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Cubes review

Free#31 of 33 in OLAP Software

An open-source Python toolkit for embedding multidimensional OLAP in applications.

6.2/10Editor score
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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.

Cubes pros and cons

  • 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

Cubes fact sheet, pricing and score →

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