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Cube

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27 reviews
  • 1 profiles
  • 3 categories
Average star rating
4.5
Serving customers since
2019

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Star Rating

22
4
0
0
1

Cube Reviews

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Profile Name
Star Rating
22
4
0
0
1
Ryan W.
RW
Ryan W.
UI/UX Engineering Team Lead at Systems Alliance, Inc.
03/06/2023
Validated Reviewer
Verified Current User
Review source: Organic

Perfect for building white label reports embedded in SaaS applications

|Cube
The most useful part about Cube is its ability to serve as a layer between the database and the application. Caching, data aggregation, and query generation have greatly decreased time-to-value for reporting features and enhanced the performance of the application for users.
Olivier D.
OD
Olivier D.
03/05/2023
Validated Reviewer
Verified Current User
Review source: Organic

A polyvalent and effective semantic layer

|Cube
A complete semantic solution with a rich syntax, caching, access controls and suite of APIs. Easy to set up, flexible and scalable.
Angelo D.
AD
Angelo D.
03/03/2023
Validated Reviewer
Review source: Organic

Build your BI lighting fast

|Cube
It's preatty easy to inject custom security, plus the definition of the cubes is very intuitive. Sometimes it may get hard having in result the query you actually need, but tuning the cubes so far it was always possible to instrument it returning the needed query.

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HQ Location:
San Francisco, CA

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@the_cube_dev

What is Cube?

Cube is the agentic analytics platform — built on a semantic layer. Cube is the AI-native generation of business intelligence. It's one platform for two use cases: internal BI, where data teams and business users analyze their own company's data, and embedded analytics, where software companies ship analytics inside their own products. The same governed semantic layer powers both. What makes AI answers trustworthy in production is the semantic layer underneath them. Cube was built around the semantic layer from day one rather than retrofitting one onto a dashboard- or notebook-first tool. The data team's governed metric definitions stay intact while AI constructs ad-hoc calculations on top of them — so you get governance and flexibility at the same time, not one at the expense of the other. That's why teams like Brex chose Cube to power AI-driven analytics at production scale. With Cube, data teams can: Model metrics and business definitions once, in a SQL-first semantic layer, and serve them consistently to every downstream consumer — AI agents, BI, spreadsheets, and embedded apps. Let business users ask questions in natural language through Analytics Chat, with answers grounded in the governed model instead of guessed from raw tables. Bring analytics to where people already work — Slack, and any MCP-compatible agent like Claude or ChatGPT via the Cube MCP server. Embed analytics in customer-facing products with multi-tenancy, row-level security, and query performance under load — choosing from a chat API, drop-in iframes, embedded creator mode, or data APIs. Apply software engineering best practices to analytics: version control with Git, CI/CD, isolated environments, and pre-aggregation caching for fast queries and lower warehouse spend. Cube Core, the open-source semantic layer at its foundation, has years of production use across a large developer community — battle-tested infrastructure that commercial-only tools can't match. Cube sits on top of your cloud data warehouse (Snowflake, BigQuery, Redshift, Databricks); it doesn't replace it.

Details

Year Founded
2019
Website
cube.dev