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 ThoughtSpot Embedded

Search-driven analytics at scale.

Buyers shortlisting AI-powered embedded analytics often compare ThoughtSpot for search-driven governed exploration. ThoughtSpot popularized AI-assisted search over governed models; embedded scenarios often emphasize consistent answers on top of curated metrics and a strong operations story for analytics teams. QueryPanel fits when the goal is customer-facing analytics inside a multi-tenant SaaS product with a headful React workspace and optional headless zero-trust Node path.

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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	ThoughtSpot Embedded	QueryPanel
In-app experience for end users	Embedded search experiences often wrap vendor-managed UI; integration and hosting patterns vary by edition.	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.
Experience shape	Search-first exploration on curated business objects and metrics.	Customer questions on your routes, rendered by your React tree, routed to tenant-safe SQL against your operational schema.
Trainable knowledge & steering	Search quality is driven by governed business objects and metric definitions; layering ad-hoc SQL libraries next to product code is less central to the platform story.	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).
Integration owner	Analytics and data platform teams typically lead modeling and rollout.	Product engineering owns the embedded surface; data stays in your existing Postgres / warehouse.
Flexibility vs guardrails	Strong guardrails when metrics are modeled carefully upstream.	Faster iteration for product teams that accept owning validation, review, and rollout of generated SQL.
Stack fit	Best when the ThoughtSpot stack aligns with your enterprise data architecture choices.	Postgres, ClickHouse, BigQuery, and MySQL today—fits teams that want to stay close to operational stores.
Buyer motion	Often a top-down analytics platform decision with POCs across data leadership.	Bottom-up trial: add `@querypanel/react-sdk` to a route, wire `@querypanel/node-sdk` in an API handler, connect a database, ship.

When ThoughtSpot Embedded is the better fit

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

Search and drill across a wide enterprise metric layer is the primary experience you are selling.
You already invest in governed semantic layers and want AI on top of that foundation.
You need a brand-name analytics platform with a long enterprise track record.

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 embedded analytics as native React—`QuerypanelEmbedded` or composable provider pieces—so customers never deal with a boxed iframe app inside your SaaS.
You are embedding analytics into a multi-tenant SaaS product, not rolling out another enterprise BI portal.
You want developers to own the stack: React components in your app, Node SDK on your API for auth and `ask()`, not a separate BI release train.
You want lightweight onboarding: connect the database, describe tenants, ship dashboards on a normal product route.

Still evaluating ThoughtSpot Embedded and QueryPanel?

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

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