Qrvey gives SaaS product and engineering teams the cloud-native analytics infrastructure they need to deliver secure, scalable, self-service analytics inside their applications — without building and maintaining it all themselves.
Deploy Qrvey alongside your application in your cloud environment, keep control over your data and infrastructure, and give customers the analytics experience they expect.
Many analytics tools can display charts inside an application. The harder challenge is everything around those charts. For SaaS teams, embedded analytics has to fit into your architecture, your release cycles, your security model, your customer environments, and your long-term product roadmap.
General-purpose BI tools were not designed for this. Qrvey was.
Qrvey is an AI-native embedded analytics platform built for SaaS companies that need more than dashboards. It gives your team the infrastructure layer required to deliver analytics inside your product — securely, efficiently, and at scale.
Deploy analytics directly into your cloud environment, connect to your data sources, manage multi-tenant access, embed self-service experiences, and support AI-powered analytics workflows — all from the same governed architecture.
Deploy analytics in your cloud environment with the control, flexibility, and performance SaaS teams require.
Support secure analytics across customers, tenants, roles, and data models — without rebuilding the foundation yourself.
Qrvey's MCP Server connects AI to the same datasets, dashboards, metadata, and tenant permissions that power the rest of your analytics experience.
Qrvey slots into your cloud, your data, your tenant model, and your release process — no parallel ops, no second deployment universe.
Qrvey is deployed directly into your cloud environment using container-based architecture. This gives your team control over where analytics runs, where data lives, and how the platform aligns with your SaaS infrastructure.
Qrvey connects to your existing databases, warehouses, APIs, and data sources. Use Qrvey Pro when you already have an analytics-ready database, or Qrvey Ultra when you need a full-stack analytics layer with built-in data management and transformation.
Qrvey aligns analytics access with your application's roles, permissions, and tenant structure. This keeps customer-facing analytics governed — without forcing your team to duplicate security logic across separate systems.
Embed dashboards, reports, self-service builders, and analytics workflows directly into your SaaS application using modern web components and APIs.
Manage analytics content across dev, test, staging, and production environments. Promote updates with confidence and keep analytics aligned with your product release process.
Cloud-native deployment, multi-tenant security, built-in data management, modern embedding, environment-aware releases, and an AI-ready architecture — working together so the analytics layer of your product doesn't fight the rest of your stack.
Deploy Qrvey directly into your cloud environment using container-based infrastructure built for modern SaaS applications.
Qrvey is designed for customer-facing SaaS analytics, where every user interaction must respect tenant boundaries, permissions, and data access rules.
Qrvey helps SaaS teams prepare, transform, and optimize data for customer-facing analytics.
Qrvey embeds through modern web components and APIs, giving your team more control over the user experience than traditional iframe-based BI embedding.
SaaS teams need analytics to move with the product. Qrvey supports structured deployment workflows so analytics content can be managed across environments.
Qrvey's AI capabilities are built into the analytics environment, not bolted on as a separate experience. Qrvey Sidekick provides the conversational interface for embedded AI, while AI Agents deliver defined analytical capabilities inside customer-facing workflows. These agents are powered by the Qrvey MCP Server, which connects AI to datasets, dashboards, metadata, and tenant-specific permissions.
Eight guiding principles, four critical pillars, and twelve vendor questions for evaluating analytics platforms.
Read the guide →The architectural decisions that make or break customer-facing analytics on a shared data warehouse.
Read the guide →A practical checklist for SaaS leaders evaluating embedded analytics platforms, drawn from the full evaluation guide.
Read the article →Traditional BI tools were built for internal reporting. Qrvey was built for SaaS teams delivering analytics to their own customers — and the architecture choices reflect that difference end-to-end.
Most "embedded" offerings from traditional BI vendors are internal reporting tools retrofitted for customer-facing use. The multi-tenant security, deployment flexibility, and SaaS-aware governance that come standard with Qrvey become workarounds your team has to build and maintain — because the underlying product wasn't designed for SaaS architecture in the first place.
Qrvey is designed to fit into the way modern SaaS companies build and operate.
Run Qrvey inside the AWS or Azure account your product already operates in. (GCP coming soon!)
Wire up databases, warehouses, APIs, and event sources without rebuilding your data stack.
Run dev, QA, staging, production, and regional production from the same platform.
JavaScript components and APIs let analytics behave like part of your product.
Tenant provisioning, content promotion, and lifecycle workflows handled programmatically.
Sidekick, AI Agents, and the MCP Server connect AI to your governed analytics context.
See how Qrvey helps SaaS teams deliver secure, scalable, AI-ready analytics inside their products.
Runs in your cloud, not ours