- Open source BI and data exploration tools compared
- Nine tools for dashboards, BI as code and data apps
- Who builds the reports decides the tool
- AI agents in open source BI
- SQL editors, spreadsheets and Python apps
- Questions to settle before you install anything
- My advice on choosing open source BI
- Frequently asked questions
The best open source BI tool depends on who will build the reports. Point-and-click dashboard tools suit business users, BI-as-code tools suit analysts who work in a repo, semantic-layer platforms suit teams that want one governed model, and Python app frameworks suit data scientists. Pick the working style first, then the tool.
Ask three data teams what open source BI means and you'll get three different pictures: a dashboard builder, a folder of SQL and Markdown files, or a Python script that turns into a web app. They're all fair answers, and that's what makes this shelf confusing.
This list covers nine tools for business intelligence and data exploration, using only what each maker's own website says, read on 2 Nov 2026 (as of Nov 2026). It's alphabetical and in no particular order. Some makers here put the words open source on their homepage and some don't, so check the licence on each maker's site before you build on it.
Open source BI and data exploration tools compared
| Tool | Type | Good for |
|---|---|---|
| Cube | Agentic analytics platform on a semantic layer | Embedded analytics and an AI context layer |
| Evidence | Business intelligence as code | Pages written in Markdown, SQL and components |
| Mathesar | Open-source UI for Postgres data | A spreadsheet-like view of Postgres |
| Metabase | Open source AI analytics | Self-service and embedded analytics |
| Preset | AI-native BI, fully managed in the cloud | A cloud-hosted service with an AI chatbot |
| Redash | SQL editor with shared dashboards | Querying data sources from the browser |
| Rill | Agentic analytics and business intelligence | A natural language interface over your data |
| Streamlit | Data apps built in Python | Turning data scripts into shareable web apps |
| Superset | Open-source data exploration and visualization | Separate guides for users, admins and developers |
Nine tools for dashboards, BI as code and data apps
Cube
Best for: Building on one governed semantic model, with data modeling, embedded analytics and an AI context layer on the same platform.
Evidence
Best for: Writing reports and dashboards as pages in Markdown, SQL and components, with an analytics agent and embedded analytics alongside.
Mathesar
Best for: Letting DB admins and business users view, edit and query Postgres data in a spreadsheet-like UI that works with Postgres permissions.
Metabase
Best for: Giving your team self-service analytics, or embedding customer-facing analytics, with AI answers you can check against the query behind them.
Preset
Best for: Running AI-native BI built on Apache Superset as a fully managed, cloud-hosted service, or in your own private cloud.
Redash
Best for: Connecting and querying your data sources in an online SQL editor, then building dashboards and sharing them with your company.
Rill
Best for: Exploring large datasets with an AI agent and a natural language interface, so questions don't have to wait on an analyst.
Streamlit
Best for: Turning Python data scripts into shareable web apps, with no front-end experience needed, and putting them online through its Community Cloud.
Superset
Best for: Exploring data, building charts and dashboards and connecting databases, with docs split into guides for users, admins and developers.
Who builds the reports decides the tool
Here's the split most searchers don't expect. These tools aren't versions of one product: they're built for different people working in different ways. Evidence calls itself 'business intelligence as code', with pages authored in Markdown, SQL and components. Metabase pitches 'open source AI analytics you can verify' and self-service analytics for your team. Cube is 'the agentic analytics platform built on a semantic layer', and Mathesar offers a 'spreadsheet-like' UI for Postgres data.
So the useful first question isn't which tool has the longest feature list. It's who'll build the reports: business users clicking through a query builder, analysts who want reports in Git, a data team that wants one model every tool reads from, or data scientists who'd rather stay in Python.
- Point-and-click and SQL dashboards: query builders, online SQL editors and dashboards you share with the company.
- BI as code: reports written as files in Markdown and SQL, kept in version control.
- Semantic-layer analytics: one governed model that keeps answers consistent wherever they're asked.
- Python data apps: data scripts that become shareable web apps.
AI agents in open source BI
Several makers here now put AI agents up front. Rill's homepage says its AI agent runs queries to find the 'why' behind your numbers, so anyone can explore large datasets without waiting on an analyst. Preset says you can use its AI chatbot or connect your own agent over MCP. Another maker's latest release announcement mentions a better MCP server, and one describes business intelligence built for AI agents.
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Treat that as a second question, though. Work out who builds the reports first, then check whether the AI features sit on top of the model your team already trusts.
SQL editors, spreadsheets and Python apps
Not every tool here is a dashboard builder in the classic sense. Redash pairs a 'powerful online SQL editor' with dashboards you share with your company, and its page calls it a popular Open Source project. Streamlit is about building and sharing data apps: its page says you turn data scripts into shareable web apps, all in pure Python, with no front-end experience required.
And one Apache project on this list describes itself as 'an open-source modern data exploration and visualization platform', with docs split into guides for users, admins and developers. If you'd rather run the open-source project yourself than use a managed service built on it, that split shows who'll be doing the work.
Questions to settle before you install anything
- Who'll build most of the reports: business users, analysts or data scientists?
- Do we want reports in version control, or built in a browser?
- Do we need one governed model that other tools can query?
- Will we self-host the open-source project, or would we rather have a managed service?
- Do we need to embed analytics in our own product for customers?
- Which databases and data sources does each tool connect to?
Do you make an open source BI or data exploration tool? Tool makers can ask to be considered for a featured place on this list.
My advice on choosing open source BI
Start with people, not features. If business users will build most of the reports, shortlist the point-and-click and SQL dashboard tools. If your analysts live in Git, look at BI as code. If several tools need the same numbers, look at the semantic-layer platforms, and if your data scientists work in Python, try a data app framework. Then check the licence and hosting options on each maker's own site, and treat any speed or scale line as the maker's own claim.
If you're still deciding where the data lives, here's why choosing a good database matters for your business. And if your sources don't talk to each other yet, my guide to common data integration challenges is worth a read first.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
Which of these BI tools say they're open source?
Several do on their own pages. One pitches open source AI analytics, one calls itself an open-source UI for Postgres data, one describes an open-source data exploration and visualization platform, and one says it's a popular Open Source project. Check each maker's licence before you build on it.
What does BI as code mean?
One maker on this list uses the phrase, and its page says you author pages with Markdown, SQL and components, with deep Git integration so reports ship like software. If your analysts already write SQL in a code editor, that's the style to look at.
What is a semantic layer in BI?
One maker here describes itself as an agentic analytics platform built on a semantic layer, and its page says one semantic model keeps every answer governed and consistent. Ask each maker how its model is defined and which tools can query it.
Can I embed these tools in my own product?
Some say so. One maker lists customer-facing analytics, two more list embedded analytics, and a managed service on this list offers embedded dashboards for your custom applications.
Is there a managed version if I don't want to self-host?
For some, yes. One company on this list runs a fully managed, cloud-hosted service for an open-source Apache BI platform, and other makers here show cloud options on their sites. Check each maker's hosting page for what's on offer.
Do these tools work with AI agents?
Several mention it. One describes business intelligence built for AI agents, one offers agentic analytics with a natural language interface, and one says you can use its AI chatbot or connect your own agent over MCP. Treat AI features as a second question after who builds the reports.
