If you are still running Redash, you have probably noticed something: the GitHub repository has gone quiet, pull requests pile up without review, and when something breaks you are largely on your own. Redash was genuinely excellent when it launched a clean, SQL-first dashboard tool that worked with almost every database. That story ended with a corporate acquisition that had nothing to do with keeping Redash alive. This guide covers what happened, what Redash users actually valued, and which tools in 2026 come closest to replacing it including tools that do everything Redash did and add capabilities Redash never had.

What Happened to Redash: The Full Timeline

Understanding the Redash situation matters before choosing a replacement, because the lesson shapes what you should look for in the next tool.

2013–2018: Redash launches and grows. Redash was founded by Arik Fraimovich and became a popular open-source business intelligence tool. It offered a clean SQL editor, multi-database support, shared dashboards, and scheduled query refresh. For self-hosted analytics, it was one of the best options available.

2019: Databricks acquires Redash. Databricks, the data and AI platform, acquired Redash. The stated goal at the time was to integrate Redash's capabilities into the Databricks platform. The Redash team was absorbed into Databricks.

November 2021: Databricks shuts down the hosted Redash service. The hosted version at app.redash.io was shut down with approximately three months' notice. Teams relying on the hosted service either had to self-host or migrate to another tool.

2021–present: Open-source maintenance declines. The open-source repository remains available on GitHub but has seen minimal meaningful maintenance. There have been no major feature releases, dependency updates have lagged, and security vulnerabilities have accumulated without official patches. Community contributors have kept it partially alive, but the project has no organizational backer with an interest in maintaining it.

2026: The practical situation. Running Redash in production in 2026 means running software with outdated dependencies, known security vulnerabilities, and no roadmap. Docker-based self-hosting still works, but upgrades are risky, debugging requires deep familiarity with the codebase, and there is no path to new features. For teams on older Redash versions, the migration question is not "should we move?" but "when and to what?"

What Redash Users Loved (And Why It Mattered)

Before recommending alternatives, it is worth being specific about what made Redash genuinely good. These are the features to verify in any replacement.

The SQL editor was excellent. Redash had schema browsing, auto-complete, syntax highlighting, and multi-query execution. It felt like a purpose-built tool for analysts who think in SQL, not an afterthought. Many alternatives claim SQL editor quality but fall short on the details particularly schema auto-complete.

Multi-database support was comprehensive. Redash supported PostgreSQL, MySQL, SQLite, Microsoft SQL Server, BigQuery, Amazon Redshift, Presto, Hive, MongoDB, and more. For organizations with heterogeneous data infrastructure, this meant a single tool could address most analytics needs.

Dashboard scheduling was simple and reliable. You could set any query to refresh on a schedule, and dashboards composed of those queries would update automatically. This is table stakes for a dashboard tool, but Redash did it reliably without requiring infrastructure expertise to maintain.

Simplicity was a feature. Redash did not try to be a full business intelligence platform with drag-and-drop report builders and enterprise role management. It was a SQL editor with dashboards attached. That simplicity made it fast to adopt and easy to maintain for small engineering teams.

Self-hosting was practical. The Docker-based deployment was documented well, worked as described, and did not require specialized operations expertise. Small teams could run it on a modest server without a dedicated infrastructure engineer.

What Redash did not do: natural language queries, AI-assisted SQL generation, workflow automations, conditional alerts, or any of the capabilities that have become standard in the analytics tools built after 2020.

What to Look for in a Redash Replacement

Before reviewing the alternatives, here is a checklist of what a genuine Redash replacement needs to provide:

SQL editor with schema browsing and auto-complete

Support for your current databases

Dashboard creation from saved queries

Scheduled query refresh

Shared dashboard access for non-SQL users

Reasonable operational cost (either hosting simplicity or affordable SaaS)

Active maintenance and a clear product roadmap

Bonus features that Redash never offered but you should consider now that you are switching:

Natural language query capability for non-technical team members

AI-assisted SQL generation for analysts

Workflow automations (condition-based alerts to Slack, email, or webhooks)

Hosted/managed option to eliminate self-hosting burden entirely

The 8 Best Redash Alternatives in 2026

1. AI for Database Best for Teams Who Want to Move Beyond SQL-Only Analytics

AI for Database (aifordatabase.com) is the alternative that covers everything Redash did and adds capabilities that the next generation of analytics tools requires. It is a fully hosted platform no self-hosting, no Docker, no server maintenance that connects to your existing databases and makes them queryable by anyone on your team, not just the people who write SQL.

For Redash users migrating, the first thing to know is that AI for Database fully supports SQL. If you have analysts who are proficient in SQL and want to keep writing it, they can. The SQL editor is accessible, and your existing Redash queries can be reused directly.

Beyond what Redash offered, AI for Database adds:

Natural language queries anyone on your team types a question in plain English ("show me daily active users for the past two weeks") and gets a correctly generated query and visualization. The business team no longer requires an analyst to translate requests into SQL.

Self-refreshing dashboards save queries as dashboard tiles, set refresh intervals, and share a URL. Your stakeholders see live data without any manual steps. This is the same scheduled refresh Redash had, with a significantly better sharing experience because it is fully hosted.

Workflow automations define conditions against your database data and trigger alerts to Slack, email, or a webhook when those conditions are met. "Alert the team when the overnight batch job table shows fewer than 100 new rows" is a workflow, not a custom script. Redash had no equivalent feature.

Database support includes PostgreSQL, MySQL, SQLite, MongoDB, Supabase, PlanetScale, MS SQL Server, and BigQuery. For most Redash users whose data lives in Postgres, MySQL, or SQL Server, the coverage is complete.

The free tier is available, and paid plans are priced for teams rather than enterprises. For former Redash users who were self-hosting to avoid SaaS costs, the comparison is no longer just licensing it is licensing versus the time and operational overhead of running your own infrastructure.

2. Apache Superset Best Open-Source Replacement

Apache Superset is the most direct open-source successor to Redash in terms of feature scope. It is a mature project under active development by the Apache Software Foundation, has a large community, and is used by many organizations at scale.

Superset offers a rich SQL editor with schema browsing, a no-code chart builder for non-technical users, dashboards, scheduled refresh, and support for a wide range of databases via SQLAlchemy connections. It is more capable than Redash in almost every dimension.

The tradeoff is complexity. Superset is significantly more complex to self-host than Redash was. The recommended production deployment involves multiple services (the Superset web server, Celery workers, Redis, a separate metadata database), and the initial setup requires more configuration experience than Redash's Docker Compose setup. Upgrades between versions have historically required careful attention to migration steps.

For organizations with an infrastructure engineering team or a dedicated data engineering function, Superset is the right open-source choice. For small engineering teams who found Redash manageable but would struggle with a more complex operational footprint, the self-hosting burden may outweigh the benefits.

Best for: Organizations with infrastructure capacity who want an open-source BI platform with no licensing costs.

3. Grafana Best for Infrastructure and Time-Series Data

Grafana is the dominant tool for infrastructure monitoring, time-series dashboards, and operational metrics. It connects to a wide range of data sources including PostgreSQL, MySQL, InfluxDB, Prometheus, Elasticsearch, and many others.

For Redash users whose primary use case was monitoring application performance metrics, infrastructure health, or time-series data, Grafana is an excellent replacement. The dashboard capabilities are mature, alerting is built in and works well, and there is a large community and plugin ecosystem.

Where Grafana falls short as a general Redash replacement is in its handling of relational business data. It is not designed for the analyst workflow of writing arbitrary SQL against a relational database and building ad hoc dashboards from the results. The SQL-to-chart workflow that was Redash's core use case is not what Grafana optimizes for. Business intelligence queries "revenue by plan last quarter", "cohort retention by signup month" are possible but awkward in Grafana.

Grafana Cloud offers a hosted version that removes the self-hosting burden, with a free tier and paid plans for higher usage.

Best for: Teams whose Redash use was primarily monitoring operational and infrastructure metrics, not business analytics.

4. Metabase Best Open-Source Option for Non-Technical Users

Metabase is an open-source business intelligence tool that targets a broader audience than Redash did. Where Redash was SQL-first, Metabase includes a visual query builder that lets non-technical users explore data without writing SQL. SQL is still available for power users.

Metabase is one of the most widely adopted open-source BI tools in the market. It is actively maintained, has a managed cloud option (Metabase Cloud), and has a large plugin and connector ecosystem. Dashboard creation, scheduled refresh, and email-based alerts are all included.

For Redash users who want to stay on open-source and extend analytics access to non-SQL users, Metabase is the strongest option. The visual query builder is genuinely usable for business users without training, and the SQL editor is complete enough for analyst workflows.

The open-source version is self-hosted. Metabase Cloud removes the hosting burden with a paid subscription. The Enterprise edition adds role-based access control, embedding, and other features for larger organizations.

Best for: Teams wanting open-source with a good non-technical user experience and active maintenance.

5. Holistics Best Managed BI for SQL Teams

Holistics is a managed business intelligence platform oriented around a code-first, SQL-based workflow called AMIR (Analytics-as-Modeling-in-Repository). The approach is similar to dbt you define your metrics and transformations in SQL and YAML, version-control them, and build dashboards from those modeled definitions.

For data teams who already follow a dbt-style workflow and want a BI layer that fits that paradigm, Holistics is well-suited. The SQL editor is high quality, dashboards are clean, and the caching model is designed for teams running queries against large datasets.

Holistics is a pure SaaS product, so there is no self-hosting option. It is priced for teams and scales reasonably. For former Redash users who want a managed product with a SQL-first philosophy and active development, it is worth evaluating.

Best for: Data teams using dbt or a similar SQL modeling workflow who want managed hosting.

6. PopSQL Best for Collaborative SQL Workflows

PopSQL focuses on the collaborative SQL editor experience. It is a managed tool where analysts can write SQL, share queries with teammates, build simple dashboards, and maintain a team-wide query library.

The value proposition is the combination of a high-quality SQL editor with version history, team query sharing, and dashboards in a single hosted product. For teams whose main Redash use was collaborative query development rather than end-user dashboards, PopSQL addresses that core need well.

PopSQL does not have the same breadth of dashboard capabilities as Redash, and there are no workflow automations or conditional alerts. It is primarily a SQL editor and query collaboration platform that also produces dashboards, rather than a dashboard-first tool.

Best for: Analytics teams whose primary need is collaborative SQL writing and query sharing.

7. BlazeSQL Best AI-Assisted SQL for Analysts

BlazeSQL is an AI-powered SQL tool that helps analysts write queries faster. You describe what you want, it generates SQL, you review and run it. The workflow is designed for analysts comfortable with SQL who want AI as an accelerator rather than a replacement.

For Redash users who primarily used the SQL editor for analysis and found the dashboard capabilities secondary, BlazeSQL offers a modern version of that SQL-centric workflow with AI assistance. Dashboard capabilities are more limited than Redash's, and there are no self-refreshing dashboards or workflow automations.

Best for: Individual analysts who want AI assistance in SQL writing without building out a full dashboard infrastructure.

8. Evidence Best for Code-Driven Analytics Reports

Evidence is an open-source tool for building analytics reports and dashboards using Markdown and SQL. You write .md files that contain SQL queries and chart definitions, and Evidence renders them as a static analytics site that can be hosted anywhere.

This is a fundamentally different workflow from Redash it is code-driven rather than UI-driven but for engineering teams who prefer infrastructure-as-code patterns, it is compelling. The output is highly customizable, version-controllable, and lightweight.

Evidence does not have a point-and-click SQL editor or a non-technical user interface. It is designed for data engineers and developers who are comfortable writing code and want analytics that live in their repository alongside their application code.

Best for: Engineering teams who want analytics reports as code, version-controlled in git.

Comparison Table

Moving from Redash to AI for Database: A Migration Guide

If you are ready to migrate from Redash and AI for Database is your chosen replacement, the process is straightforward. Unlike moving to a self-hosted alternative, there is no infrastructure to configure the complexity is almost entirely in the data connection and query recreation steps.

Step 1: Export Your Redash Queries

From your Redash instance, export your existing queries. You can do this via the Redash API (/api/queries?page_size=100) or by manually copying the SQL from your most important queries. Focus on the queries that power your active dashboards and scheduled reports these are the ones worth migrating first.

Step 2: Create Your AI for Database Account

Sign up at https://app.aifordatabase.com/signup. The free tier supports the connection and query volumes typical of a migrating Redash team. You do not need a paid plan to start.

Step 3: Connect Your Databases

In AI for Database, click Add Connection and enter the same connection details your Redash queries use. If Redash connected to a PostgreSQL database at a given host and port, those same credentials work in AI for Database. Repeat for each data source.

One advantage over Redash's setup: AI for Database supports SSL connections and handles connection pooling transparently. If your database is behind a connection pooler, AI for Database handles this without requiring you to configure connection pool settings manually.

Step 4: Recreate Your Most Important Dashboards

Rather than migrating every Redash query, start with the ten to twenty queries that represent your most-viewed dashboards. Paste the SQL directly into AI for Database or rephrase each query in natural language and let the AI generate the equivalent SQL. The natural language approach is faster for common business metrics; pasting SQL directly is more reliable for complex queries with specific business logic.

Create your dashboards, set refresh intervals matching what you had in Redash, and share the URLs with your team.

Step 5: Set Up Workflow Alerts

For any Redash scheduled queries that served as monitoring checks queries you ran to look for specific conditions convert these to AI for Database workflow automations. Define the condition, set the schedule, and configure the alert channel. This replaces the "run the query and look at it" habit with an active alert that comes to you.

Step 6: Decommission Redash

Once your new dashboards are running and your team has adopted the new tool, you can shut down your Redash instance. If you were paying for server hosting, this reduces ongoing costs. The data, of course, stays in your database Redash and AI for Database are both read-only analytics tools.

Special Angle: What AI for Database Gives Redash Users That Redash Never Had

Redash users know what they are giving up: a familiar SQL editor, a well-understood deployment model, and an interface that had been stable for years. What they are gaining when moving to AI for Database is worth being specific about.

The analytics team is no longer the bottleneck. In a Redash setup, every business question that requires data goes through someone who can write SQL. With AI for Database's natural language interface, the product manager can ask "how many users activated their account in the first 48 hours after signup last month?" directly, get an answer, and iterate on the question without waiting for an analyst.

Monitoring is proactive rather than reactive. Redash told you what was happening when you looked. AI for Database's workflow automations tell you when something worth looking at has happened. The difference compounds over time anomalies get caught faster, and the team spends less time manually checking dashboards.

Operational overhead drops to zero. Running Redash in 2026 means maintaining a Docker deployment, managing Python dependencies, and dealing with security patches that are no longer officially released. AI for Database is fully managed there are no servers to maintain, no upgrade procedures to follow, and no operational incidents caused by the analytics tool itself.

Conclusion

Redash had a good run. For many teams, it was the first analytics tool that actually worked for their small engineering team's needs SQL-first, multi-database, self-hostable, and simple enough to not require a dedicated data team to run. The decision to acquire and then deprioritize it was Databricks' call to make, and it made sense for their business. It did not make sense for the teams still running it.

In 2026, moving off Redash is the responsible choice. The question is where to go. For teams who want to stay in the open-source self-hosted world, Apache Superset and Metabase are both solid. For teams who want to eliminate infrastructure overhead entirely and gain capabilities Redash never had natural language queries, workflow automations, and zero maintenance AI for Database is the direct upgrade.

If you are ready to put the Redash migration behind you, start your free account at https://app.aifordatabase.com/signup. Connecting your first database takes minutes, and your first self-refreshing dashboard will be ready before the end of the day.

Redash Is No Longer Maintained. Here Are the 8 Best Alternatives in 2026