Cube

Open source alternative to AtScale, Looker and Tableau Embedded Analytics

21K stars
Active this week
Last commit 1 day ago

Last verified Oct 20, 2026

Power embedded analytics with a self-hosted semantic layer that keeps warehouse metrics consistent across BI tools, SQL, and APIs.

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Read the full breakdownWho it is for, how it works, trade-offs and the FAQ

who it's for

Who Cube is for#

SaaS teams embedding customer dashboards

Cube provides governed metrics and cached APIs for product analytics features shown inside an application.

Skip if:

Skip if all analytics are internal and your BI tool already handles performance well.

Data teams standardizing metric definitions

Use Cube to keep revenue, usage, and operational metrics consistent across BI, notebooks, and application APIs.

Skip if:

Skip if your organization is not ready to maintain semantic models in code.

the problem

The problem it solves#

Analytics teams often define the same metric in dashboards, notebooks, warehouse SQL, and application code. Revenue, activation, and retention numbers drift because each tool has its own formulas, filters, and joins.

The pain grows when product teams embed analytics into customer-facing apps. Developers need fast APIs and caching, while data teams need metric definitions that stay consistent across tools.

how Cube solves it

How it solves it#

Code-defined semantic layer

Models cubes, measures, dimensions, joins, and access rules in code so metrics can be reviewed and versioned.

APIs for embedded analytics

Exposes REST, GraphQL, SQL, and client SDK paths for building dashboards and data apps on top of the same metric definitions.

Caching and pre-aggregations

Supports pre-aggregations and query acceleration so high-traffic embedded analytics do not hit the warehouse for every request.

strengths · trade-offs

Strengths and trade-offs#

Strengths

Metric logic leaves the BI siloCube lets teams define metrics once and serve them to multiple frontends instead of copying SQL between dashboards and apps.
Good fit for embedded analyticsThe API-first model serves product teams building analytics into SaaS applications, not only analysts using internal BI.

Trade-offs

-Semantic modeling is upfront workTeams must design cubes, joins, measures, and cache strategy. For a small internal dashboard, a direct BI connection may be simpler.

versus alternatives

Cube vs alternatives#

Cube vs Looker

Cube and Looker both help teams centralize metric definitions. Looker bundles modeling, exploration, dashboards, and governance into a proprietary BI platform; Cube focuses on an open semantic API layer that developers can embed in products.

Cube is better when analytics must appear inside your application or across multiple tools. Looker is still better when the organization wants a managed BI suite with mature analyst workflows and vendor support.

tech stack · detected from GitHub

What it's built on#

Languages: JavaScriptRustTypeScript
Frameworks: Express
Databases: BigQueryMySQLPostgreSQLSnowflake
Infrastructure: AWSDatabricks
Tooling: Rollup

frequently asked

FAQ#

Is Cube open source?

Cube has an open source core and publishes its code on GitHub.

What is Cube used for?

Cube is used as a semantic layer and API layer for analytics, especially embedded dashboards and customer-facing data apps.

How does Cube compare to Looker?

Looker is a proprietary BI platform with its own semantic modeling layer. Cube focuses on an open source, API-first semantic layer that can power many frontends.

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by category

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104

Hostinger

Run Cube on your own server

Self-host Cube on a Hostinger VPS.

Deploy on Hostinger

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Who Cube is for#