Registro lado a lado

Holistics 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 Holistics con $960.

Comparación completa

	Holisticsdesde $960 month	Kyvosdesde Free
Audyense Score	28AS*Bajo	84ASSólido
Posicionamiento	Self-service analytics with a governed semantic layer	Universal semantic layer for sub-second BI and grounded AI on billions of rows
Plan gratuito	No	Sí
Implementación	Nube / SaaS	Nube / SaaS, Instalación local, Híbrido
Mejor encaje	PYME, Mediana empresa, Empresa	Mediana empresa, Empresa
Planes de precio	Entry$960 Standard$1,200 Security Compliance Suite$2,400	Kyvos FreeFree Kyvos on Cloud Marketplace$0 Kyvos Managed Service$1 Kyvos On Premise$48,000
Semantic modeling	SíCurates reusable data models and definitions.	—
Self-service analytics	SíLets business users answer questions without SQL.	—
Dashboard builder	SíCreates narrative and operational reports.	—
dbt integration	SíConnects analytics workflows to dbt projects.	—
Git version control	SíSupports controlled changes to analytics logic.	—
Embedded analytics	SíOffers unlimited viewers in embedded use cases.	ParcialAPIs allow embedding analytics in custom applications, but there is no dedicated embedding SDK or white-label product.
API	SíSupports report data, schedules, jobs, and users.	—
Row-level permissions	SíSupports governed access for sensitive data.	—
Dashboards & visualization	—	SíBrowser-based dashboards and visualization combining multiple views into a real-time overview.
Self-service BI	—	SíBusiness users explore governed models directly or through Power BI, Tableau and Excel without SQL.
Natural language query	—	SíKyvos Dialogs provides context-aware conversational analytics over the semantic layer.
Semantic layer / data modeling	—	SíCore product: one governed model of metrics, dimensions and hierarchies shared across BI and AI.
AI features	—	SíMCP server for AI agents, LangChain connectivity and semantic grounding to reduce LLM hallucination.
Data governance & lineage	—	SíRBAC, row and column-level security, column masking, centralized policy control and visual audit logs.
Public API	—	SíFull API support for querying models and programmatic model management, plus an MCP server interface.
Alerts & notifications	—	ParcialNotifications fire on data model and access-pattern changes; no general metric-threshold alerting engine.
Mobile app	—	NoNo dedicated mobile application; consumption is via browser or a connected BI tool's own mobile client.
Integraciones verificadas	—	15+
Valoración agregada	— · No reviews yet	4.8 · 247 reviews
Integraciones	SnowflakeBigQueryRedshiftPostgreSQLMySQLSQL Server+8 más	Microsoft Power BITableauMicrosoft ExcelLookerStrategy (MicroStrategy)Snowflake+9 más
Seguridad y cumplimiento	SOC 2GDPR	SOC 2 Type ISOC 2 Type IIISO 27001HIPAA
Pros	Public plan pricing Strong modeling and governance Self-service without SQL for end users Embedded and API support	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	High starting price Requires data-modeling setup Smaller brand than large BI suites Advanced security is a separate plan	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
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