Tableau Alternative

Qrvey vs Tableau

There’s Only One Winner for
Embedded Analytics

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Companies Choose Qrvey Over Tableau for Embedded Analytics Solutions Because Qrvey is
Purpose Built for SaaS Applications

Decrease Churn

Your users will love you for adding self-service analytics and automation. 

Increase Productivity

Give you developers the tools they need to build world-class analytics.

Innovate

Build workflows and data pipelines to automate data processes and create modern analytics applications that can be embedded.

Developer-Friendly

Qrvey offers easy-to-use APIs and pre-built Javascript widgets your developers will love

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Learn why Tableau just wasn’t built for embedded analytics

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Comparison Overview

Tableau

Tableau is a great choice if your needs are limited to sharing pre-designed visualizations, reports and dashboards to internal stakeholders. If distribution, scalability and self-service are not important considerations for your use case, you can’t go wrong with Tableau.

Core Strengths

Visualizations
Read-Only Reporting
Internal Use-Case

Conclusion:

Suitable choice for internal applications/use cases

Qrvey

Qrvey was built from the ground up with the modern architecture needed to deliver the performance required by today’s demanding SaaS analytics projects. Qrvey can ingest any data, not just structured data, activating the remaining 70% of dark data that gets missed by legacy tools.

Core Strengths

Embedding/White-Label
Scalability/Performance
Pricing
Multi-Tenancy/Security

Conclusion:

Best choice for SaaS applications and distributed use cases

FEATURE TABLEAU QRVEY

100% Cloud-Native

All-in-One Platform

Dashboards and Visualizations

Unlimited Users

SaaS-Friendly Pricing

Fully Embeddable

White Label
Partial Implementation Full Implementation

Ready to Learn More?

Request a demo with one of our embedded analytics experts today

Download our FREE Comparison Guide

Learn why Tableau just wasn't built for embedded analytics.

What's in the Guide:
Embedding in multi-tenant environments
Limitations of legacy architectures
How to keep your DevOps team happy
The true costs of embedded analytics
Why data automation matters