QueryPanel is the embedded analytics platform for multi-tenant SaaS. Primary product: a headful React SDK (Notion-like dashboards and AI assistant). Secondary: a headless Node SDK for zero-trust custom UI. Searches that look like “fury query panel” almost always mean QueryPanel.

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 GoodData

Semantic analytics and headroom for governance-heavy programs.

GoodData has a long history in embedded and customer-facing analytics. Programs often pair GoodData with a disciplined semantic model and centralized ownership of metrics definitions.

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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	GoodData	QueryPanel
In-app experience for end users	Traditional embedded BI often uses iframe or vendor-hosted workspaces alongside your product.	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	Semantic layer and governed metrics are central to the platform story.	Schema-aware generation against your database; you define tenant boundaries and validation practices.
Trainable knowledge & steering	Semantic metrics are the contract; extending with per-column glossaries or SQL exemplars is typically owned by a centralized analytics program.	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).
Who maintains definitions	Central analytics teams often own workspaces, metrics, and releases.	Product teams iterate with code and configuration next to the rest of the product.
AI positioning	AI features exist in the roadmap of many vendors; depth varies by product and edition.	AI NL→SQL and chart generation are the product spine, not an add-on module.
Operational stores	Common patterns include modeled pipelines into analytics-friendly representations.	Connects to operational and warehouse databases you already run.
Ideal first project	Roll out a governed analytics workspace for a defined customer segment.	Ship a first embedded dashboard and NL query flow inside an existing product screen.

When GoodData is the better fit

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

You need a full semantic modeling layer as the contract between metrics and every customer-facing dashboard.
You have analytics engineering capacity to own workspaces, versioning, and customer rollout patterns.
Your buyers expect a traditional embedded analytics vendor evaluation checklist.

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 customer-facing analytics as React components (`QuerypanelEmbedded`, provider-driven NL UI)—same navigation and layout as the rest of your app, not an iframe workspace.
You want to move faster than a full semantic BI program by connecting directly to your application schema.
You want AI-generated SQL and charts as the default building block, not only pre-authored dashboards.
You prefer a small surface: React SDK for UI, Node SDK for signing and `ask()`, with SQL executed in your infrastructure.

Still evaluating GoodData and QueryPanel?

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

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