QueryPanel vs Looker

Governed BI and LookML modeling compared with AI-native embedded analytics.

Looker is a mature BI platform centered on governed modeling and analytics experiences across teams. QueryPanel is a better fit when the first job is embedding tenant-aware natural-language analytics into a SaaS product without building a full BI program around the customer experience.

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Comparison at a glance

This table summarizes typical positioning. Every vendor changes over time—validate details against current documentation and your security review.

Dimension	Looker	QueryPanel
In-app experience for end users	Looker supports embedded analytics from individual visualizations through dashboards and exploration experiences.	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.
Modeling approach	LookML is a core modeling layer for dimensions, measures, explores, and governed definitions.	Schema-aware generation steered by gold SQL, annotations, glossary terms, and tenant context.
Trainable knowledge & steering	Governance usually centers on LookML projects and modeled business definitions maintained by analytics 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	AI and conversational capabilities depend on the Looker product path and edition you adopt.	Natural language to SQL and chart generation are first-class API and embedded SDK workflows.
Integration owner	Often owned by analytics engineering or data teams that maintain models, explores, and governed releases.	Product engineering can embed the workspace and call `ask()` from API routes while reusing the existing application auth model.
Multi-tenant SaaS fit	Supported through modeling, permissions, and embedding configuration that should be validated against your tenant architecture.	Tenant id and isolation metadata travel with the embedded JWT and query-generation request.
Best first win	Extend a governed BI program into embedded dashboards or data apps.	Add customer-facing NL analytics to a product route without making LookML or a semantic BI rollout the first milestone.

Trainable knowledge base

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 Looker is the better fit

Honest tradeoffs help your team pick faster—and match how buyers actually decide.

You already have LookML expertise and want governed metrics across internal BI, dashboards, and embedded analytics.
Your analytics team needs a centralized semantic model as the main contract for business definitions.
You are standardizing on Google Cloud analytics workflows and want embedded analytics to follow that architecture.

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 the embedded customer surface to feel like native product UI through `@querypanel/react-sdk`, not a separate BI experience.
You want natural-language SQL and chart generation as the starting point, not a layer added after semantic modeling work.
You prefer to keep SQL execution and database credentials in your infrastructure while QueryPanel handles generation and UI.
You need a small developer-owned integration path for multi-tenant SaaS analytics.

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.

Embedded analytics tools

Compare common platform categories and buying criteria.

Tenant isolation guide

Plan the tenant boundaries behind every customer dashboard.

Native React vs iframe

Understand the customer UX and developer tradeoffs.

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.

React — QuerypanelEmbedded (tsx)

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.

Node — QueryPanelSdkAPI (typescript)

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 driver

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