BI tools now compete on AI. Here is what each one is best for.
Most BI tools now ship an AI agent. The question is not which tool draws the best chart. It is whether an agent gives the same number as your dashboard. Here are 9 tools, who each one suits, what it costs, and where it falls short.
Kater makes Kater, one of the tools on this list. We list it first. Every fact about another tool comes from its own docs and pricing pages, and each card says where that tool is strong. Reviewed October 2026.
Is each metric defined once, so a dashboard and an agent give the same number?
AI and agents
Does the tool answer in its own agent, or in the agent your team already uses?
Content in git
Are models, queries and dashboards files you can review, test and undo?
Pricing
Do you pay for each viewer, each editor, AI credits or a capacity?
Fit
Which warehouses does it connect to, and who on your team does the work?
The 9 tools
01
Kater
BI for people and agentsBy Kater
Best for teams whose own AI agent builds and answers on governed metrics
Your data team defines each metric once, in SQL. Your coding agent builds models, queries, dashboards and apps as files in git, and Kater compiles every change. People read dashboards in the browser, or ask in Slack, Claude or ChatGPT, and every answer uses the same metrics.
A good fit if
You want your own AI agent to build and answer on governed metrics.
You want every model, dashboard and app in git, with a review before it ships.
You do not want to pay for each viewer or each dashboard.
Your warehouse is Snowflake or DuckDB.
Watch out for
Kater connects to Snowflake and DuckDB today.
Embedding is in development.
Kater is not open source. Your content is files you can clone and keep.
Kater is a young company.
AI
None of its own. Your agent: Claude, ChatGPT, Claude Code, Codex or Cursor.
Pricing
A 30-day free trial with every feature, then a subscription quoted for your team. Kater does not charge per viewer or per dashboard. Kater runs no chat of its own, so answers run in the agent your team already uses.
Tableau is the best tool for drawing charts by hand.
A good fit if
Your analysts build highly custom visuals by hand, and love it.
You need offline work on extracts in Tableau Desktop.
You embed Tableau views in your own product.
Watch out for
Renewals jump under Salesforce.
The product stopped moving.
No version control, and no checks.
Building by hand is slow next to an agent.
AI
Hosted Tableau MCP connects Claude and ChatGPT. Tableau Agent needs Cloud+ or the Tableau+ Bundle.
Pricing
Tableau Cloud is priced per user per month, billed annually. Standard is $75 for a Creator, $42 for an Explorer and $15 for a Viewer. Enterprise is $115, $70 and $35. Tableau Agent needs Cloud+ or the Tableau+ Bundle, priced by quote, and Tableau Next starts at $40 a user a month.
You are a Microsoft shop, and Power BI comes with your E5 licenses.
Your analysts live in Excel and Power Query.
Your data is in Fabric, SQL Server or Azure.
You embed Power BI reports in your own product.
Watch out for
DAX is hard to write, and harder to debug.
Costs climb, and everything points to Fabric.
Git came late, and it breaks.
Copilot uses up capacity, not your backlog.
AI
Copilot, which needs Fabric capacity, and an MCP server for governed models.
Pricing
Power BI Pro is $14 a user a month and Premium Per User is $24, after increases in April 2025. Below an F64 capacity, every viewer needs a paid license. Fabric capacity runs from about $263 a month for F2 to about $8,410 for F64, pay as you go. Copilot needs F2 or higher.
Best for teams committed to Google Cloud and BigQuery
Looker made the governed model and git normal in BI.
A good fit if
You embed Looker in your own product.
You are committed to Google Cloud, BigQuery and Gemini.
Your LookML project is large, stable and well staffed.
Watch out for
LookML is a second language.
Only the model is in git.
The price keeps going up.
The core product moves slowly.
AI
Conversational Analytics in Looker and Gemini Enterprise. The managed MCP server is in preview.
Pricing
Looker sells a platform edition (Standard, Enterprise or Embed) plus Developer, Standard and Viewer users, on annual terms. Google lists no prices. The Standard edition lists at $60,000 a year on AWS Marketplace, before users. Conversational Analytics will be metered in data tokens after its promotional period.
Your business users want to build their own analysis in a spreadsheet grid.
You need input tables that write back to the warehouse.
You embed analytics in your own product.
Your warehouse is BigQuery, Databricks or Redshift.
Watch out for
Git keeps a copy. Sigma keeps the work.
Builder seats get rationed.
Agents cost extra.
It looks like Excel, but it is not Excel.
AI
A Sigma MCP server for Claude, ChatGPT and Cursor. Sigma Agents are a paid add-on.
Pricing
Sigma does not publish prices. Its AWS Marketplace listing shows an Enterprise platform at $150,000 a year, with creator and explorer seats at $2,000 each and free viewers. Vendr reports a median contract near $65,000 a year. Sigma Agents are a premium add-on, metered in credits.
Metabot in every plan, with Slack and an MCP server.
Pricing
Metabase is free to host yourself. Cloud Starter is $100 a month with 5 users, then $6 for each user. Pro is $575 a month with 10 users, then $12 for each user. Enterprise starts at $20,000 a year. Git sync and row-level permissions need Pro or Enterprise.
Your analysts write one-off Python in notebooks for statistical work.
Exploration matters more to you than governed dashboards.
Your warehouse is BigQuery, Databricks or Postgres.
Watch out for
Notebooks multiply, and numbers drift.
Dashboards are not its strength.
Git is an export, not the source.
Credits cap the AI.
AI
Threads in Hex and Slack. Each person needs an Explorer seat, and use draws on credits.
Pricing
Hex charges for each editor: $36 a month on Professional and $75 on Team, with Enterprise by quote. Business users who ask Threads need an Explorer seat. Agent use draws on monthly credits, and 50 more credits cost $25.
A semantic layer defines metrics for other tools to query. It has no dashboards of its
own, so it is not on the list above.
dbt Semantic Layer
The dbt Semantic Layer defines metrics in YAML, for other tools to query. Kater reads the SQL dbt compiles, and adds measures, dashboards and apps on top.
It depends on your team. For teams whose own AI agent builds and answers on governed metrics, Kater. For analysts who draw custom visuals by hand, Tableau. For Microsoft shops, Power BI. For teams committed to Google Cloud and BigQuery, Looker. For business users who think in spreadsheets, Sigma. For a free BI tool you host yourself, Metabase. For analysts who work in Python notebooks, Hex. For dashboard-first teams that want a governed model, Omni. For teams that keep their metrics in dbt, Lightdash.
What is the best BI tool for AI agents?
A tool where the agent reads governed metrics, so it gives the same number as your dashboards. We built Kater for this. Your own agent, such as Claude, ChatGPT or Claude Code, builds and answers on metrics your data team defines once in SQL. Most other tools ship a built-in agent of their own.
What is the best free BI tool?
Metabase and Lightdash are open source and free to host yourself. Their cloud plans are paid. Kater has a 30-day free trial with every feature.
Do we have to replace our BI tool to try another one?
No. Most BI tools read the same warehouse, so you can run two side by side. Kater runs beside the tool you have, and we do the migration with you for free when you want to move a dashboard.
Already on a BI tool? We do the migration for free.
Tell us what you want to move. We do the migration with you, on your warehouse, and we do not charge for it. Then compare the numbers and decide.