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 Sisense

Enterprise embedded analytics and OEM-style programs.

Sisense is a long-standing analytics platform with strong enterprise traction. Teams often evaluate it when they need a full embedded BI stack, semantic modeling, and services-led rollout for complex customer bases.

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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	Sisense	QueryPanel
In-app experience for end users	Embedded programs often lean on vendor-hosted surfaces or iframe-style modules, depending on product packaging and deployment.	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 interface	Dashboards and modeling workflows aimed at analysts and BI teams.	Product teams lead with `@querypanel/react-sdk` for customer UI; `@querypanel/node-sdk` on your backend for signing, schema sync, and `ask()` from API routes.
Trainable knowledge & steering	Correctness is anchored in governed metrics and BI modeling; ad-hoc SQL exemplars and column-level glossaries are usually separate analytics workflows from product code.	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).
AI / natural language	Varies by product line; often centered on governed exploration inside the BI suite.	Natural language to SQL is a first-class path, designed for customer questions inside your app.
Multi-tenant SaaS fit	Achievable with modeling and services; complexity scales with program maturity.	Tenant context and tenant-scoped SQL generation are core to the embedded workflow.
Credentials & data plane	Depends on deployment; commonly involves moving data through the vendor-controlled path you choose.	You execute SQL in your environment; QueryPanel focuses on generation and UX, not holding database credentials.
Time-to-first embed	Often measured in quarters when programs include modeling, rollout, and customer onboarding.	Designed for days-to-weeks when your schema and tenant model are already defined in Postgres or similar.

When Sisense is the better fit

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

You need a broad enterprise BI footprint with established procurement paths.
You are standardizing on a vendor that already serves centralized analytics and embedded programs together.
You have dedicated data engineering and BI resources to own modeling, rollout, and customer-specific tailoring.

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 analytics to feel like the rest of your product: React components in your shell and design system—not an iframe-hosted analytics app with its own chrome and awkward sizing.
You want `@querypanel/react-sdk` for the embedded UI and `@querypanel/node-sdk` on your API for JWT signing, schema sync, and tenant-scoped `ask()`—a small, composable stack.
You need to ship customer-facing analytics quickly without standing up a classic BI program or months of custom chart UI.
You want AI-native generation (question → SQL → chart) as the default workflow, not a bolt-on.

Still evaluating Sisense and QueryPanel?

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

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