QueryPanel vs Power BI
Microsoft embedded analytics for teams already standardized on Azure and the Power Platform.
Microsoft Power BI Embedded brings Power BI dashboards and reports into applications, usually for teams already invested in Azure AD, Fabric/Power BI capacity, and Microsoft governance. It is a common Google-cited option for embedded analytics, but the customer experience often still feels like Power BI unless your product team invests heavily in embedding UX.
Comparison at a glance
This table summarizes typical positioning. Every vendor changes over time—validate details against current documentation and your security review.
| Dimension | Power BI | QueryPanel |
|---|---|---|
| In-app experience for end users | Power BI Embedded delivers Power BI reports and dashboards inside your application; the interaction model is familiar Power BI unless heavily customized. | First-class `@querypanel/react-sdk` components—`QuerypanelEmbedded` for a full dashboard, or `QueryPanelProvider` with `QueryInput` / `QueryResult` for a bespoke flow. They render in your React tree like any other product screen (layout, router, modals, tokens)—not a separate iframe "mini app" on another origin. Mint short-lived JWTs on your server; never ship your workspace private key to the browser. |
| Primary product shape | Microsoft BI platform with Desktop/authoring workflows, workspace governance, and embedded capacity for application delivery. | AI-native React workspace plus headless Node `ask()` for SaaS teams shipping customer analytics as a product feature. |
| Trainable knowledge & steering | Governance typically lives in datasets, measures, and Microsoft modeling practices maintained by BI teams. | Gold SQL queries (curated examples the model prioritizes), database annotations (business context on tables/columns, re-embedded with schema), glossary (domain terms and definitions), and tenant-level definitions (isolation field, enforcement, and per-tenant sync context so every ask() is grounded in the right customer slice—not a one-size global prompt). |
| Natural language workflow | Copilot and Power BI AI features depend on Microsoft packaging, licensing, and data readiness in the Power BI environment. | Natural language to SQL and chart generation are first-class embedded SDK and API workflows on your schema. |
| Ecosystem fit | Strongest when Azure identity, capacity, and Microsoft governance already define how analytics ships. | Stack-agnostic for Postgres, ClickHouse, BigQuery, and MySQL with JWT auth owned by your SaaS backend. |
| Multi-tenant SaaS fit | Supported through row-level security, Azure configuration, and embedding patterns that should be validated against your tenant model. | Tenant id and isolation metadata travel with the embedded JWT and generation request. |
| Best first win | Embed existing Power BI reports for Microsoft-centered customers using familiar authoring and Azure capacity. | Add a product-native AI analytics route without making Power BI capacity or PBIX authoring the first customer milestone. |
Four layers your team—and your tenants—can train for better answers
Natural language is only as good as the context the model sees. QueryPanel's knowledge system lets you steer retrieval and SQL with curated examples, business meaning on the schema, shared glossary terms, and tenant-aware definitions—so customer-facing analytics matches how your product actually defines revenue, usage, and risk.
Gold queries
Save vetted SQL for recurring questions. Gold examples are retrieved with schema context and treated as the strongest pattern signal when they match the end user's intent—so joins, filters, and metrics follow what your team already proved in production.
Database annotations
Attach free-text business meaning to tables and columns. Annotations are merged into embedded schema chunks (“Business Context”) so search and generation see how revenue, activation, or ARR are really defined in your warehouse—not only raw column names.
Glossary
Define terms customers actually say (“active seat”, “net MRR”, “expansion”). Glossary entries are embedded alongside schema so the model resolves ambiguous language the way your finance and product teams mean it.
Tenant-level definitions
Per-tenant isolation settings and tenant-scoped schema sync mean each customer’s ask() carries the right tenant id and rules—so retrieval and generated SQL respect dynamic per-tenant shape, not a single global tenant-agnostic prompt.
Manage gold SQL and glossary from the dashboard knowledge base; annotations attach business context to schema objects; tenant isolation and sync keep per-customer context aligned. See documentation for SDK routes and ingestion APIs.
When Power BI is the better fit
Honest tradeoffs help your team pick faster—and match how buyers actually decide.
- Your company already standardizes on Microsoft 365, Azure, and Power BI for internal analytics.
- Report authors are Power BI specialists and you want to reuse existing PBIX assets in an embedded capacity model.
- Procurement, compliance, and identity are simpler when everything stays inside the Microsoft stack.
- Customers or partners already expect Power BI-style reports rather than a product-native React workspace.
When QueryPanel is the better fit
Especially strong for B2B SaaS shipping customer-facing analytics on Postgres and similar databases.
- You want a trainable knowledge system—gold queries, DB annotations, glossary, and tenant-aware definitions—so NL→SQL and charts reflect your business, not generic schema-only guesses.
- You want analytics to feel native to a React SaaS product, not like a Power BI report framed inside your app.
- You want AI-native question → SQL → chart generation as the default customer workflow, independent of Microsoft Copilot packaging.
- You prefer to keep SQL execution and credentials on your infrastructure with a small Node SDK integration rather than Azure capacity planning as the first constraint.
- Your product team owns the customer analytics roadmap and does not want a second Microsoft BI release process.
Keep comparing the implementation details
Vendor fit depends on more than a feature matrix. These guides cover the security, embedding, and buying choices that usually decide a SaaS analytics rollout.
Ship the customer UI with React—not an iframe
Most teams lead with @querypanel/react-sdk: drop QuerypanelEmbedded on a normal product route, or compose QueryPanelProvider with QueryInput / QueryResult. It behaves like any other React subtree—your app shell, router, modals, and design tokens—not a separate cross-origin iframe "mini app" with its own layout chrome.
The browser talks to the QueryPanel API with a JWT you mint on your server (RS256). Never ship your workspace private key to the client.
import { QuerypanelEmbedded } from "@querypanel/react-sdk";
// Render like any other page — not an iframe. Mint tenantJwt (RS256) on your server
// with @querypanel/node-sdk; pass only the JWT to the client.
export function CustomerAnalytics({ tenantJwt }: { tenantJwt: string }) {
return (
<QuerypanelEmbedded
dashboardId="your-dashboard-id"
apiBaseUrl="https://api.querypanel.io"
jwt={tenantJwt}
allowCustomization
/>
);
}Headless Node SDK (optional, for your API)
Use @querypanel/node-sdk on your backend to attach database clients, sync schema, sign JWTs for the React embed, and call ask() from API routes when you want a fully custom pipeline. SQL still runs with your drivers. Full quickstart in documentation.
import { QueryPanelSdkAPI } from "@querypanel/node-sdk";
const qp = new QueryPanelSdkAPI(
process.env.QUERYPANEL_URL!,
process.env.PRIVATE_KEY!,
process.env.QUERYPANEL_WORKSPACE_ID!,
);
// After attachPostgres / syncSchema — tenant comes from your auth layer
const result = await qp.ask("Revenue by country last quarter?", {
tenantId: org.id,
database: "analytics",
});
// result.sql, result.params, result.rows, result.chart — you execute SQL with your driverStill evaluating Power BI and QueryPanel?
Start on the free tier, embed one dashboard, and compare implementation time against your current shortlist.