Open-source AtScale alternatives

Cube Core is an open-source semantic layer. You describe your metrics, dimensions, joins and permissions a single time, as code, and Cube exposes that model through SQL, REST and GraphQL APIs to whatever sits downstream: BI tools, custom applications or AI agents. It is headless, meaning it ships no user interface of its own, which leaves you free to design the analytics experience your product needs.

The project argues that every BI tool already relies on a semantic layer to hide data complexity, but most are proprietary and tied to a single platform. Cube aims to make that layer reusable. It connects to any SQL data source, including warehouses such as BigQuery, Snowflake and Databricks, engines such as Amazon Athena and Presto, and application databases such as Postgres. A built-in relational caching engine delivers fast responses and high concurrency for API requests.

The core is written in Rust and JavaScript, and its license is listed as 'Other' on GitHub, so review the terms for commercial use. Cube can be self-hosted, and the company also offers Cube Cloud. It suits data teams building embedded analytics or wanting consistent metric definitions across tools.

MetricFlow is a semantic layer that makes it simpler to define and manage metrics in code. It compiles those metric definitions into clear, reusable SQL, so results stay consistent and accurate when analyzed by relevant attributes, or dimensions. It is maintained by dbt Labs, written in Python, and released under the Apache-2.0 license.

The name reflects its method: a request for a metric is compiled into a dataflow-based query plan that is optimized and translated into engine-specific SQL. It helps with complicated logic such as multi-hop joins between fact and dimension sources and complex metric types, generating queries dynamically instead of requiring hand-written SQL for every cut of the data.

Documentation is hosted in the dbt docs, and a changelog tracks updates. MetricFlow suits analytics engineers and data teams who want a single source of truth for business metrics that can feed dashboards, notebooks, and other tools.

Saiku began in 2010 as an open-source OLAP browser for Mondrian and was rebuilt in 2026 as a modern semantic layer for analytics. It defines business cubes once and then exposes them to different consumers: a drag-and-drop browser interface, Excel through MDX and XMLA, dashboards, and AI agents through MCP.

In the browser, users drag fields onto rows, columns and filters and the application writes MDX for them. The back end uses a fork of Mondrian 4.8 with an Apache Calcite-based SQL planner, which can reach modern engines such as lakehouse systems. A typed REST interface allows AI agents to query data without handling MDX. A demo mode ships with a self-contained H2 database and the FoodMart sample cube.

Saiku is written in Java and licensed under Apache-2.0. It runs from a Docker image, which runs as a non-root user and requires setting an admin password for real deployments, and a hosted demo instance resets nightly. It suits analytics and BI teams that need governed, reusable metric definitions across spreadsheets, dashboards and AI tools.