Metabase vs Redash: Which SQL Dashboard for Your Team?

Redash used to be the default answer when someone on a data team said "I just want to write SQL and get a chart." It was fast to set up, opinionated about SQL-first workflows, and free to self-host. For years, it held a loyal following among engineers and data analysts who did not want a visual query builder telling them how to think.

Then Databricks acquired Redash in 2020. The hosted version (Redash.io) shut down. The open-source repository went quiet. And a large, vocal community of Redash users started asking: "What do I migrate to?"

Most of them looked at Metabase first. This post is for them -- and for anyone else evaluating both tools right now.

Two different philosophies for the same problem

Both Redash and Metabase solve the same core problem: connect to a database, run queries, build charts, arrange them on dashboards. But they have fundamentally different opinions about who should be driving.

Redash assumes you write SQL. The entire interface is organized around queries. You open Redash, you write a query, you pick a visualization type, you save it. Dashboards are collections of saved query visualizations. There is no visual query builder. There is no drag-and-drop. If you do not write SQL, Redash has nothing for you.

Metabase assumes you might not. Its headline feature is a visual query builder -- sometimes called the "notebook editor" -- that lets you pick a table, add filters, choose groupings, and select aggregations without writing a single line of SQL. For people who can write SQL, there is also a native SQL editor. But the default path is designed for someone who cannot.

This is not a small difference. It shapes every design decision in both tools.

In Redash, parameterized queries are a core feature. You write SELECT * FROM orders WHERE status = {{status}} and Redash renders a dropdown on the dashboard. Users pick a value, the query re-runs. This is incredibly powerful for data teams who build internal tools on top of SQL -- and it is still one of the best parameterized query experiences in any BI tool.

In Metabase, the equivalent is a dashboard filter that you wire up to cards through the GUI. More approachable, but less flexible. You cannot express arbitrary SQL logic through the filter system the way you can with Redash's query parameters.

The elephant in the room: Redash's maintenance status

I need to address this directly because it is the single biggest factor in this comparison.

Databricks acquired Redash in June 2020. By early 2022, the hosted Redash.io service was shut down and all hosted users were migrated to Databricks SQL. The open-source repository at getredash/redash on GitHub still exists, but the pace of development slowed dramatically. As of mid-2026:

• The last major release (v10) shipped in late 2021.
• Community PRs sit open for months. Some sit open for years.
• Critical security patches get merged, but new features do not.
• Docker images are still published, but the surrounding documentation is increasingly stale.
• There is no roadmap, no public commitment to future development.

The open-source community has kept Redash alive through forks and patches. Several groups maintain their own builds. But there is a real difference between "a project with active maintainers shipping releases" and "a project that is technically not archived but is not really moving either."

If you are starting from scratch today and considering Redash, you need to factor in the risk that you are adopting a tool with no clear development future. If you already run Redash and it works, the question is different -- it is about how long you can ride the existing version before something forces your hand (a database driver update, a security vulnerability, a dependency that stops building).

Metabase, by contrast, ships monthly releases, maintains an active GitHub, and has a funded company behind it. You can disagree with their pricing or product direction, but the tool is actively maintained. That matters.

Query editing: Redash still wins for pure SQL workflows

If your entire team writes SQL and you just need a place to run queries, save them, and share charts, Redash's query editor is still hard to beat.

Here is why:

• Auto-complete that knows your schema. Type a table name, get column suggestions. It is fast and accurate.
• Query snippets. Save reusable SQL fragments and insert them across queries. For teams with common CTEs or filter patterns, this saves real time.
• Parameterized queries with typed widgets. Date pickers, dropdowns populated from other queries, multi-select -- all wired up through {{parameter}} syntax in the SQL. The simplicity of this approach is genuinely elegant.
• Query results as data sources. You can write a query that reads from the results of another query. This lets you layer transformations without complex CTEs or temp tables.
• Fork and edit. See a colleague's query, fork it, modify it. Simple version control that fits how data teams actually work.

Metabase has a SQL editor too, and it is perfectly competent. It has schema browsing, basic auto-complete, and the ability to save and share queries. But it does not have query snippets (in the open-source edition), does not support query-results-as-data-sources, and its parameterization system -- while functional -- requires more clicking through the GUI to set up.

For a team of five analysts who all think in SQL, Redash's editor is faster and more natural. The gap has narrowed as Metabase has improved its SQL mode, but Redash's SQL-first design still shows through. If SQL-first is the primary criterion, our best SQL dashboard tools roundup covers eight options ranked by query editing, parameterization, and scheduling. And if your team's editor workflow extends beyond the BI tool, our best SQL editors for data teams guide covers the full landscape.

Visualization: Metabase pulls ahead

This is where Metabase's broader ambition pays off.

Metabase ships with more visualization types out of the box: line, bar, area, scatter, pie, funnel, gauge, progress bar, map (US states, countries, pin maps), pivot tables, trend lines, and goal lines. The chart formatting options are more granular -- you can control axis labels, color palettes, number formatting, and conditional formatting with reasonable precision.

Redash's visualization library is smaller and simpler. You get line, bar, scatter, pie, map, pivot table, cohort, word cloud, and a few others. The formatting options are more limited. Charts look functional but not polished. You will not embarrass yourself putting a Redash chart in a board deck, but a Metabase chart will look noticeably better.

The gap is particularly visible with:

• Maps. Metabase's map visualizations support region shading, pin clustering, and custom lat/lng columns. Redash has basic map support but it is less refined.
• Pivot tables. Both have them. Metabase's handles larger result sets more gracefully and has better formatting options.
• Number/detail cards. Metabase's single-number cards with trend indicators look clean. Redash's "counter" visualization works but feels more utilitarian.

If your dashboards are mostly for internal consumption by technical people, Redash's visualizations are fine. If your dashboards are going to executives or external stakeholders, Metabase looks better.

Database support

Both tools connect to the databases most data teams actually use, but the lists differ:

Redash actually supports more data source types out of the box -- around 40+, including some unusual ones like Google Sheets, JSON APIs, CSV URLs, and various proprietary databases. Metabase supports around 20-25 natively, with more available through community drivers.

For most teams, both tools cover the databases that matter. But if you need to query something exotic -- a REST API, a Google Sheet, an Elasticsearch cluster -- Redash's broader connector library is worth noting.

Collaboration and sharing

Redash keeps it simple. Queries are saved in a flat list (with tags for organization). Dashboards are collections of query visualizations. You can share a dashboard via URL, set permissions at the query or dashboard level, and schedule queries to run and email results on a cron. There are no folders, no collections, no nested organization. For small teams this is fine. For larger organizations, the flat structure becomes a mess.

Metabase offers a more structured approach. Queries (called "questions" in Metabase's terminology) live in collections, which function like folders. Collections have permissions. You can create sub-collections, pin important items, and organize by team or project. Dashboards support subscriptions (email, Slack) on a schedule. The sharing model is more granular and scales better.

Metabase also has a concept of "verified" questions -- queries that an admin has reviewed and marked as trusted. This is surprisingly useful for preventing the "which revenue number is right?" problem that plagues every data team.

Alerting

Both tools offer basic alerting, but neither is particularly sophisticated.

Redash lets you set alerts on query results: when a value goes above or below a threshold, or when it changes. Notifications go to email, Slack, or webhooks. Simple and functional.

Metabase has a similar feature: alerts when a result crosses a threshold or when a goal line is hit. Notifications via email or Slack.

Neither tool has routing, escalation, silencing, or anything resembling the alerting sophistication of Grafana or a dedicated incident management tool. If alerting is a primary requirement, you will need something else alongside either tool. We wrote about how Grafana's alerting compares to Metabase's if that dimension matters for your decision.

Deployment and ops

Both run in Docker. Both are quick to get started.

Redash:

docker compose up -d

Redash requires PostgreSQL (for metadata) and Redis (for job queuing). The Docker Compose file bundles both. A typical Redash deployment is 4-5 containers: web server, worker(s), scheduler, PostgreSQL, Redis.

Metabase:

docker run -d -p 3000:3000 metabase/metabase

Metabase can run as a single container with an embedded H2 database for quick evaluation. For production, you point it at PostgreSQL for metadata storage. One container, one database -- simpler operationally.

The difference in operational complexity is meaningful. Redash's Redis dependency and separate worker/scheduler processes mean more things that can break. Metabase's single-JAR architecture is simpler to deploy, monitor, and debug.

The comparison table

Migrating from Redash to Metabase

A lot of teams are making this move right now, so it is worth discussing what the migration actually looks like.

There is no official migration tool. No "Import from Redash" button. The migration is manual, and here is what it involves:

1. Data sources. Reconnect each database in Metabase. This is straightforward -- enter the same connection strings. Budget 15 minutes per data source.

2. Queries. This is the painful part. Every Redash query needs to be recreated in Metabase. If you have 50 queries, that is a day of work. If you have 500, it is a week. Copy-paste the SQL into Metabase's SQL editor. The queries will mostly work as-is, but Redash-specific parameter syntax ({{param}}) needs to be converted to Metabase's {% raw %}{{param}}{% endraw %} syntax (similar but not identical).

3. Dashboards. Rebuild manually. There is no shortcut. The layout system is different, the widget types are different, and the filter wiring is different.

4. Alerts and schedules. Recreate in Metabase. Again, manual.

5. Permissions. Map Redash groups to Metabase collections and permission groups. This is a good opportunity to clean up the permission model that has probably drifted over the years.

The total effort scales linearly with the number of queries and dashboards. For a team with 20-30 queries and 5-10 dashboards, plan a weekend. For a team with hundreds of queries, plan a sprint.

One silver lining: the migration is a good forcing function. Every team I have talked to that migrated from Redash to Metabase ended up pruning 40-60% of their queries. "We migrated the ones people actually use and left the rest behind."

When Redash still makes sense

Redash is not dead. If these describe your situation, it might still be the right tool:

• Your entire team writes SQL and nobody else touches the dashboards. The visual query builder is irrelevant. Redash's SQL-first editor is faster for pure SQL workflows.
• You need to query non-database sources. Google Sheets, REST APIs, CSV endpoints. Redash's broader data source library has real value here.
• You already run Redash and it works. There is no reason to migrate a working system purely because the project has slowed down. Migrate when something forces your hand -- a critical vulnerability, a driver incompatibility, or a team that has outgrown the tool.
• You want fully free, no paid tier. Redash has no commercial edition. Everything is open source. Metabase's OSS edition is capable but gates SSO, embedding, and row-level permissions behind the $85/user/month Pro tier.

When Metabase wins

Metabase is the stronger choice when:

• Non-technical users need to self-serve. The visual query builder is the single most important feature in this comparison. If even one stakeholder needs to build reports without SQL, Metabase wins by default.
• You care about long-term maintenance. Active development, regular releases, security patches, new features. Metabase has a team shipping code. Redash does not -- or at least, not consistently.
• You need embedding. Putting charts inside your own product is a core Metabase Pro use case. Redash has basic embedding, but Metabase's is more mature and better documented.
• Your organization is growing. Collections, verified questions, granular permissions -- Metabase's organizational features scale better than Redash's flat structure.
• You want polished visualizations. Metabase charts look better with less effort. This matters when dashboards go to executives or customers.

For a broader view of Metabase alternatives, including Superset, Lightdash, and others, see our Metabase alternatives roundup. And if you are evaluating Metabase against Superset specifically, we wrote a detailed Metabase vs Superset comparison. Our best open-source dashboard tools guide also ranks all seven major options by use case. For the Power BI angle, our Power BI vs Metabase comparison covers when open source wins.

Where both tools hit a ceiling

Both Redash and Metabase are query-and-visualize tools. You connect a database, you run a query, you see a chart. That model works well for retrospective reporting -- "what happened last month?" -- but it struggles with anything that needs custom logic, real-time event processing, or code execution.

Things that are hard or impossible in both tools: joining data across databases (Stripe payments with CRM records, for example), running Python transformations on query results before visualizing, building interactive applications that go beyond filtering, or triggering actions when a metric changes. If you find yourself hitting those walls, tools like Fastero that combine SQL querying with code execution and event-driven workflows can fill the gap.

For teams evaluating how to automate SQL reports without a BI tool, we wrote a separate guide that covers the full spectrum from cron scripts to managed platforms.

My actual recommendation

If you are choosing today between Redash and Metabase for a new deployment: pick Metabase. Not because Redash is bad -- its SQL editor is genuinely excellent -- but because betting on a tool with unclear maintenance status is a risk you do not need to take. Metabase covers everything Redash does (SQL mode works fine for technical users) and adds the visual query builder, better visualizations, and active development.

If you already run Redash and it works: do not panic-migrate. Keep running it until you have a concrete reason to move. When that reason arrives -- and it probably will, eventually -- Metabase is the most natural destination.

And if your team is pure SQL and you actively do not want a visual query builder, look at Apache Superset too. It occupies a similar SQL-forward niche but has a more active open-source community than Redash. We also wrote a full three-way comparison of Superset vs Metabase vs Redash if you're evaluating all three at once, and a dedicated Superset vs Redash comparison for SQL teams if you have already ruled out Metabase.

Try Fastero free — connect your database, ask questions in plain English, and get dashboards that update themselves — no BI tool learning curve. No credit card required.

Last updated: July 2026. Metabase ships monthly -- check metabase.com/docs for the latest. Redash's open-source repo is at github.com/getredash/redash.