AWS Native Analytics

The Right Architecture Is A LOT
More Important Than You Might Think

A Unique Deployment Model

Only Qrvey gives you the best of the cloud, deployed right into your existing AWS account

The Best of the Cloud

The Best of Deployed

A Deeper Look At Why
Qrvey’s Architecture Matters

Qrvey is a 100% cloud-native and serverless analytics platform that’s built on AWS but deployed right to you. Here’s why that matters and how Qrvey helps you achieve your embedded analytics goals using the power of over two dozen AWS services.

Secure Deployments in Your Environment

Using AWS CloudFormation, Qrvey automatically coordinates the deployment of dozens of AWS services and builds a single AWS analytics solution that runs inside your existing AWS environment.

KEY BENEFIT: Qrvey simplifies administration and centralizes management functions. Your data never leaves your own environment, streamlining data security and compliance requirements.

Key AWS Services:

Maximum Flexibility with 100% Auto-Scaling Services

With a unique microservices-based framework, Qrvey has engineered an AWS analytics platform that is always fast, available and resilient. Ensure your users never miss a beat and insights flow at the speed of your data.

KEY BENEFIT: As your needs data and analytics needs expand, Qrvey automatically scales up to meet demand while always minimizing costs with a 100% serverless architecture.

Key AWS Services:

The Lowest Cost with Serverless Technology

Using AWS Lamba, the Qrvey platform only uses resources when your users need them, allowing the system to scale instantly to changing conditions. Stop paying for servers that sit idle the majority of the time. Maximize your analytics investment with Qrvey.

KEY BENEFIT: Qrvey dramatically reduces your total cost of ownership compared to legacy, server-based analytics solutions.

Key AWS Services:

Zero-Downtime Version Updates

Qrvey takes advantage of AWS Code Deploy to make updating your analytics applications a seamless experience. Update on your schedule with zero downtime on your production instances.

KEY BENEFIT: Since Qrvey is SDLC compliant, every update can be thoroughly tested in your development and staging environments. Updating with Qrvey is always a zero-hassle, zero-downtime process.

Key AWS Services:

SDLC Compliant For Easy Management

DevOps teams love Qrvey because we’re fully SDLC with development, staging and production environments that make installing, developing, testing and deploying analytics easier and faster than ever.

KEY BENEFIT: Create multiple instances in your production, staging, development and disaster recovery environments while supporting embedded analytics use cases for all your users.

Key AWS Services:

The Cost Savings Speak For Themselves!

Stop wasting money with traditional servers and infrastructure that sit idle.

Typical AWS Cost Savings vs Other BI Vendors


Save up to


Compared to a 
recommended load 
balanced deployment


Save up to


Compared to a Sisense
recommended multi-
node deployment

Typical 3-Year Infrastructure Savings

$100,000 - $200,000

for a single node or load-balanced deployment based on Tableau minimum recommended specs plus 100GB of AWS Redshift storage

Learn more about how Qrvey provides an alternative to Tableau

Typical 3-Year Infrastructure Savings

$90,000 - $130,000

for a multi-node, load-balanced deployment based on Sisense recommended specs plus 100GB of AWS Redshift storage

Learn more about how Qrvey provides an alternative to Sisense

*All comparisons updated October 2020. Comparison's assume AWS Redshift is the primary data storage method. Percentages may change depending on storage method.

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Explore the possibilities that a modern, embedded analytics platform like Qrvey can offer!

Qrvey has Revolutionized Embedded Analytics

  • A serverless, auto-scaling, cloud-native architecture
  • Automation, workflows, AI and machine learning
  • All data accepted -- even images and video!
  • Data collection and transformation built-in
  • Business-friendly licensing optimized for embedding