Is Magnum BI a free alternative to Tableau, Looker or Power BI?

Yes. Magnum BI is open source under the GNU AGPL v3.0 and free to self-host, including for commercial work. It focuses on governed metrics defined as YAML, drag-and-drop dashboards, drill-down, period comparison, row-level filters and one-line embedding.

How is Magnum BI different from Apache Superset and Metabase?

Magnum BI is a single stateless Rust server, shipped as one Docker image with a Compose file and a Helm chart. Models and dashboards are plain YAML that can be reviewed in git, and every change, from the UI, files or the API, goes through the same validation. Each organisation gets its own isolated ScramDB instance for dashboards and query cache.

Which databases does Magnum BI connect to?

PostgreSQL, ScramDB, BigQuery and DuckDB, including Parquet, CSV, JSON, Avro and Apache Iceberg files on S3, GCS or HTTPS. Snowflake and MySQL are planned.

Does Magnum BI work with AI agents?

Yes. Coding agents can install it from one prompt, and its built-in MCP endpoint lets Claude, Cursor and other MCP clients run queries and build dashboards with scoped, expiring API tokens.

🌺 Compare BI tools

An open-source alternative to Tableau, Looker, Power BI and Superset

Magnum BI is a lightweight, self-hosted business intelligence tool, now in early alpha: metrics and dashboards as code, a friendly drag-and-drop UI, and an API (and MCP endpoint) for everything. Here's how it compares with the tools you're probably looking at, and when one of them is the better fit.

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At a glance

A short, fair summary. Every tool here is good at something; pick the one that fits how your team works.

Tool	License	Where it runs	Where metrics are defined	Strongest at
Magnum BI (alpha)	Open source (AGPL-3.0)	Self-hosted: Docker image, Compose or Helm chart	YAML models in git	BI as code, multi-tenant and embedded dashboards
Tableau	Commercial	Desktop app, Tableau Cloud or Server	Workbooks and published data sources	Rich visual exploration
Power BI	Commercial	Windows desktop app and Microsoft cloud	DAX in semantic models	Microsoft-centric organisations
Looker	Commercial	Google Cloud	LookML in git	Governed metrics on Google Cloud
Apache Superset	Open source (Apache-2.0)	Self-hosted Python app, or managed	Datasets and SQL in the UI	Large chart library, SQL-savvy teams
Metabase	Open source (AGPL) plus paid editions	Self-hosted Java app, or Metabase Cloud	Models and metrics in the UI	Quick self-serve questions
ThoughtSpot	Commercial	Cloud	Models and worksheets	Search and AI-driven analytics

Summaries reflect publicly available information as of 2026; check each vendor's site for current details. Product names are trademarks of their owners and are used here only to describe compatibility and differences.

Why teams pick Magnum BI

Free and self-hosted

No per-seat licences. Run it on your own infrastructure; your data stays in your databases and is only read.

Metrics as code

Like LookML, but plain YAML: define “revenue” once, review it in a pull request, validate it in CI.

Click, code or API

The UI, YAML files and scripts all go through one API with the same rules, so nothing drifts apart.

Fast and light

One small, stateless Rust server in a Docker image, plus an opt-in cache on ScramDB that answers repeat questions instantly.

Built for many tenants

Each organisation has its own members, connections, dashboards and isolated database, plus row-level filters.

Embed and share

Put a live dashboard in your product with one HTML tag, share it publicly from a cache that protects your warehouse, or with guests by link and password for up to 14 days.

Agent-ready

Set up by a coding agent from one prompt, then driven over MCP with scoped, expiring API tokens.

Modern sign-in

Passkeys, authenticator codes and each organisation's own single sign-on, with policies per organisation.

Your brand colours

Organisation themes with contrast checks, exported to and imported from CSS design tokens.

Magnum BI vs Tableau

Tableau is a polished commercial tool for visual analysis, built around a desktop authoring app and Tableau Cloud or Server. Magnum BI is free and self-hosted, and keeps the definitions behind every chart in reviewed YAML instead of individual workbooks, so the same “revenue” shows up on every dashboard.

Choose Tableau if you need its very wide range of chart types and free-form visual exploration. Choose Magnum BI if you want governed metrics, version control and no licence fees.

Magnum BI vs Power BI

Power BI fits organisations that already live in Microsoft 365 and Azure, with reports authored in a Windows desktop app and shared through the Microsoft cloud. Magnum BI runs anywhere a single binary runs, works entirely in the browser, and defines measures in readable YAML rather than DAX.

Choose Power BI if your company is standardised on Microsoft. Choose Magnum BI if you want a self-hosted, cloud-neutral tool your engineers can manage as code.

Magnum BI vs Looker

Looker popularised the semantic layer in git with LookML. Magnum BI follows the same idea, models that say what your numbers mean, kept in version control, but with plain YAML files, an open-source license and self-hosting instead of a Google Cloud subscription.

Choose Looker if you're all-in on Google Cloud and need its mature ecosystem. Choose Magnum BI if you like the LookML approach but want it open source and on your own servers.

Magnum BI vs Apache Superset

Superset is a popular open-source BI platform with a large chart library and a SQL editor. A production setup typically adds a metadata database, a cache and background workers. Magnum BI is one stateless Rust server (one Docker image, or a Helm chart); models and dashboards are YAML in git, and each organisation's dashboards and cache live in their own ScramDB instance.

Choose Superset if you want the widest choice of charts and a built-in SQL IDE. Choose Magnum BI if you want metrics as code, multi-tenancy and a lighter footprint.

Magnum BI vs Metabase

Metabase is known for making it easy for anyone to ask questions of a database, with an open-source edition and paid tiers. Magnum BI puts more weight on governance: metrics are defined once in reviewed YAML, every change goes through one API, and every dashboard save is kept with its author.

Choose Metabase if ad-hoc questions from non-technical users are the priority. Choose Magnum BI if you want dashboards and metrics under version control, with the same rules for clicks, files and scripts.

Magnum BI vs ThoughtSpot

ThoughtSpot focuses on search and AI-driven analytics over cloud data warehouses, presented in liveboards. Magnum BI is an open-source, self-hosted tool built around drag-and-drop dashboards, drill-down and period comparison.

Choose ThoughtSpot if natural-language search is central to how people use data. Choose Magnum BI if you want an open, self-hosted alternative with dashboards as code.

Questions

Is Magnum BI really free?

Yes. It's open source under the GNU AGPL v3.0 and free to self-host, including for commercial work.

Which databases does it connect to?

PostgreSQL, ScramDB, BigQuery and DuckDB, including Parquet, CSV, JSON, Avro and Iceberg files on S3, GCS or HTTPS. Snowflake and MySQL are planned.

How is it different from Superset and Metabase?

It's a single stateless Rust server in one Docker image, models and dashboards are YAML in git, and each organisation gets its own isolated database for dashboards and cache.

Is it ready for production?

Not yet: Magnum BI is an early alpha. Its features work and are tested, but configuration, APIs and file formats will change, with no upgrade path guaranteed between versions. Try it, pilot it, and pin a version if you build on it.

Does it work with AI agents?

Yes. Coding agents can install it from one prompt, and its MCP endpoint lets Claude, Cursor and others run queries and build dashboards with scoped, expiring tokens.

Aloha! See for yourself

Comes with a sample sales dataset and a ready-made dashboard. You'll be cracking your first case in minutes.

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