Qrvey
Qrvey is an embedded analytics platform that agencies and SaaS companies white-label and embed into their products via JavaScript widgets and APIs. It ingests data from Postgres, Snowflake, S3, MongoDB, and Azure Blob into a managed multi-tenant data lake, then serves self-service dashboards, AI-powered natural language querying (via OpenAI, Claude, or Bedrock), and no-code workflow automation. The platform runs in customer cloud infrastructure, ensuring data sovereignty, and charges flat-rate pricing with no per-seat or per-tenant fees. Agencies can ship analytics features in weeks rather than months, making it viable for white-label SaaS product delivery or embedded analytics retainers to B2B SaaS clients.
Qrvey is an embedded analytics platform, integrating with Postgres, Snowflake, S3, and MongoDB. InnovaAI scores it 8.8/10 for agency resale, strong fit for agencies running 10+ client accounts under their own brand.
Agency Audit
Qrvey embeds white-labeled analytics dashboards, self-service reporting, and AI-powered natural language querying into SaaS products via JavaScript widgets and APIs, with multi-tenant data isolation and flat-rate pricing. It's built for agencies reselling analytics to B2B SaaS clients or building white-label SaaS products themselves. The platform runs in customer cloud infrastructure (Postgres, Snowflake, S3, MongoDB, Azure Blob) and handles data ingestion through a managed data lake. Agencies can ship analytics features in weeks rather than months, making it viable for retainer-based client delivery. Pricing is custom enterprise-only, so margin structure depends on negotiated terms with Qrvey.
8.8/10
Depends on volume
2d 1-2 days
- Your clients are B2B SaaS companies that need embedded analytics for their own end-users and you want to white-label the entire analytics UI to match their product branding.
- You serve clients with multi-tenant data requirements and need purpose-built security isolation across customer accounts within a single analytics instance.
- Your clients already run Postgres, Snowflake, or MongoDB and want to avoid moving data to a third-party data warehouse for analytics.
- Your clients are SMBs or agencies that need simple client reporting dashboards; Qrvey is architected for SaaS product teams and carries enterprise-grade complexity.
- You need transparent, predictable per-client pricing to build fixed retainer fees; custom enterprise pricing makes it hard to forecast margins.
- Your clients cannot or will not host analytics infrastructure in their own cloud accounts; Qrvey does not offer a managed SaaS option.
Profit Path
Contact for quote
$1K–$3K/project
Setup Fee
Planning benchmark at United States price levels. Not a measured market survey.
Platform Features
Core capabilities of Qrvey
White-label embedded dashboards
Embed self-service analytics dashboards into your SaaS product or client portal via JavaScript widgets and APIs, with full UI branding customization. Agencies can ship client-facing analytics without building a reporting layer from scratch.
Multi-tenant data isolation
Purpose-built security at all layers ensures each client's data is isolated within a shared analytics instance. Agencies can serve multiple end-clients from one Qrvey deployment without cross-tenant data leakage.
AI-powered natural language querying
End-users ask questions of their data in plain English; Qrvey translates queries to SQL using OpenAI, Claude, or Bedrock. Reduces training burden on client teams and accelerates insight discovery.
No-code workflow automation
Trigger actions (alerts, notifications, data exports) based on analytics thresholds or events without writing code. Agencies can build client-specific automation workflows and embed them as part of retainer deliverables.
Multi-source data ingestion
Ingest data from Postgres, Snowflake, S3, MongoDB, and Azure Blob into a managed data lake. Agencies can consolidate client data from multiple systems into a single analytics source.
Customer-hosted cloud infrastructure
Qrvey runs in the customer's own cloud account, ensuring data sovereignty and compliance with client data residency requirements. Eliminates third-party data movement for regulated industries.
What Makes Qrvey Different
Unique advantages vs similar tools in this niche
Purpose-built multi-tenant security at every layer
vs Generic BI tools that require manual VIEW-based securityQrvey bakes in row, column, object, asset, and feature security natively, eliminating the need for custom maintenance.
AI that reads your semantic model, not guesses
vs GPT wrappers that cannot see your schemaQrvey's AI uses the semantic model to provide accurate, grounded answers and anomaly detection.
Flat-rate pricing with no per-seat fees
vs Per-seat vendors like Tableau or LookerTenant 10,000 costs the same as tenant 10, enabling predictable scaling.
Latest Updates
Recent releases and improvements for Qrvey
Qrvey 9.4 Brings AI Agents to Embedded Analytics for SaaS Products
NewNew release introducing AI Agents to Qrvey's embedded analytics platform for SaaS products.
Value Equation
Outcome-likelihood-time-effort assessment for Qrvey
Value math requires real pricing
The Value Equation (dream outcome × likelihood ÷ time × effort) feeds directly into ROI math. Qrvey has no published pricing, so we hold this section until real numbers are available.
Contact QrveyPricing
Qrvey platform cost to your agency
Custom Pricing: Contact Vendor
Qrvey does not publish fixed pricing. Costs are determined based on your organization's size, feature requirements, and usage volume.
Agencies should request a demo or partner pricing directly from the vendor. Many enterprise platforms offer agency/reseller partner programs with volume discounts.
View Qrvey pricing pageWhite-Label Capabilities
- Custom domain & branding under your agency name
- Client management portal with performance analytics
- Multi-account management for agency operations
- White-label everything to match your UI and branding
Market Intelligence
Offer + scale economics for Qrvey
Offer economics require real pricing
Offer economics, scale projections, and margin potential all depend on Qrvey's actual platform cost. Once pricing is published or shared with your agency, we'll compute the full breakdown here.
Contact QrveyInvestment Decision Framework
Strategic vetting analysis for Qrvey
Strong Buy
Strong agency fit, low resell friction
Buy If
5Your clients are B2B SaaS companies that need embedded analytics for their own end-users and you want to white-label the entire analytics UI to match their product branding.
You serve clients with multi-tenant data requirements and need purpose-built security isolation across customer accounts within a single analytics instance.
Your clients already run Postgres, Snowflake, or MongoDB and want to avoid moving data to a third-party data warehouse for analytics.
You want to offer AI-powered natural language querying on client data (OpenAI, Claude, or Bedrock) without building a semantic layer yourself.
You need no-code workflow automation triggered by analytics insights and want to embed that capability into your client deliverables.
Skip If
5Your clients are SMBs or agencies that need simple client reporting dashboards; Qrvey is architected for SaaS product teams and carries enterprise-grade complexity.
You need transparent, predictable per-client pricing to build fixed retainer fees; custom enterprise pricing makes it hard to forecast margins.
Your clients cannot or will not host analytics infrastructure in their own cloud accounts; Qrvey does not offer a managed SaaS option.
You require HIPAA compliance or specific regulatory certifications beyond SOC2; the scraped content does not mention healthcare or regulated-industry compliance.
You need immediate time-to-value for clients; setup requires data ingestion configuration, semantic model definition, and dashboard design before end-users see value.
Bottom Line
Qrvey embeds white-labeled analytics dashboards, self-service reporting, and AI-powered natural language querying into SaaS products via JavaScript widgets and APIs, with multi-tenant data isolation and flat-rate pricing. It's built for agencies reselling analytics to B2B SaaS clients or building white-label SaaS products themselves. The platform runs in customer cloud infrastructure (Postgres, Snowflake, S3, MongoDB, Azure Blob) and handles data ingestion through a managed data lake. Agencies can ship analytics features in weeks rather than months, making it viable for retainer-based client delivery. Pricing is custom enterprise-only, so margin structure depends on negotiated terms with Qrvey.
Reality Check
Qrvey requires customers to host data in their own cloud infrastructure, creating an operational dependency on client cloud accounts and compliance posture. Pricing is custom enterprise-only with no published per-seat or per-tenant rates, making it difficult to forecast client MRR or build predictable retainer margins without direct sales conversations.
Moderate effort: standard configuration with some customization needed
Academy for Qrvey
Work through it in order: the course for this service first, then the modules behind it.
No Academy modules are published for this service yet. Browse the full Academy
Core concepts
The mental model you need to price and scope the work.
- Template Debt RatioConcept
Template Debt Ratio is the share of an embedded analytics deployment that is bespoke client work rather than reused configuration. Every hour spent on one client's custom chart logic, tenant rules, or branding is an hour that cannot be resold, so the ratio predicts whether an embedded analytics retainer compounds or quietly becomes a services project. Agencies win this category when the first two or three builds are treated as template construction and later clients inherit the same connector, security, and layout patterns. Luzmo's 40+ native connectors and multi-tenant security model exist precisely so the reusable layer absorbs most of the work, while Reveal's SDK-layer integration inherits the host application's design system, which removes a common source of one-off styling labor. The discipline is refusing scope that cannot be templated. Forrester's finding that private AI deployments outperform shared public tools for B2B marketing makes the same point about differentiation: reusable, owned infrastructure beats bespoke work that any competitor can replicate.
- The Stickiness PremiumConcept
The Stickiness Premium is the extra retainer value an agency captures when analytics live inside the client's own product rather than in a separate reporting portal. Once a client's end users log in daily to dashboards the agency built, ripping out the agency means ripping out a customer-facing feature, so churn risk drops and renewal conversations shift from cost to continuity. The premium is real but conditional: it only holds when the integration is templated. A white-label platform such as Luzmo supplies 40+ connectors and multi-tenant security, which lets an agency reuse one dashboard architecture across accounts instead of rebuilding per client. Reveal takes the opposite route, integrating at the SDK layer so dashboards inherit the host app's design system, which suits clients with strict brand systems but raises per-account build effort. The framework's test: does the second client cost less to onboard than the first? If not, the premium is being paid for in delivery hours.
- The Customization Tax CurveConcept
The Customization Tax Curve describes how every unbudgeted customization request on an embedded analytics deployment compounds against retainer margin. The first bespoke chart costs a few hours; the tenth costs a redesign, because each one forks the template and multiplies maintenance across every client instance. Agencies that price embedded analytics as a fixed monthly line item absorb this tax silently until the account turns unprofitable. The framework asks one question before any scope change: does this customization get templated for reuse, or does it stay a one-off? Luzmo's no-code builder and 40+ connectors keep the baseline cheap, while Reveal's SDK-layer integration inherits the host design system so branding requests do not become custom work. The discipline is treating every client ask as either a template candidate or a billable change order, never as free goodwill.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- Embedded Analytics Rule: Template the Dashboard Before You Sell the RetainerEvaluation Rule
Price embedded analytics as a templated, recurring line item only after one dashboard pattern has been built and reused across at least two client accounts.
- When Client Data Lives in Three Systems, Embed the Join Before the ChartEvaluation Rule
Consolidate and model the client's data sources into one governed layer before embedding a single dashboard, because an embedded chart that contradicts the client's existing reports destroys trust faster than no chart at all.
- The Customization Creep Trap: Why Embedded Analytics Retainers Lose Margin After LaunchFailure Pattern
- The Empty Dashboard Trap: Why Embedded Analytics Stalls at Client AdoptionFailure Pattern
8 modules selected for Qrvey
Frequently Asked Questions
Answers about pricing, setup, implementation, and more
Qrvey is an embedded analytics platform that SaaS companies and agencies white-label and embed into their products. It provides data ingestion from Postgres, Snowflake, S3, MongoDB, and Azure Blob; multi-tenant data lake management; self-service dashboard creation; AI-powered natural language querying via OpenAI, Claude, or Bedrock; and no-code workflow automation. The entire platform is white-labelable and runs in the customer's cloud infrastructure.
Qrvey offers two enterprise plans with custom pricing: Qrvey Pro and Qrvey Ultra. Both require contacting sales for a quote. Qrvey Pro includes self-service embedded analytics, pixel-perfect reporting, AI-driven insights, no-code workflow automation, and multi-tenant security. Qrvey Ultra adds a data transformation layer and built-in data engine. Both plans feature flat-rate pricing with no per-seat or per-tenant fees.
Yes. Qrvey is fully white-labelable via JavaScript widgets and APIs, allowing you to embed analytics dashboards that match your client's product branding and user experience. The platform supports custom domain configuration and branded reporting, so client end-users see your client's branding, not Qrvey's.
Yes. Qrvey natively supports both Postgres and Snowflake as data sources for ingestion into its managed data lake. It also integrates with S3, MongoDB, and Azure Blob. Data is ingested and consolidated into a single analytics instance for multi-tenant querying and reporting.
Qrvey states it can ship analytics features in weeks rather than months. Setup time depends on data source complexity, semantic model definition, and dashboard design. Once the parent agency account is configured, onboarding additional client sub-accounts involves connecting their data sources and defining their analytics schemas.
Qrvey is designed for SaaS product teams and B2B SaaS companies with multi-tenant customers. It's a strong fit for agencies building white-label SaaS products or delivering embedded analytics to SaaS clients who need to offer reporting to their own end-users. It's less suitable for SMBs or agencies seeking simple client dashboards.
Yes. Qrvey's core architecture is multi-tenant, with purpose-built security isolation at all layers. You can serve multiple client accounts from a single Qrvey instance, and each client's data remains isolated. This makes it cost-effective for agencies managing many client analytics deployments under flat-rate pricing.
The scraped content does not specify data retention or export policies upon cancellation. You should confirm with Qrvey sales whether client data can be exported to customer cloud accounts and what the data retention window is after subscription termination.