Semantic & metrics layers · Cube Dev, Inc.

Cube

Open-source headless semantic layer that defines metrics once and serves them via SQL, REST, and GraphQL APIs to any BI tool or app.

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Cube is a headless semantic layer: it sits between the data warehouse and every downstream consumer, so metrics, dimensions, and joins are defined once in code (YAML or JavaScript/Python data models) rather than redefined inside each BI tool. Client applications, dashboards, and BI tools query Cube through SQL, REST, or GraphQL APIs, and Cube compiles those requests into warehouse SQL while handling caching, pre-aggregations, and access control centrally. This differs from a BI-native semantic layer (like Looker's LookML) in that Cube is tool-agnostic — it can sit under Tableau, Power BI, a custom React app, and an LLM-based text-to-SQL layer at the same time. Cube Core is open source and self-hostable; Cube Cloud adds managed hosting, orchestration, and a visual modeling UI. It suits teams standardizing metric definitions across multiple front ends rather than a single BI tool.

At a glance

Vendor Cube Dev, Inc.
Pricing model Subscription
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Teams needing one governed metric definition served consistently across multiple BI tools and applications.

Pricing

Cube Core is free to self-host; Cube Cloud is priced per developer per month with an Enterprise tier for larger deployments.

Plan Price Notes
Free $0 For hobbyists and small projects
Starter $40/developer/month For teams streamlining data workflows
Premium $80/developer/month Adds Explorer ($40/user/mo) and Viewer ($20/user/mo) seat roles
Enterprise Custom pricing For enterprises needing added security and support

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Headless, API-first semantic layer (SQL, REST, GraphQL)
  • Data modeling in YAML or JavaScript/Python
  • Pre-aggregations for query acceleration and cost control
  • Row- and column-level access control
  • Compatible with any BI tool via its SQL API
  • Caching layer independent of the source warehouse
  • Visual modeling and orchestration UI in Cube Cloud

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

AtScale Universal semantic layer that models metrics once on top of a cloud data warehouse and exposes them to any BI tool without data movement. Malloy Open-source modeling and query language that compiles to SQL, letting analysts define reusable metrics and explore data without repeating joins. dbt Transforms data already in the warehouse with SQL SELECT statements, adding version control, testing, documentation and lineage.

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Terms to know

Semantic layer A layer that defines business metrics and dimensions once, centrally, so every tool and report uses the same numbers. Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. Business intelligence (BI) The practice of turning company data into dashboards, reports and metrics that guide business decisions. Embedded analytics Charts, reports or dashboards built directly into another application instead of a separate BI tool. OLAP (online analytical processing) A category of technology optimized for fast, multi-dimensional aggregation and slicing of data for analysis. Data warehouse A centralized, structured database optimized for fast historical reporting rather than day-to-day transactions.

Related guides

How to build a metrics layer Define each metric once, in code, so every tool that queries revenue or churn gets the same number — and govern changes to it. How to choose a semantic layer Semantic layers differ by where they sit relative to the warehouse and who they serve — one BI tool, every BI tool, an app, or an AI agent.