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Cube

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

Profile Name

Star Rating

22
4
0
0
1

Cube Reviews

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Profile Name
Star Rating
22
4
0
0
1
William T.
WT
William T.
03/07/2023
Validated Reviewer
Verified Current User
Review source: Organic

Deliver Data Insights Quick and Easy

|Cube
Cube makes it incredibly easy to expose our reporting data to our businesses. Our shift to using Cube to serve our reporting data has cut the deployment process of new features and bug fixes from hours to minutes. Also, the onboarding process for getting up to speed with the product was swift, and the Cube team has been very responsive to feature requests.
DS
Dhyan S.
Product Manager at RamSoft
03/07/2023
Validated Reviewer
Verified Current User
Review source: Organic

Flexible modern solution for creating embedded analytical application

|Cube
* Flexibility and Customization it provides to an embedded analytical application * Speed of development * Good Support
Jonathan B.
JB
Jonathan B.
Data Product Engineer at South China Morning Post
03/06/2023
Validated Reviewer
Verified Current User
Review source: Organic

Achieving metrics consistency across BI tools

|Cube
The semantic layer concepts is easy to onboard and get a PoC working. The documentation is well-written and exhaustive. But what I like the most is the pre-aggregations which allow us to warm up queries and reduce query time by 10x or more!

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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