Metabase vs Apache Superset vs Redash

Overview of the Three Platforms

These three tools emerged at roughly the same time in the mid-2010s, each targeting a different segment of the open source analytics market. Metabase launched in 2015 with the explicit goal of making data accessible to everyone in an organization, not just people who can write SQL. Apache Superset started as an internal tool at Airbnb in 2015, built to serve the analytical needs of a data-intensive engineering organization. Redash launched in 2013 as a lightweight SQL query tool that made it simple to write queries, visualize results, and share them as dashboards.

By 2026, their trajectories have diverged significantly. Metabase has grown into the most widely adopted open source BI tool, with over 60,000 organizations using it and a commercial company (Metabase, Inc.) funding its development through Pro and Enterprise editions. Superset has become a top-level Apache Software Foundation project with major production deployments at companies including Dropbox, Lyft, and Netflix, plus a commercial managed service through Preset. Redash was acquired by Databricks in 2020, and while the open source codebase remains available, active development has slowed considerably, with the latest release being version 26.3.0 from March 2024.

Ease of Use and User Experience

Metabase wins this category decisively. Its visual query builder allows users to select tables, choose columns, apply filters, add groupings, and pick chart types through a point-and-click interface. A marketing manager can open Metabase, click through to their campaign data, filter by date range and channel, and produce a bar chart showing conversion rates by source, all without typing a single SQL keyword. The interface is clean, focused, and designed around the assumption that most users are not data professionals.

Superset's interface is more powerful but more complex. The chart builder provides dozens of configuration options for each visualization type, which gives analysts fine control but can overwhelm casual users. SQL Lab, Superset's interactive query editor, is excellent for analysts who think in SQL but offers nothing for users who do not. The dashboard builder is capable, with cross-filtering and drill-down interactions, but the learning curve is steeper than Metabase's equivalent.

Redash takes a minimalist approach centered on SQL. You write a query, pick a visualization type, and add it to a dashboard. The simplicity is appealing for SQL-proficient users who want to get from query to chart as quickly as possible, but there is no visual query builder at all. Non-SQL users have no path to creating their own queries in Redash.

Data Source Support

Superset supports the widest range of data sources, with over 80 connectors available through its SQLAlchemy-based architecture. Any database that has a SQLAlchemy dialect can be connected to Superset, which includes virtually every major SQL database, several NoSQL databases, and cloud data warehouses. Adding a new data source often requires nothing more than installing a Python package.

Metabase supports over 20 databases through native drivers. The list covers the most popular options: PostgreSQL, MySQL, MariaDB, SQL Server, Oracle, MongoDB, BigQuery, Snowflake, Redshift, ClickHouse, Databricks, DuckDB, and several others. Native drivers provide tighter integration than generic connectors, with better type handling, more accurate metadata extraction, and database-specific query optimizations. Community-contributed drivers extend support to additional databases.

Redash supports approximately 35 data sources, including SQL databases, NoSQL stores, APIs, Google Sheets, and specialized sources like Jira and Salesforce. The query runner architecture makes it relatively straightforward to add new data sources, though the slower pace of development means newer databases may not have up-to-date connectors.

Visualization and Dashboarding

Superset offers the richest visualization capabilities, with over 40 built-in chart types including advanced options like sunburst charts, chord diagrams, geographic heatmaps, parallel coordinates, and time-series forecasting visualizations. The dashboard builder supports cross-filtering, dynamic filter boxes, and layout customization through a drag-and-drop grid. Superset also supports custom visualization plugins, allowing organizations to build and deploy proprietary chart types that integrate seamlessly with the platform.

Metabase provides a more curated set of approximately 15-20 chart types that cover the most common analytical needs: line charts, bar charts, scatter plots, pie charts, maps, funnels, pivot tables, and gauges. The emphasis is on making each chart type work well with sensible defaults rather than offering maximum configuration options. Dashboards support click-through filtering and parameter-based interactivity, and the recently added drill-through functionality lets users click on a data point to see the underlying rows.

Redash offers basic chart types including line, bar, scatter, pie, map, cohort, pivot, and word cloud. The visualization options are functional but less polished than either Metabase or Superset. Dashboard interactions are limited compared to the other two tools.

Security and Access Control

Superset provides the most comprehensive security model. Role-based access control covers databases, schemas, datasets, dashboards, and individual charts. Row-level security policies can restrict data visibility based on user attributes, enabling multi-tenant deployments where different teams or customers see only their authorized data. Superset supports LDAP, OAuth, OpenID Connect, and SAML for authentication, and its security model is built on Flask-AppBuilder, which provides a well-tested foundation for enterprise access control.

Metabase offers solid access control in its Community Edition, with permissions manageable at the database, schema, and table level. The paid Enterprise Edition adds row-level and column-level permissions, SAML authentication, audit logging, and data sandboxing that restricts what data specific groups can query. For most small to mid-sized deployments, the Community Edition's permissions are sufficient.

Redash provides basic group-based permissions for data sources and queries. The security model is simpler than both Metabase and Superset, which can be an advantage for small teams that do not need granular controls but a limitation for organizations with strict data governance requirements.

Deployment and Maintenance

Metabase has the simplest deployment path. A single Docker command starts a fully functional instance, and the embedded H2 database handles metadata storage for evaluation and small production deployments. Upgrading to a production-grade setup with PostgreSQL as the metadata database adds minimal complexity. The total time from zero to a working deployment with connected data sources is typically under an hour.

Superset requires a more involved deployment. A production setup includes the Superset web application, a PostgreSQL metadata database, Redis for caching and result storage, and Celery workers for async query execution. Docker Compose configurations are available to orchestrate these components, but the initial setup and ongoing maintenance require more DevOps expertise than Metabase. The payoff is a platform that handles higher concurrency and more complex workloads.

Redash falls between the two in deployment complexity. It requires a PostgreSQL database and Redis, with Docker Compose providing the standard deployment method. The lighter resource requirements make it easy to run on modest hardware, but the reduced development activity means that security patches and compatibility updates arrive less frequently than with the other two tools.

Community and Development Activity

Metabase and Superset both have thriving development communities with regular releases, active issue trackers, and responsive maintainer teams. Metabase's development is funded by the commercial company behind it, ensuring consistent resources for bug fixes, security patches, and feature development. Superset benefits from Apache Software Foundation governance and contributions from multiple organizations, plus commercial backing from Preset.

Redash's open source community has contracted since the Databricks acquisition. While the project remains available and functional, the pace of new features and bug fixes has slowed to the point where organizations evaluating open source BI should consider Redash only if they already run it and the migration cost to another tool is prohibitive.

Which Should You Choose?

Choose Metabase if your primary users are business people who need to explore data without SQL, if you want the fastest path from installation to productive use, or if your organization does not have dedicated DevOps resources for complex deployments. Metabase is the right default choice for most organizations evaluating open source BI for the first time.

Choose Apache Superset if your data team needs SQL-first exploration, advanced visualization types, or enterprise-grade access control with row-level security. Superset is the stronger platform for data-mature organizations with engineering resources to handle deployment and maintenance.

Avoid starting new deployments on Redash. The reduced development activity creates risk around security, compatibility with newer databases, and long-term viability of the open source edition. Organizations currently using Redash should evaluate a migration plan to Metabase or Superset over the next year.