What Is Embedded Analytics? Dashboards Inside Your Product, Build vs Buy

Your customers keep asking the same question: “Can I get a report of this?” Your support team exports CSV files by hand. Your sales team loses deals to competitors that show a slick dashboard in the demo. And your product manager has a line on the roadmap that just says “analytics.”

That’s usually when software companies start looking at embedded analytics. It’s also common inside larger organizations that want reports to show up in the CRM, intranet or operations app people already use.

This guide explains what embedded analytics is, how it works and the build-versus-buy questions that decide whether it takes 3 weeks or 3 quarters.

What embedded analytics means

Embedded analytics is the practice of placing reports, dashboards, charts and data exploration tools directly inside another application. The user never leaves your product to see their data.

Microsoft’s Power BI documentation describes it as integrating analytical and BI capabilities directly within applications, so end users can explore and visualize data without switching to a separate BI tool.

There are 2 main audiences:

Microsoft labels these 2 patterns embed for your customers (also called “app owns data”) and embed for your organization (“user owns data”). In the first, your app authenticates to the BI service on the user’s behalf, and users don’t need their own Power BI license. In the second, each user signs in with their own credentials and license.

Why companies add analytics to their product

• Customers expect it. Business software buyers increasingly expect to see their own data inside the tool.
• It reduces manual work. Fewer custom report requests and CSV exports for your support and success teams.
• It can be a paid feature. Many SaaS companies put advanced reporting in a higher pricing tier.
• It keeps users in your app. If customers export data to analyze elsewhere, they spend less time in your product.

Key takeaway: Embedded analytics is a product feature, not an internal reporting project. Plan it with product, engineering and security at the table, not just the data team.

How embedded analytics works

Whether you build or buy, the same 5 pieces are involved:

1. Data layer. Your product data is copied or connected to an analytics-ready store, such as a data warehouse or a read replica, so heavy queries don’t slow your main app. See what a data warehouse is for background.
2. Semantic model. Business definitions, such as orders, active users and revenue, are defined once so every customer sees consistent metrics.
3. Visualization layer. Charts and dashboards are rendered, either by your own code or by an embedded BI platform.
4. Embedding method. The content appears inside your app, usually through a JavaScript library, web component or iframe, controlled by an API.
5. Security and identity. Your app tells the analytics layer who the user is and what they’re allowed to see.

The part you can’t get wrong: tenant isolation

In a multi-customer product, Customer A must never see Customer B’s data. Embedded BI platforms handle this in a few ways. Microsoft’s documentation lists 3 common approaches for Power BI:

• Row-level security (RLS): One shared dataset, filtered per user or per customer.
• Object-level security: Hiding specific tables or columns from certain viewers.
• Workspace-based isolation: Each customer gets its own separate dataset and workspace, which can be combined with RLS.

Tableau’s Embedding API uses signed tokens from your app, through a feature called connected apps, and can pass user attributes to control row-level access. The details vary by vendor, but the principle is the same: your application passes a trusted identity, and the analytics layer filters data based on it. Test isolation as rigorously as you test login.

Build vs buy: the core decision

This is the decision that shapes cost, timeline and flexibility.

A useful rule: if users only need a handful of fixed charts, building may be simpler. If they need filtering, drill-downs, exports, scheduled reports and their own custom dashboards, buying usually wins because those features take years to build well. Our guide to build vs buy for AI uses a similar framework that applies here too.

How embedded analytics is priced

Prices vary widely, and vendors change packaging often, so use quotes rather than published estimates. The common models are:

• Capacity-based: You reserve compute capacity and serve as many viewers as it can handle. Microsoft, for example, requires a purchased capacity for production embedding in its embed for your customers model, with free trial tokens limited to development testing.
• Per-user: You pay for each viewer or each user who creates content. This can get expensive for products with many occasional viewers.
• Usage-based: You pay by sessions, queries or data processed.
• Platform fee: A flat annual license, sometimes tiered by number of customers or features.

When building, the costs are engineering time, cloud infrastructure for the analytics database and the long tail of maintenance. Our article on custom software development cost explains how those estimates usually work, and how much BI tools cost covers standard BI licensing.

Questions to ask before you choose

Product questions 1. What decisions do our users need to make, and which 5 to 10 metrics support them? 2. Do users need fixed dashboards, or will they want to build their own reports? 3. Will analytics be included for everyone or sold as a premium feature?

Technical questions 4. Where will analytics queries run so they don’t slow the main app? 5. How will we isolate each customer’s data, and how will we test it? 6. Does the platform support our sign-in method and single sign-on? 7. Can we match our product’s look, including fonts, colors and removing vendor branding?

Commercial and compliance questions 8. How does pricing scale if our customer count grows 10 times? 9. Can we export our dashboards and definitions if we switch vendors? 10. Does the vendor meet our security commitments, such as a SOC 2 report? Our SOC 2 compliance guide explains what to look for.

Where to start

1. Interview 5 customers. Ask what they currently export and what they do with it. That list is your first dashboard.
2. Define the metrics. Write clear definitions, so every customer’s numbers mean the same thing. Our guide to self-service analytics covers how shared definitions prevent conflicting numbers.
3. Prototype with 2 approaches. Try a code-library prototype and a trial of 1 or 2 embedded BI platforms. Compare effort honestly.
4. Design isolation first. Decide how tenant separation works before you build a single chart.
5. Launch to a pilot group. Watch which charts get used and which get ignored.
6. Plan the pricing. Decide whether analytics is a core feature or an upgrade before launch, not after.

For dashboard design principles that apply inside your product too, see how to build a business dashboard and how to choose the right chart.

Conclusion

Embedded analytics puts insights where users already work, inside your product or internal tools. The concept is simple; the hard parts are tenant security, consistent metrics and choosing between building and buying. Start with the questions your users actually ask, protect each customer’s data from day one and let real usage guide what you add next.

Sources

• Microsoft Learn: Power BI embedded analytics overview — embed for your customers vs your organization, licensing and capacity requirements
• Microsoft Learn: Security in Power BI embedded analytics — row-level security, object-level security and workspace-based isolation
• Tableau Embedding API v3 — embedding views in web apps
• Tableau Help: Authentication and embedding — connected apps and token-based authentication for embedded content