analytics, and AI platformUniversal semantic layer for sub-second BI and grounded AI on billions of rows
Plan gratuitoNo
ImplementaciónNube / SaaS, Híbrido, Instalación localNube / SaaS, Instalación local, Híbrido
Mejor encajeMediana empresa, EmpresaMediana empresa, Empresa
Planes de precio
  • TeamCustom pricing
  • EnterpriseCustom pricing
  • Kyvos FreeFree
  • Kyvos on Cloud Marketplace$0
  • Kyvos Managed Service$1
  • Kyvos On Premise$48,000
Visual data preparationPrepares data through visual workflows.
Code notebooksSupports Python, R, and advanced analysis.
Machine learningBuilds and evaluates models in shared projects.
Model deploymentSupports production endpoints and operational workflows.
AI governanceAdds control and traceability to AI projects.
CollaborationBrings technical and business users into common projects.
Cloud integrationsConnects with major clouds and data platforms.
Hybrid deploymentSupports cloud, on-premise, and controlled environments.
Dashboards & visualizationBrowser-based dashboards and visualization combining multiple views into a real-time overview.
Self-service BIBusiness users explore governed models directly or through Power BI, Tableau and Excel without SQL.
Natural language queryKyvos Dialogs provides context-aware conversational analytics over the semantic layer.
Semantic layer / data modelingCore product: one governed model of metrics, dimensions and hierarchies shared across BI and AI.
Embedded analyticsParcialAPIs allow embedding analytics in custom applications, but there is no dedicated embedding SDK or white-label product.
AI featuresMCP server for AI agents, LangChain connectivity and semantic grounding to reduce LLM hallucination.
Data governance & lineageRBAC, row and column-level security, column masking, centralized policy control and visual audit logs.
Public APIFull API support for querying models and programmatic model management, plus an MCP server interface.
Alerts & notificationsParcialNotifications fire on data model and access-pattern changes; no general metric-threshold alerting engine.
Mobile appNoNo dedicated mobile application; consumption is via browser or a connected BI tool's own mobile client.
Integraciones verificadas15+
Valoración agregada · No reviews yet4.8 · 247 reviews
Integraciones
SnowflakeDatabricksAWSGoogle CloudMicrosoft AzureGit+9 más
Microsoft Power BITableauMicrosoft ExcelLookerStrategy (MicroStrategy)Snowflake+9 más
Seguridad y cumplimiento
SOC 2ISO 27001GDPR
SOC 2 Type ISOC 2 Type IIISO 27001HIPAA
Pros
  • Visual and code-based workflows
  • Strong governance and collaboration
  • Broad cloud and model ecosystem
  • Production-oriented platform
  • Query performance on very large datasets is the standout theme, with reviewers reporting reports that previously took minutes returning in seconds
  • Meaningful reduction in cloud warehouse spend because aggregates absorb query load instead of pushing it down to metered compute
  • Works through existing BI tools via native connectors, so analysts keep using Power BI, Tableau or Excel rather than learning a new interface
  • Strong enterprise security and governance depth: row and column-level security, RBAC, SSO, column masking and visual audit logs
Contras
  • Enterprise pricing and implementation
  • Large feature surface
  • Requires operating-model alignment
  • May exceed the needs of a small data team
  • Steep learning curve; effective use assumes solid grounding in multidimensional data modeling and big data environments
  • MDX expertise is needed for advanced work, which slows down teams without prior OLAP experience
  • Initial setup, cube design and build cycles require significant upfront investment before value is realized
  • The admin and modeling UI is described as less intuitive than expected, and cube-scoped models can make cross-dataset exploration feel siloed
Visitar Dataiku ↗Visitar Kyvos ↗