Apache Superset vs Lightdash
Both are alternatives to Tableau. Here's how they stack up — verified facts, no spin.
Also searched as Lightdash vs Apache Superset — same comparison, one verdict.
Apache Superset
Enterprise BI under Apache Software Foundation governance.
Superset is the most capable open BI platform: dozens of visualisation types, a SQL Lab for analysts, caching, row-level security and dashboards that hold up at enterprise scale. Apache-2.0 under Apache Software Foundation governance, around 74k stars, originally built at Airbnb and now used very widely. The distinction that matters against Metabase is governance — no company can relicense it — and against Tableau it is cost. The trade is operational: it is a Python application with a database and a cache, and it expects competence.
Lightdash
BI that reads your dbt models, so metrics are defined once.
Lightdash builds its entire semantic layer from your existing dbt project: define a metric once in dbt, and it appears in BI with the same definition everywhere. That solves the specific and expensive problem of the same number meaning three different things in three dashboards, which is where most BI trust breaks down. Around 6k stars — the smallest here — and it only makes sense if you already run dbt. Where you do, it is the tightest fit on this page.
Side by side
| Apache Superset | Lightdash | |
|---|---|---|
| Sovereignty Score | 93 | 87 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | Apache-2.0 | MIT (open core; paid cloud tier) |
| Pricing | Free and open source. Managed offerings available from Preset and others. | Open-source core free and self-hostable. Lightdash Cloud is paid. |
Apache Superset edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Apache Superset
Strengths
- +Apache Software Foundation governance — no vendor can change the terms
- +The widest range of visualisation types of anything open
- +Row-level security and caching built in
- +Proven at very large scale
Trade-offs
- −Heaviest to deploy and operate of the open options
- −Steeper learning curve for non-technical users than Metabase
- −Chart building is less forgiving than Tableau's
- −Upgrades need care
Lightdash
Strengths
- +Metrics defined once in dbt and used everywhere — no drifting definitions
- +Version-controlled BI, reviewed in pull requests like code
- +MIT core
- +Fast to adopt if dbt is already in place
Trade-offs
- −Effectively requires dbt — useless without it
- −Smallest project and community on this page
- −Fewer visualisation types than Superset
- −Less suited to ad-hoc exploration by non-technical staff
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Facts verified 2026-08-04. Licenses and pricing change — spotted something out of date? That's a correction we want.