Apache Superset vs Redash
Apache Superset and Redash both serve the open-source BI space, but they target different team profiles. Superset is a full-featured BI platform with a semantic layer, 40+ chart types, and enterprise-grade access controls — we recommend it for organizations that need scalable, production-grade analytics. Redash is a focused SQL query and visualization tool that prioritizes simplicity and speed of setup — we recommend it for data teams that want to get dashboards running quickly without managing a heavyweight platform.
Apache Superset4.3Redash4.3
Business Intelligence
Quick Comparison
| Feature | Apache Superset | Redash |
|---|---|---|
| Best For | Enterprise teams needing advanced BI with a semantic layer and 40+ chart types | Data teams wanting a lightweight, SQL-first query and dashboard tool |
| Pricing | Free and open-source under Apache License 2.0 | Self-hosted free (BSD-2-Clause license) |
| Learning Curve | Moderate — no-code builder available, but full power requires SQL and configuration knowledge | Low — straightforward SQL editor with drag-and-drop dashboards |
| Data Source Support | Any SQL-based database including cloud-native engines at petabyte scale | SQL, NoSQL, Big Data, and API data sources with broad integration support |
| Visualization Options | 40+ pre-installed chart types with plug-in architecture for custom visualizations | Charts, cohorts, pivot tables, boxplots, maps, counters, sankey, sunburst, word cloud, funnel |
| Community & Development | 72,400+ GitHub stars, active Apache project, latest release 6.0.0 (Dec 2025) | 28,500+ GitHub stars, owned by Databricks since 2020, latest release v26.3.0 (Mar 2026) |
Apache Superset
- Best For:
- Enterprise teams needing advanced BI with a semantic layer and 40+ chart types
- Pricing:
- Free and open-source under Apache License 2.0
- Learning Curve:
- Moderate — no-code builder available, but full power requires SQL and configuration knowledge
- Data Source Support:
- Any SQL-based database including cloud-native engines at petabyte scale
- Visualization Options:
- 40+ pre-installed chart types with plug-in architecture for custom visualizations
- Community & Development:
- 72,400+ GitHub stars, active Apache project, latest release 6.0.0 (Dec 2025)
Redash
- Best For:
- Data teams wanting a lightweight, SQL-first query and dashboard tool
- Pricing:
- Self-hosted free (BSD-2-Clause license)
- Learning Curve:
- Low — straightforward SQL editor with drag-and-drop dashboards
- Data Source Support:
- SQL, NoSQL, Big Data, and API data sources with broad integration support
- Visualization Options:
- Charts, cohorts, pivot tables, boxplots, maps, counters, sankey, sunburst, word cloud, funnel
- Community & Development:
- 28,500+ GitHub stars, owned by Databricks since 2020, latest release v26.3.0 (Mar 2026)
Community & Adoption Signals
| Metric | Apache Superset | Redash |
|---|---|---|
| GitHub stars | 73.1k | 28.6k |
| TrustRadius rating | — | 8.1/10 (17 reviews) |
| PyPI weekly downloads | 626.0k | — |
| Docker Hub pulls | 598.3M | 91.7M |
| Search interest | 1 | 0 |
| Product Hunt votes | 75 | 9 |
As of 2026-06-01 — updated weekly.
Interface Preview
Apache Superset
Redash
Feature Comparison
| Feature | Apache Superset | Redash |
|---|---|---|
| Query & Data Exploration | ||
| SQL Editor | SQL Lab IDE with auto-complete, syntax highlighting, and query history | Browser-based SQL editor with schema browsing, snippets, and auto-complete |
| No-Code Exploration | Visual chart builder with drag-and-drop metric/dimension selection | Limited — primarily SQL-driven with drag-and-drop for dashboard layout only |
| Query Caching | Built-in caching layer for query results and dashboard performance | Results cached with configurable auto-refresh schedules |
| Visualization & Dashboards | ||
| Chart Types | 40+ pre-installed types including geospatial charts | 12+ types including charts, cohorts, pivot tables, maps, sankey, and funnel |
| Custom Visualizations | Plug-in architecture for building and installing custom viz types | Community-contributed visualization extensions |
| Dashboard Sharing | Dashboard embedding with role-based access controls | One-click sharing via secret URL or public embedding |
| Data Architecture | ||
| Semantic Layer | Built-in semantic layer with reusable metrics and dimensions | No semantic layer — relies on raw SQL queries |
| Database Support | Any SQL-based database including Druid, Presto, Trino, and cloud-native engines | SQL and NoSQL databases plus API data sources including BigQuery, Redshift, and MongoDB |
| Architecture | Modern TypeScript/Python stack with React frontend and Flask backend | Python backend with JavaScript frontend, lightweight and self-contained |
| Security & Administration | ||
| Access Control | Role-based access control with row-level security | User management with SSO and access control features |
| Authentication | OAuth, OpenID, LDAP, and custom authentication providers | SSO integration with enterprise authentication providers |
| API Access | Full REST API for programmatic access and integrations | REST API for query creation, management, and data retrieval |
| Operations & Workflow | ||
| Alerts & Scheduling | Dashboard refresh scheduling with configurable intervals | Query-based alerts with threshold triggers and scheduled refresh |
| Collaboration | Shared dashboards with annotation layers and comment support | Query sharing with team visibility and collaborative dashboard editing |
| Deployment | Docker, Kubernetes, or bare-metal; managed options from Preset (commercial fork) | Docker-based self-hosted deployment; previously offered hosted version |
Query & Data Exploration
SQL Editor
Apache SupersetSQL Lab IDE with auto-complete, syntax highlighting, and query history
RedashBrowser-based SQL editor with schema browsing, snippets, and auto-complete
No-Code Exploration
Apache SupersetVisual chart builder with drag-and-drop metric/dimension selection
RedashLimited — primarily SQL-driven with drag-and-drop for dashboard layout only
Query Caching
Apache SupersetBuilt-in caching layer for query results and dashboard performance
RedashResults cached with configurable auto-refresh schedules
Visualization & Dashboards
Chart Types
Apache Superset40+ pre-installed types including geospatial charts
Redash12+ types including charts, cohorts, pivot tables, maps, sankey, and funnel
Custom Visualizations
Apache SupersetPlug-in architecture for building and installing custom viz types
RedashCommunity-contributed visualization extensions
Dashboard Sharing
Apache SupersetDashboard embedding with role-based access controls
RedashOne-click sharing via secret URL or public embedding
Data Architecture
Semantic Layer
Apache SupersetBuilt-in semantic layer with reusable metrics and dimensions
RedashNo semantic layer — relies on raw SQL queries
Database Support
Apache SupersetAny SQL-based database including Druid, Presto, Trino, and cloud-native engines
RedashSQL and NoSQL databases plus API data sources including BigQuery, Redshift, and MongoDB
Architecture
Apache SupersetModern TypeScript/Python stack with React frontend and Flask backend
RedashPython backend with JavaScript frontend, lightweight and self-contained
Security & Administration
Access Control
Apache SupersetRole-based access control with row-level security
RedashUser management with SSO and access control features
Authentication
Apache SupersetOAuth, OpenID, LDAP, and custom authentication providers
RedashSSO integration with enterprise authentication providers
API Access
Apache SupersetFull REST API for programmatic access and integrations
RedashREST API for query creation, management, and data retrieval
Operations & Workflow
Alerts & Scheduling
Apache SupersetDashboard refresh scheduling with configurable intervals
RedashQuery-based alerts with threshold triggers and scheduled refresh
Collaboration
Apache SupersetShared dashboards with annotation layers and comment support
RedashQuery sharing with team visibility and collaborative dashboard editing
Deployment
Apache SupersetDocker, Kubernetes, or bare-metal; managed options from Preset (commercial fork)
RedashDocker-based self-hosted deployment; previously offered hosted version
Our Verdict
Apache Superset and Redash both serve the open-source BI space, but they target different team profiles. Superset is a full-featured BI platform with a semantic layer, 40+ chart types, and enterprise-grade access controls — we recommend it for organizations that need scalable, production-grade analytics. Redash is a focused SQL query and visualization tool that prioritizes simplicity and speed of setup — we recommend it for data teams that want to get dashboards running quickly without managing a heavyweight platform.
When to Choose Each
Choose Apache Superset if:
Choose Apache Superset if your organization needs a comprehensive BI platform with advanced visualization capabilities. Superset excels when you have a dedicated data team that can leverage the semantic layer to create reusable metrics, when you need 40+ chart types including geospatial visualizations, and when enterprise security with row-level access control is a requirement. The larger community (72,400+ GitHub stars) and active Apache Foundation governance ensure long-term project stability. Superset is also the stronger choice if you plan to embed dashboards into customer-facing products or need to connect to modern cloud-native data engines at petabyte scale.
Choose Redash if:
Choose Redash if your team values simplicity and fast time-to-insight over feature breadth. Redash stands out for its clean SQL-first workflow where analysts can write queries, visualize results, and build dashboards within minutes of deployment. The tool supports both SQL and NoSQL data sources plus API endpoints, giving it broader protocol-level connectivity despite fewer visualization types. Redash is the better fit for teams that work primarily in SQL, want lightweight alerting on query results, and prefer a tool that stays out of the way. With Databricks ownership since 2020 and consistent releases through v26.3.0, Redash maintains active development while keeping its focused scope.
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Frequently Asked Questions
Is Apache Superset truly free for enterprise use?
Yes. Apache Superset is released under the Apache License 2.0, which permits commercial and enterprise use without licensing fees. You are responsible for hosting infrastructure, configuration, and maintenance. Preset offers a managed commercial version for teams that want Superset without the operational overhead.
Can Redash connect to the same databases as Apache Superset?
Both tools support a wide range of data sources, but their approaches differ. Superset focuses on SQL-based databases and modern cloud-native engines like Druid, Presto, and Trino. Redash supports SQL databases alongside NoSQL stores like MongoDB and API-based data sources, giving it broader protocol-level connectivity even if its visualization layer is simpler.
Which tool is easier to set up for a small data team?
Redash is generally faster to deploy and learn. Its Docker-based setup gets a working instance running in minutes, and the SQL-first interface means analysts can start querying immediately. Superset has more components to configure (caching, metadata database, authentication) and a steeper initial setup curve, though Docker Compose makes it manageable.
What happened to Redash after the Databricks acquisition?
Databricks acquired Redash in June 2020. The open-source project continues to receive updates, with the latest release being v26.3.0 in March 2026. The previously offered hosted Redash service was retired, so teams now self-host or use the Databricks SQL Analytics product that incorporates Redash technology.