Apache Superset

Open-source BI platform you can self-host with SQL-first dashboards

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What is Apache Superset?

Apache Superset is an open-source business intelligence platform that began at Airbnb and now runs under the Apache Software Foundation. It offers a no-code chart builder, a full SQL Lab editor with result export, a semantic layer for reusable metrics, and dashboards that can be shared, embedded or scheduled as email reports. It connects to most SQL-speaking databases through SQLAlchemy. Deploying it yourself or through a managed provider keeps costs and data residency under your control. It is conventional BI rather than an AI product - there are no language-model features in the core project.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

  • No-code chart builder plus a full SQL Lab editor with saved queries
  • Connects to most SQL databases through SQLAlchemy drivers
  • Semantic layer for reusable datasets, metrics and calculated columns
  • Dashboard sharing, embedding, alerts and scheduled email reports
  • Role-based access control with row-level security rules
  • Self-hosted via Docker or Kubernetes, or run through managed providers

Pros & cons

Strengths

  • Free and open source, so cost scales with infrastructure only
  • SQL-first workflow suits analysts who prefer writing queries
  • Data never leaves infrastructure you control

Watch out for

  • No language-model or natural-language query features in core
  • You own upgrades, backups and security patching yourself
  • Dashboard polish trails commercial tools without custom CSS work

Best for & use cases

self-hosted bi, sql analysis, internal dashboards and data residency requirements

If you're comparing similar products, check the alternatives below, or browse all tools in the AI Data Analysis category.

FAQ

What does self-hosting Superset actually involve?

Production setups usually run the web server, a Celery worker, a metadata database and a cache such as Redis behind a reverse proxy. Docker Compose is fine for a small team; larger installations typically use Kubernetes with an external metadata database.

Is Superset an AI tool?

No. The project focuses on conventional charting, SQL exploration and dashboard delivery. Any prompt-to-chart experience would come from a separate layer or a managed fork rather than from Superset itself.

How does it compare with Metabase?

Both are open-source BI tools with free editions. Superset leans towards SQL-first analysts and larger chart libraries; Metabase leans towards non-technical users and faster setup. Try both on the same dataset before committing.