Holistics is a self-service BI platform where data teams model data in code and business users explore with AI assistance.
Holistics lets analytics teams define a semantic data model in code, then gives business users a self-service exploration layer to build charts without writing SQL. Its AI features let users ask questions in natural language and generate visualizations from the governed model, avoiding the metric-definition chaos of ad-hoc analysis. Dashboards, scheduled reports and embedding cover distribution, and everything is version-controlled. It is built for companies that want self-service without sacrificing governance.
Holistics offers a free trial with flat, predictable subscription pricing by platform usage rather than per-seat surprises.
Data teams at mid-size companies that want governed self-service BI with modern engineering practices and no per-viewer pricing.
Data teams define governed metrics once in code, then let marketing and finance self-serve dashboards without analyst queues. Product teams embed customer-facing analytics directly into their SaaS applications.
Connect your warehouse, model your core datasets in the semantic layer and publish a first dashboard before opening exploration to business users.
A free trial is available; production use requires a subscription.
Yes — Holistics supports API-based embedding and automation of reporting.
Versus Looker, Holistics is lighter and cheaper with code-based modeling; versus drag-drop-only BI tools it keeps governance with the data team.
Major cloud warehouses including Snowflake, BigQuery, Postgres and Redshift work with Holistics.
No, the exploration layer and AI querying are designed for non-technical users while the model keeps definitions consistent.
Metrics and dimensions are defined in code once, and every chart draws from those definitions, eliminating the competing numbers problem across departments.
Yes — Holistics queries your warehouse directly rather than extracting data, which keeps governance and performance anchored in your existing data stack.
Users ask questions in natural language and the AI generates visualizations from the governed semantic model, keeping metric definitions consistent even in self-service exploration.
Yes — embedded analytics with permission controls supports customer-facing reporting as a product feature, not just internal BI.
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