Cubes

At a glance

• Editor score
  6.2 / 10
• Pricing
  Free plan
• Best for
  Developers building a lightweight relational OLAP API
• Free plan
  Yes
• Paid from
  Free
• Deployment
  Self_hosted
• Facts checked
  24 Sep 2026

• Where it wins

  • Multidimensional cubes support hierarchies, slicing, dicing and drill-down
  • SQLAlchemy backend supports multiple relational database backends
  • Built-in WSGI server exposes OLAP functions through a JSON API

• Where it doesn't

  • Self-hosted deployment puts operation and integration work on your team
  • Designed for embedded applications rather than standalone end-user BI
  • No built-in AI features are included

Our verdict on Cubes

Cubes is an open-source Python framework and toolkit for developers building reporting and analytical applications. It models data as cubes containing facts, measures, dimensions and hierarchies, making it suited to teams that want to add relational OLAP capabilities inside their own software rather than deploy a standalone business-intelligence product. The framework supports small, mid-market and enterprise use cases, with self-hosted deployment and an API-oriented platform.

Its core strength is the depth of its multidimensional model. Hierarchical dimensions can contain multiple hierarchies, while aggregation browsing, slicing and dicing, and drill-down support common analytical navigation patterns. The built-in relational OLAP backend uses SQLAlchemy and supports multiple database backends and data sources in a workspace. Supported relational systems include Drizzle, Firebird, Informix, Microsoft SQL Server, MySQL, Oracle, PostgreSQL, SQLite and Sybase. A WSGI HTTP OLAP server provides a JSON API, and localization, formatters and command-line tools extend the application toolkit.

Cubes also includes configurable authentication and authorization, including cube-level and cell-based restrictions, which can help developers define access within embedded analytical experiences. The trade-off is operational and product scope: deployment is self-hosted, so the implementing team owns integration and runtime management, while support is centered on documentation. Because Cubes is intended to be integrated into applications, organizations seeking a ready-to-use end-user BI environment or broader platform ecosystem should consider a different category fit. Choose Cubes when a Python-based, relational OLAP API is the priority; avoid it when turnkey analytics or built-in AI capabilities are requirements.

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Cubes pricing

Cubes fact sheet

Alternatives to Cubes

• Google BigQueryA serverless warehouse with separate storage and compute, streaming, ML, and governance.9.4
• Amazon RedshiftA broad AWS-native warehouse for teams balancing SQL analytics, lakes, streaming, and ML.9.3
• Databricks SQL WarehouseA usage-based SQL warehouse for lakehouse analytics, BI, governance, and incremental ETL.9.2

See all Cubes alternatives →

Featured on iTechGuides

Cubes is listed in our OLAP Software directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 7 Oct 2026 · How we research and update