At a glance
| Database connectors | Any SQLAlchemy dialect; 81 documented, including PostgreSQL, MySQL, SQL Server, Oracle, Snowflake, BigQuery, Redshift, ClickHouse, Trino; drivers installed separately |
| Dashboards | Dashboards built from charts; 40+ visualization types |
| Reporting | Alerts and reports by email, Slack or webhook (ALERT_REPORTS feature flag) |
| Embedding | Embedded SDK with guest tokens and row-level security rules |
| Semantic layer or modeling | Datasets (physical or SQL-defined virtual) with metrics and calculated columns |
| Deployment | Self-hosted: Docker Compose, Kubernetes (operator recommended) or PyPI; managed by Preset |
| Licensing | Apache License 2.0, no paid edition of the project |
Superset suits SQL-heavy analyst teams that want a fully open source BI tool with no feature gating and the engineering capacity to run it. Production deployment involves a metadata database, Redis, Celery workers and, by the docs' recommendation, Kubernetes; teams that do not want that work can rent it from Preset.
Overview
Apache Superset is a data exploration and dashboard application maintained by the Apache Software Foundation under the Apache License 2.0. Users build charts in a drag-and-drop builder (the project lists more than 40 visualization types), write SQL in SQL Lab with Jinja templating, and save queries as datasets with metrics and calculated columns. The latest stable release on PyPI is 6.1.0 (13 May 2026, Python 3.10 or later); 7.0.0 was at release candidate stage in October 2026.
Superset does not bundle database drivers: you install a Python DB-API driver and SQLAlchemy dialect for each engine, and the docs cover 81 databases. Docker Compose is the suggested way to try it; Kubernetes is described as the best-practice production deployment, with the Apache Superset Kubernetes Operator now recommended and the in-tree Helm chart deprecated. Preset offers Superset as a managed service.
Key features
- SQL Lab: web SQL IDE with metadata browsing and Jinja templating
- No-code chart builder with more than 40 built-in visualization types
- Datasets with metrics and calculated columns as a lightweight semantic layer
- Alerts and reports: scheduled dashboard or chart screenshots and data by email, Slack or webhook (needs Celery and a headless browser)
- Embedded SDK using guest tokens that can carry row-level security rules (EMBEDDED_SUPERSET feature flag)
- Connectivity to any database with a SQLAlchemy dialect and Python driver; 81 databases documented
- Data caching for charts and dashboards, and a REST API
Pros and cons
Pros
- Permissive Apache 2.0 licence with no features held back for a paid edition
- Strong SQL workflow through SQL Lab and SQL-defined datasets
- Embedding with row-level security is part of the open source project
- Wide database reach through SQLAlchemy dialects
Cons
- More infrastructure to operate than single-service tools: metadata database, Redis, Celery workers and beat scheduler
- Database drivers must be added to your image or environment
- Commercial support and hosting come from third parties such as Preset, not from the project
Pricing
Apache Superset is free under the Apache License 2.0, with no paid edition of the project; self-hosting costs are infrastructure and staff time. Preset, a separate company, sells managed Superset. Listed on Preset's pricing page (USD, excluding tax): Starter free for up to 5 users and 1 workspace; Professional USD 20 per user per month billed annually or USD 25 billed monthly, with a 14-day trial; Enterprise quoted; embedded dashboards an add-on from USD 500 per month for 50 viewer licences.
Pricing checked on the vendor's official page on 7 November 2026. Prices change; confirm before buying.
Who it is best for
Analyst teams that work in SQL and want a fully open source BI and dashboard tool they can host, modify and embed without licence fees.
Who should look elsewhere
- Small teams that want business users to self-serve with minimal operations: see Metabase vs Apache Superset.
- Organisations that want a single vendor for licences, support and desktop authoring: see Power BI vs Apache Superset.
Verdict
Apache Superset gives SQL-minded teams a capable, fully open source BI platform with SQL Lab, many chart types and embedding included. The cost is operational: plan for several services and Kubernetes in production, or budget for Preset if you would rather not run it.
Frequently asked questions
Is Apache Superset free?
Yes. It is licensed under the Apache License 2.0 and has no paid edition. You pay only for the infrastructure you run it on, or for a managed service such as Preset.
What is the difference between Superset and Preset?
Superset is the Apache Software Foundation project. Preset is a separate company that sells managed Superset, with a free Starter tier for up to 5 users and paid Professional and Enterprise plans.
Does Superset include database drivers?
No. The docs state that Superset does not ship with database connectivity; you install a Python driver and SQLAlchemy dialect for each database, for example through requirements-local.txt in Docker.
Sources
- Apache Superset documentation
- Apache Superset on GitHub
- Apache Superset on PyPI
- Superset: installation methods
- Superset: connecting to databases
- Preset pricing
Checked November 2026.
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