Side-by-side record

Dataiku vs. Kyvos

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

Kyvos starts at Free per seat, versus Dataiku at Custom pricing.

Full comparison

	Dataikufrom Custom pricing	Kyvosfrom Free
Audyense Score	22AS*Low	84ASStrong
Positioning	Collaborative data science, analytics, and AI platform	Universal semantic layer for sub-second BI and grounded AI on billions of rows
Free tier	No	Yes
Deployment	Cloud / SaaS, Hybrid, On-premise	Cloud / SaaS, On-premise, Hybrid
Best fit	Mid-market, Enterprise	Mid-market, Enterprise
Pricing plans	TeamCustom pricing EnterpriseCustom pricing	Kyvos FreeFree Kyvos on Cloud Marketplace$0 Kyvos Managed Service$1 Kyvos On Premise$48,000
Visual data preparation	YesPrepares data through visual workflows.	—
Code notebooks	YesSupports Python, R, and advanced analysis.	—
Machine learning	YesBuilds and evaluates models in shared projects.	—
Model deployment	YesSupports production endpoints and operational workflows.	—
AI governance	YesAdds control and traceability to AI projects.	—
Collaboration	YesBrings technical and business users into common projects.	—
Cloud integrations	YesConnects with major clouds and data platforms.	—
Hybrid deployment	YesSupports cloud, on-premise, and controlled environments.	—
Dashboards & visualization	—	YesBrowser-based dashboards and visualization combining multiple views into a real-time overview.
Self-service BI	—	YesBusiness users explore governed models directly or through Power BI, Tableau and Excel without SQL.
Natural language query	—	YesKyvos Dialogs provides context-aware conversational analytics over the semantic layer.
Semantic layer / data modeling	—	YesCore product: one governed model of metrics, dimensions and hierarchies shared across BI and AI.
Embedded analytics	—	PartialAPIs allow embedding analytics in custom applications, but there is no dedicated embedding SDK or white-label product.
AI features	—	YesMCP server for AI agents, LangChain connectivity and semantic grounding to reduce LLM hallucination.
Data governance & lineage	—	YesRBAC, row and column-level security, column masking, centralized policy control and visual audit logs.
Public API	—	YesFull API support for querying models and programmatic model management, plus an MCP server interface.
Alerts & notifications	—	PartialNotifications 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.
Integrations verified	—	15+
Aggregate rating	— · No reviews yet	4.8 · 247 reviews
Integrations	SnowflakeDatabricksAWSGoogle CloudMicrosoft AzureGit+9 more	Microsoft Power BITableauMicrosoft ExcelLookerStrategy (MicroStrategy)Snowflake+9 more
Security & compliance	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
Cons	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
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