Metabase Use Cases: Business Intelligence vs Embedded Analytics

Metabase serves two distinct use cases, and the right one for you depends on who needs the analytics. Business Intelligence is for internal teams exploring company data on their own. Embedded Analytics is for putting analytics inside your own product so your customers can use it. Both run on the same foundation — a governed semantic layer and AI querying — but they differ in audience, setup, and what you're optimizing for. Metabase publishes separate pricing pages for each use case, so you can compare options directly.

Business Intelligence: self-service analytics for internal teams

This is the use case where your own employees — analysts, operators, support, leadership — explore data without filing requests to a data team.

The core idea from Metabase's own framing: teams explore data on their own, but are "guided to correct answers with a trustworthy foundation." Your data team curates a governed semantic layer of canonical tables and metrics in Data Studio, and everyone else builds on top of that.

What that looks like in practice:

• Plain-English querying. AI is available on all plans, so anyone can ask questions, visualize results, and answer their own questions. Metabot AI handles the query, or you connect via the MCP server.
• Visual query builder. For people who prefer clicking over typing, the no-code builder constructs queries without SQL.
• Drilldowns. Clicking into a chart surfaces the "why" behind a number.
• Dashboards and reporting. Filter and breakout interactive dashboards, combine charts with text in Documents for collaborative reporting, and set up subscriptions and alerts to keep people in the loop.

The payoff is speed on routine data requests. As Cal.com co-founder Peer Richelsen put it, "I don't need to ask an engineer to get me some special data from our customer database, I can make that query on my own and help out the customer in minutes vs hours or days."

Choose this when: your audience is internal, your priority is letting non-technical people answer their own questions, and you want a data team to curate trusted metrics rather than field every request.

Embedded Analytics: customer-facing analytics inside your app

This use case is about shipping analytics as part of your product — your customers see the dashboards, not your employees.

Metabase describes it as putting "production-grade analytics into your app without drama," with the goal of fast, flexible customer-facing analytics. The same semantic layer and AI foundation applies, but the audience and the constraints shift: you're now responsible for what your customers see, how it's scoped to their data, and how it performs at your scale.

Choose this when: analytics is a feature of your product, your customers need to see their own data, and you want to avoid building a reporting layer from scratch.

How the two compare

Both share the same semantic layer and AI querying foundation — the difference is who's on the other side of the screen.

What both use cases have in common

Regardless of which path you take, the same building blocks apply:

• Deployment. Metabase runs as a self-hosted open-source instance or on Metabase Cloud. The self-hosted route is a single Docker command: docker run -d -p 3000:3000 metabase/metabase. Metabase says you can spin up a free, self-hosted instance in minutes, and that it ships with AI features.
• Data connectivity. Query and visualize data from your database or warehouse, and work with your data via the MCP server, CLI, APIs, Slack, and more.
• Verifiability. Every answer is grounded in your semantic layer — your metrics and business logic — so you can inspect the query behind it, whether the answer came from an agent or a teammate.
• Security and compliance. Metabase states it is secure and compliant across SOC1, SOC2, GDPR, CCPA, and more, and that this holds as you scale "from pre-seed to post-IPO."

How to decide

Ask one question first: who is the audience?

• If it's your own team → Business Intelligence.
• If it's your customers → Embedded Analytics.

If you need both, they aren't mutually exclusive — the shared semantic layer means the trusted metrics you curate for internal use can also underpin what you expose to customers.

To compare options, Metabase maintains separate pricing pages for each use case (Business Intelligence and Embedded Analytics), plus a general plans page and a Pro product page. Check those for current tiers and terms, since what's included can differ between the two paths.