Apache Superset
RedashRedash vs Apache Superset: Which Open-Source BI Tool Wins in 2026?
Quick Verdict
Choose Apache Superset if...
Best for data teams at growing companies that need real BI — row-level security, a semantic layer, embedded analytics — and have the DevOps capacity to operate it well.
Choose Redash if...
Best for engineering-led teams under ~30 users who want a fast, simple SQL dashboarding tool and don't need advanced governance, embedded analytics, or non-SQL chart building.
If you are building internal BI for a technical team and refuse to pay Tableau or Looker prices, the shortlist almost always comes down to two open-source projects: Redash and Apache Superset. Both are free, both are SQL-first, both run anywhere you can stand up Docker — and yet they target very different teams.
Most head-to-head posts treat this as a feature checkbox race, where Superset wins by sheer volume. That framing misses the point. Redash is deliberately small. It is a thin, friendly layer over your warehouse that lets engineers ship a useful dashboard in an afternoon. Superset is a full Apache project trying to become a credible Tableau replacement, with a semantic layer, row-level security, embedded analytics, and 40+ chart types. The right answer depends on whether you want a lightweight SQL notebook with dashboards bolted on, or a real BI platform you are willing to operate.
This comparison focuses on what actually matters for technical teams building internal BI without commercial costs: the SQL editing experience, chart types and dashboard interactivity, access control and multi-tenant safety, and operational cost (Docker is easy; running Superset for 200 users at scale is not). Both tools live in the broader business intelligence category, and if you also want a no-code option in the mix, our best open-source BI tools guide covers Metabase, Lightdash, and others.
We will start with a feature-by-feature breakdown, walk through the pricing reality (spoiler: both are free, but only one has a managed cloud), then close with a clear "choose X if..." decision guide so you can stop reading and start deploying.
Feature Comparison
| Feature | Apache Superset | Redash |
|---|---|---|
| 40+ Visualization Types | ||
| No-Code Chart Builder | ||
| SQL Lab IDE | ||
| Interactive Dashboards | ||
| 40+ Database Connectors | ||
| Embedded Analytics | ||
| Row-Level Security | ||
| Semantic Layer | ||
| Jinja Templating | ||
| Alerts & Reports | ||
| SQL Query Editor | ||
| 35+ Data Source Connectors | ||
| Drag-and-Drop Visualizations | ||
| Scheduled Queries | ||
| Query-Based Alerts | ||
| Parameterized Queries | ||
| Collaboration & Sharing | ||
| API Access |
Pricing Comparison
| Pricing | Apache Superset | Redash |
|---|---|---|
| Free Plan | ||
| Starting Price | Free | Free |
| Total Plans | 1 | 1 |
Apache Superset- Full platform — all features included
- Self-hosted deployment
- Unlimited users
- 40+ visualization types
- 40+ database connectors
- SQL Lab IDE
- Community support
Redash- Full platform — all features included
- Self-hosted deployment
- Unlimited users
- 35+ data source connectors
- SQL query editor
- Dashboards and visualizations
- Community support
Detailed Review
Apache Superset
Modern open-source data exploration and visualization platform at petabyte scale
Apache Superset is the more ambitious of the two — a full BI platform that started at Airbnb in 2015 and is now an Apache Software Foundation top-level project with 65k+ GitHub stars. For technical teams building internal BI, it is the closest thing to a free Tableau or Looker, complete with a semantic layer, row-level security, embedded analytics, and a no-code chart builder that lets non-SQL users self-serve.
Where Superset shines for this audience is depth. The SQL Lab IDE is a real query environment with autocomplete, history, and Jinja templating for parameterized dashboards. The 40+ visualization library covers nearly every chart type a business team will ask for, including geospatial maps, sankey diagrams, and pivot tables, with a plugin system for custom charts. Connect it to Snowflake, BigQuery, or Databricks and it will happily query petabyte-scale data without breaking a sweat — the heavy lifting stays in your warehouse.
The trade-off is operational weight. Superset is not something you stand up in an afternoon and forget. Production deployments need Celery workers for async queries, Redis for caching, a metadata database, and ongoing tuning as concurrent user counts grow. If your team has the DevOps muscle for that — or you are willing to pay for Preset as a managed option — Superset gives you a BI platform you will not outgrow. If you don't, it can become a maintenance burden that distracts from the analytics work it was supposed to enable.
Pros
- Row-level security and RBAC make it safe for multi-team or multi-tenant dashboards out of the box
- Semantic layer lets data teams define metrics once and reuse them consistently across dashboards
- 40+ visualization types plus a plugin system — virtually any chart your team needs is built in
- No-code chart builder lets non-SQL users self-serve from datasets technical users have prepared
- Embedded analytics with custom styling makes it viable for customer-facing dashboards in SaaS products
Cons
- Production deployment requires real DevOps effort: Celery, Redis, async config, and ongoing tuning
- Steeper learning curve — both for end users navigating the UI and for admins configuring it
- No first-party managed cloud (Preset is third-party) means most teams must self-host
Redash
Open-source SQL-first dashboards and data visualization for technical teams
Redash takes the opposite philosophy. Instead of trying to replace Tableau, it asks: what if a SQL editor and a dashboarding tool were the same thing, and nothing more? The result is a deliberately lightweight platform — 27k+ GitHub stars, founded in 2013, acquired by Databricks in 2020 — that engineering-led teams can deploy in an hour and use forever.
For technical teams, the appeal is the friction-free workflow. You write a SQL query against one of 35+ supported sources (Postgres, MySQL, BigQuery, Redshift, Mongo, even Google Sheets and HTTP APIs), click to attach a visualization, drop it on a dashboard, and share. Parameterized queries make those dashboards self-serve for non-SQL users without exposing them to SQL itself: a date picker or dropdown is all most stakeholders need. Scheduled queries with email delivery and query-based alerts cover most internal reporting needs without additional infrastructure.
The limits are real and worth knowing up front. There is no no-code chart builder — every visualization starts as SQL. The visualization library is much smaller than Superset's. There is no row-level security, so multi-tenant dashboards require careful query design or duplication. And development pace has slowed noticeably since the Databricks acquisition, with most modern features (semantic layers, AI assist, advanced governance) absent. None of that matters if you are an engineering team building internal dashboards for ops, growth, or product metrics — Redash is essentially feature-complete for that use case and gets out of your way.
Pros
- Deploys in an hour with a single Docker Compose file — minimal ongoing maintenance for small teams
- SQL-first workflow feels natural for engineers and analysts who already think in queries
- Parameterized queries give non-technical users self-serve dashboards without exposing them to SQL
- Lightweight resource footprint — runs comfortably on a small VM where Superset would struggle
- 35+ data source connectors including HTTP APIs and Google Sheets, useful for quick ad-hoc dashboards
Cons
- No row-level security means multi-tenant or sensitive data scenarios need workarounds
- Smaller visualization library — no geospatial, sankey, or advanced statistical chart types
- Development has slowed significantly since the 2020 Databricks acquisition; new features are rare
Our Conclusion
Choose Redash if your team is small (under ~30 active users), everyone who builds dashboards already knows SQL, and you want something you can deploy in an hour and basically forget about. Redash is the right answer for engineering-led startups, internal tools teams, and ops dashboards where the goal is "show me the number" not "build a self-service analytics platform." The development pace is slow post-Databricks acquisition, but for the use case it serves, the tool is essentially feature-complete.
Choose Apache Superset if you need real BI: row-level security so customers or business units only see their own data, a semantic layer so metrics stay consistent across dashboards, embedded analytics for a customer-facing product, or just a much wider visualization library. Superset is the right answer for data teams at growing companies, SaaS products embedding analytics, and anyone whose dashboards will eventually need to scale past a single team. Budget for at least one engineer who treats Superset as part of their job — it rewards investment.
The honest take: most teams overestimate how much BI sophistication they need. If you are reading this and you are not sure, start with Redash. You will know within three months whether you have hit its ceiling, and migrating queries to Superset later is straightforward because both speak SQL. Going the other way — paring Superset down because nobody uses 90% of it — is much harder.
For adjacent tooling, see our guides to the best data visualization tools and the top SQL editors for analysts. And if your real bottleneck is the warehouse, not the dashboard, that is a different problem worth solving first.
Frequently Asked Questions
Is Redash or Apache Superset easier to self-host?
Redash is significantly easier. The official Docker Compose setup gets you a working instance in under an hour, and ongoing maintenance is minimal. Superset technically also has Docker Compose, but production deployments need Celery workers, a Redis cache, a metadata DB, and tuned async query handling — expect real DevOps time.
Can both connect to Snowflake, BigQuery, and Redshift?
Yes. Both ship with first-class connectors for the major cloud warehouses (Snowflake, BigQuery, Redshift, Databricks) plus the usual SQL/NoSQL suspects. Superset supports 40+ databases out of the box; Redash supports 35+. For mainstream warehouse stacks, neither tool will be the bottleneck.
Which has better access control for multi-team or multi-tenant use?
Apache Superset, by a wide margin. It supports row-level security, role-based access control, and SSO integration out of the box — critical if different users should see different slices of the same dashboard. Redash has user/group permissions on queries and dashboards but no row-level filtering, so multi-tenant scenarios usually require workarounds.
Is there a managed cloud version of either tool?
Apache Superset has Preset (preset.io), a paid managed service from the project's original maintainers. Redash had a hosted version that was sunset after the Databricks acquisition, so today self-hosting is effectively the only option.
Can business users build charts without writing SQL?
On Superset, yes — the no-code chart builder lets non-technical users drag dimensions and measures onto a chart from a pre-defined dataset. On Redash, no. Every visualization starts from a SQL query, which is fine for technical teams but a hard blocker if you want analysts and ops folks to self-serve.
Which project is more actively developed?
Apache Superset, clearly. It is an Apache top-level project with hundreds of contributors and frequent releases. Redash development slowed considerably after the 2020 Databricks acquisition; the community keeps it alive and patched, but new feature velocity is low.