Embedded Analytics Platform: Add Live Insights to Your Product — Without Rebuilding

Ask end users to “go to the BI portal” and adoption plummets. Embed the same dashboard inside the app they already use — Salesforce, Microsoft Teams, a customer portal, an internal operations tool — and usage rises almost linearly.

That's the entire thesis of embedded analytics. An embedded analytics platform surfaces charts, dashboards, and AI-powered insights inside another application, via iframes, SDK components, or native APIs. Users never leave the tool they work in. The BI layer just… shows up where they need it.

What “embedded” actually means

Three delivery modes, in increasing order of customization:

iframes are the fastest path to “something works.” API-first gives full design control at the cost of more engineering work. SDK components are the middle ground most mid-scale teams land on.

Where embedded analytics wins

Two fundamentally different markets

Customer-facing embedded analytics

SaaS products that need to give their customers dashboards inside the product. Typical requirements:

• Multi-tenancy — each customer sees only their own data, automatically.
• White-label design — the dashboards look like your product, not the vendor's.
• Scalable per-user pricing — embedded platforms that charge per end-user don't work for 100,000-user SaaS products.

Internal embedded analytics

Enterprise teams surfacing analytics inside the tools their employees already use. Typical requirements:

• Microsoft Teams, SharePoint, Outlook, Salesforce, Dynamics 365 integration.
• SSO via Azure AD / Entra ID — no separate login.
• Row-level security that respects the user's organizational role.

The single-sign-on + RLS problem

The hardest part of embedded analytics is not the rendering. It's the security model.

When a dashboard is embedded in Salesforce, the user should see only their accounts. When it's embedded in a customer portal, the user should see only their company's data. When it's embedded in an internal ops tool, the user should see only their region's numbers.

Every one of those requires:

1. Propagating the user identity from the host app to the analytics platform.
2. Mapping that identity to row-level security rules in the semantic model.
3. Returning only permitted data — even if the user tries to inspect network calls.

Platforms that define RLS once in a semantic model handle this automatically. Platforms that expect you to write RLS per-report or per-query become maintenance hell at scale.

Evaluating embedded analytics platforms

Cost reality

Embedded analytics platforms charge in four common patterns. Each has trade-offs:

• Per-seat pricing. Works for internal tools; bankrupts customer-facing SaaS.
• Per-view / per-render. Predictable for low-volume, brutal for high-traffic embeds.
• Capacity-based. Fabric-style fixed capacity — easiest to budget for growing SaaS products.
• Data-volume-based. Scales with your data, not users — good for internal tools on large datasets.

Microsoft Fabric + Power BI Embedded

For Microsoft-native shops (which is most mid-market enterprises), Power BI Embedded is the default option. It runs on Fabric capacity, supports SSO via Entra ID, and embeds via iframe or the Power BI JavaScript SDK into Teams, SharePoint, Salesforce, Dynamics, or custom portals.

The gaps Power BI leaves for customer teams to fill:

• Semantic model — you build it.
• RLS rules — you configure them per data source.
• Pre-built industry content — none ships.

Where IntelliFabric fits

IntelliFabric is not a standalone embedded analytics vendor. It's an accelerator that ships a governed semantic model, industry KPIs, and AI decision layer on top of Microsoft Fabric and Power BI Embedded. Everything you embed inherits RLS from the semantic model, natural-language Q&A from Fabric Copilot, and pre-built industry content from IntelliFabric.

For teams that want to embed live dashboards into Microsoft Teams, SharePoint, Dynamics 365, or a custom operations portal — without spending six months building the underlying data layer — this is the fastest path.

See our self-service + embedded capabilities, or book a demo to see a dashboard embedded into Teams in real time.

Related: Real-time analytics · AI-powered analytics platforms