●New: AI insights grounded on your semantic layer●Featured in G2 embedded analytics reviews●Flat-rate pricing for unlimited tenants●Webinar — Scaling multi-tenant to ten thousand customers●New: AI insights grounded on your semantic layer●Featured in G2 embedded analytics reviews●Flat-rate pricing for unlimited tenants●Webinar — Scaling multi-tenant to ten thousand customers
Cloud-Native Embedded Analytics
Deploy customer-facing analytics in your cloud, on your terms.
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.
Cloud-Native Embedded Analytics inside leading SaaS products
01 The ProblemWhere general-purpose BI hits its ceiling
The problem isn't embedding analytics. It's operating analytics at SaaS scale.
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.
That means solving for
Multi-tenant data security and governance
Cloud deployment and data residency requirements
Performance across growing customer usage
Dev, test, staging, and production environments
Secure self-service analytics inside your application
Analytics infrastructure that can support AI-driven experiences over time
General-purpose BI tools were not designed for this. Qrvey was.
02 The SolutionThe infrastructure layer SaaS teams keep building themselves
Meet Qrvey's cloud-native analytics architecture.
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.
01 / Cloud-deployed, container-based
Built for SaaS architecture.
Deploy analytics in your cloud environment with the control, flexibility, and performance SaaS teams require.
02 / Tenants, roles, scopes, data models
Built for multi-tenant scale.
Support secure analytics across customers, tenants, roles, and data models — without rebuilding the foundation yourself.
03 / Sidekick · Agents · MCP Server
Built for the AI era.
Qrvey's MCP Server connects AI to the same datasets, dashboards, metadata, and tenant permissions that power the rest of your analytics experience.
03 How It WorksBuilt to fit your release process, not interrupt it
A path that fits the way your team already operates.
Qrvey slots into your cloud, your data, your tenant model, and your release process — no parallel ops, no second deployment universe.
Step 01
Deploy Qrvey in your cloud.
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.
Image PlaceholderQrvey deployed inside your cloud environment — AWS, Azure, or GCP
Step 02
Connect your application data.
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.
Image PlaceholderConnectors to warehouses, databases, APIs — Qrvey Pro and Qrvey Ultra
Step 03
Apply tenant-aware security.
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.
Image PlaceholderTenant-aware permissions enforced across dashboards, embeds, and AI
Step 04
Embed analytics into your product.
Embed dashboards, reports, self-service builders, and analytics workflows directly into your SaaS application using modern web components and APIs.
Image PlaceholderJavaScript components & APIs embedded inside your product UI
Step 05
Scale analytics across environments.
Manage analytics content across dev, test, staging, and production environments. Promote updates with confidence and keep analytics aligned with your product release process.
Image PlaceholderPromote analytics content through dev, test, staging, and production
04 Key CapabilitiesWhat it takes to run analytics like part of your product
Everything you need to run embedded analytics in your cloud.
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.
01
Cloud-native deployment
Deploy Qrvey directly into your cloud environment using container-based infrastructure built for modern SaaS applications.
Keep analytics close to your application and data
Support cloud and data residency requirements
Scale analytics services as usage grows
Maintain control over infrastructure, deployment timing, and updates
OutcomeAnalytics runs where your application runs — no separate vendor environment for your team to manage or your data to leave.
02
Multi-tenant security & governance
Qrvey is designed for customer-facing SaaS analytics, where every user interaction must respect tenant boundaries, permissions, and data access rules.
Support co-mingled or segregated tenant data models
Apply row-level, column-level, and schema-level controls
Align analytics access with your application's security model
Enable secure self-service analytics across customers
OutcomeEvery customer interaction stays scoped to their tenant — enforced by the platform, not stitched together by your team.
03
Built-in analytics data management
Qrvey helps SaaS teams prepare, transform, and optimize data for customer-facing analytics.
Connect to databases, warehouses, APIs, and semi-structured data
Use built-in transformation capabilities when needed
Reduce dependency on external semantic-layer-only approaches
Improve performance and cost efficiency for embedded analytics workloads
OutcomeYour team prepares and serves customer-facing data from one platform — instead of stitching together a separate pipeline underneath.
04
Modern embedding, no iframes
Qrvey embeds through modern web components and APIs, giving your team more control over the user experience than traditional iframe-based BI embedding.
Deliver analytics that feels native to your application
Customize branding, layout, and interaction patterns
Embed dashboards, builders, reports, and workflows
Automate provisioning and management through APIs
OutcomeAnalytics looks, feels, and behaves like the rest of your product — not a vendor's UI dropped inside an iframe.
05
Environment & release management
SaaS teams need analytics to move with the product. Qrvey supports structured deployment workflows so analytics content can be managed across environments.
Install Qrvey in development, QA, staging, and production environments
Promote analytics content across environments
Support global and multi-region deployment models
Reduce risk when releasing new analytics capabilities
OutcomeAnalytics moves through your release process the same way the rest of your product does — no manual rebuilds between environments.
06
AI-ready analytics architecture
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.
Embed AI directly into analytics workflows
Use built-in or custom agents for defined analytical tasks
Connect AI to governed analytics assets through the Qrvey MCP Server
Keep AI interactions tenant-aware and aligned with your application context
OutcomeAI-generated outputs stay aligned with how analytics is defined, governed, and delivered inside your product.
05 Why QrveyInternal reporting vs. customer-facing analytics
Internal reporting and customer-facing analytics are not the same job.
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.
Traditional BI approach
Qrvey cloud-native architecture
Hosted outside your product architecture
Deployed in your cloud environment
Built primarily for internal business users
Built for customer-facing SaaS analytics
Iframe-based embedding
Modern web components and APIs
Limited control over multi-tenant deployment
Tenant-aware security and governance
Separate tools for data prep, embedding, and deployment
Integrated analytics infrastructure
AI disconnected from governed analytics context
AI connected to datasets, metadata, dashboards, and tenant permissions
— Worth knowing —
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.
06 EcosystemNo rip and replace required
Works with your cloud, data, and product architecture.
Qrvey is designed to fit into the way modern SaaS companies build and operate.
Deploy in your cloud environment
Run Qrvey inside the AWS, Azure, or GCP account your product already operates in.
Connect to your application data
Wire up databases, warehouses, APIs, and event sources without rebuilding your data stack.
Support multi-region & multi-environment models
Run dev, QA, staging, production, and regional production from the same platform.
Embed analytics into your product experience
JavaScript components and APIs let analytics behave like part of your product.
Use APIs to automate provisioning & management
Tenant provisioning, content promotion, and lifecycle workflows handled programmatically.
Extend analytics workflows with governed AI
Sidekick, AI Agents, and the MCP Server connect AI to your governed analytics context.
— Talk to an analytics expert, not a BDR —
Ready to ship analytics the same way you ship the rest of your product?
See how Qrvey helps SaaS teams deliver secure, scalable, AI-ready analytics inside their products.