Evidence Alternatives (2026): 8 BI Tools for Data Teams

Who on your team writes SQL, and who just wants the number? That one question sorts the business intelligence tools below faster than any feature grid. Evidence builds reports from SQL and markdown, which data teams tend to love and everyone else tends to find hard to touch. If that's pushing you to look elsewhere, here's what else is worth a look, and who each option is built for.

Reports as code versus reports by clicking

Evidence pitches itself as the code-based answer to drag-and-drop BI. That's a clear position, and it tells you exactly what to test in anything that replaces it: how people ask questions of your data, and how dashboards get shared and kept up to date once they're built. A report that one analyst updates through version control is a very different animal from a dashboard a sales manager filters on her own at nine in the morning.

Tools in this category split by who they're built for and how much you set up yourself. Some expect you to define metrics in a modelling layer before anyone sees a chart. Others connect straight to a database and let you query on day one. Neither approach is wrong, but swapping one for the other changes who does the work every week. So run the same real task through each shortlisted tool before you decide, rather than judging on the sample dashboards.

Which job each tool does best

For analysts who think in code

Mode puts SQL, Python and R next to visual reports in one shared workspace, and Redash keeps things lean: connect a database, write a query, chart it and share it. Both will feel familiar if your team liked writing reports more than dragging fields onto a canvas.

For one shared set of metrics

Cube and Holistics both lean on a modelling layer, so a metric like active customers means the same thing in every chart. The first describes itself as agentic analytics built on a semantic layer, with embedded analytics for your own customers too. The second is aimed at data teams who want to hand self-service reporting to the wider business without losing control of the definitions.

For open source fans

Metabase is open source and says every answer can be traced back to the query behind it. Preset is a hosted version of Apache Superset, so you get the open source project without running servers or handling upgrades yourself.

For business users and bigger firms

Sigma gives business users a spreadsheet-style way into warehouse data, with SQL and Python there for the analysts. ThoughtSpot is pitched at larger organisations that want governed AI answers inside the tools staff already use every day.

Eight BI tools at a glance

Tool	Approach	Good for
Cube	Semantic layer analytics	Shared metric definitions behind dashboards and embedded analytics
Holistics	Self-service BI	Data teams handing reporting to the wider business
Metabase	Open source BI	Questions and dashboards you can check down to the query
Mode	SQL, Python and R workspace	Analysts mixing code and charts in one shared space
Preset	Hosted Apache Superset	Open source dashboards without hosting them yourself
Redash	Query and visualise	Connecting many databases and sharing query results
Sigma	Spreadsheet-style warehouse BI	Business users working on warehouse data like a spreadsheet
ThoughtSpot	Search and AI analytics	Larger firms putting governed AI answers into daily tools

The full list, A to Z

Alphabetical and in no particular order. Each line sums up how the vendor describes its own product, in plain words.

Cube

Best for: Data teams that want one semantic layer behind internal dashboards and customer-facing analytics, with AI agents answering questions on top, as the vendor pitches it.

Holistics

Best for: Teams whose analysts want to set up self-service reporting the rest of the company can use, with AI help the vendor says gives reliable answers.

Metabase

Best for: Companies wanting an open source option where every answer links back to its query and the metrics defined behind it; a free plan is named.

Mode

Best for: Analysts who write SQL, Python or R and want visual reports alongside the code, in a workspace the vendor describes as collaborative.

Preset

Best for: Fans of Apache Superset who'd rather not host it, with dashboards, embedded analytics and a chatbot for questions, according to the vendor.

Redash

Best for: Technical teams connecting databases such as PostgreSQL, MySQL, BigQuery or MongoDB, then querying, charting and sharing the results across the company.

Sigma

Best for: Mixed teams on a cloud data warehouse: the vendor says people can build with chat, a spreadsheet, SQL or Python while IT keeps control.

ThoughtSpot

Best for: Bigger organisations wanting AI agents, automated insights and analytics embedded where staff already work, in the vendor's own description.

My advice for data teams choosing a BI tool

Don't pick on the demo dashboard. Every vendor's sample looks tidy because someone spent days on it. Pick on the dull jobs instead: adding a new metric, fixing a broken query, and getting a chart in front of someone who's never logged in before. If your analysts loved writing reports as code, stay close to that with a query or notebook tool. If the real problem was that nobody outside the data team could help themselves, a self-service or spreadsheet-style tool will change more than any new chart type.

Think about where the dashboards end up, too. If they sit on a website or inside your product, check how each tool handles embedding and how readable the charts are for every visitor; my guide to web accessibility covers what to look for. And if your BI search is part of a bigger software tidy-up, my list of Bitrix24 alternatives looks at all-in-one business suites.

Finally, ask who'll own the tool in a year's time. Open source and code-first options give you control, but someone has to keep them running. Hosted and AI-led tools take that work off your plate, and in return you live with their way of doing things.

A short trial plan before you switch

1. Pick one real question your team asks every week, such as sign-ups by channel.
2. Connect the same data source to two or three shortlisted tools.
3. Ask an analyst to build the answer, then ask a non-technical colleague to change one filter on it without help.
4. Add one new metric and note how many places you had to change it.
5. Share the result with someone outside the team and check it's still up to date the next morning.
6. Keep the tool where both people finished without asking for help.

Do you make business intelligence software? Tool makers can ask to be considered for a featured place on this list.

Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.

Related: python (this site).

Frequently asked questions

What's a semantic layer in BI?

It's a shared set of definitions for your metrics and business rules, so a term like active customer means the same thing in every chart and every answer, whoever builds the report.

Do I need to know SQL to use a BI tool?

Not always. Code-first tools expect SQL, while self-service and spreadsheet-style tools let business users explore data by clicking or typing questions. Most still need someone technical to connect the data first.

Can I move my existing reports to a new BI tool?

Your queries usually move more easily than your dashboards. Save the SQL behind each report, list the metrics you rely on and rebuild the most used dashboards first.

Is open source BI a good fit for a small team?

It can be, if someone on the team is happy to host and update it. If nobody wants that job, look for a hosted version of the same open source project.

What does embedded analytics mean?

It means putting charts and dashboards inside your own product or website, so your customers see their data without logging into a separate BI tool.

How do AI answers in BI tools work?

You type a question in plain English and the tool turns it into a query on your data. Check you can see the query behind each answer before you trust it in a meeting.