Is this another BI tool?

QueryPanel is infrastructure for embedding analytics and generating safe SQL—built for product surfaces, not standalone analyst workspaces.

How do you handle tenant isolation?

Tenant context is part of generation and execution—not something you toggle later in a dashboard filter.

What does implementation look like?

Attach your database with the SDK, wire auth claims, and embed UI where customers already work. Scope follows your surfaces—not building a query engine from scratch.

How do you keep AI SQL accurate as schemas evolve?

Ground generation in synced schema metadata and your knowledge base—annotations, glossaries, and examples—so retrieval and SQL stay aligned with how your data model actually works.

How are tenants counted?

A tenant represents a distinct customer or organization within your SaaS product. We count the number of unique tenants that access or generate analytics in a given billing month.

What counts as an AI chart?

An AI chart is generated whenever an end-user asks a natural language question that results in a new chart, or when an editor uses AI to create or modify a dashboard block.

What happens when limits are exceeded?

You can purchase overage credits for AI charts. For tenant limits, we'll notify you when you approach your plan's limit and help you upgrade to the next tier. We never cut off access for your end-users without warning.

How are AI models selected?

We support OpenAI, Gemini, Anthropic, and even OpenRouter. Bring-your-own-key is available from Starter and above (including Growth and Scale). Custom plans also support bring-your-own-model setups. If you want a specific provider or model path, ping us at csaba.ivancza@querypanel.io.

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.

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

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.

Still evaluating Looker and QueryPanel?

Start on the free tier, embed one dashboard, and compare implementation time against your current shortlist.

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