Performance Test All Citrix Virtualized Applications & Desktops

Scale Virtual Computing Fast

Whether you’re an automotive company designing the next-gen car, a furniture e-tailer supplying consumers with a 3D kitchen planning tool, or a manufacturer creating market-changing aviation mobility structures in the cloud, relying on Citrix to perform while thousands of users access the same virtualized application or desktop, concurrently, is critical to business success.

Ensure Business Outcomes 
Top Reasons to Performance Test Citrix

Upgrade, not Degrade

Ensure new versions of Citrix meet virtual app or desktop performance benchmarks

Access for All

Guarantee fast end user access to virtual apps and desktops at scale

Issue Isolation

Pinpoint performance problems and discern infrastructure issues

NeoLoad Rapid Test Design:
One and Done.

Visual test design for a visual test and visual outcome. With NeoLoad’s GUI test design tool you can see the test as you design it. With other code-based only tools, it takes multiple times before a test is designed correctly. In NeoLoad you can see the XY coordinates of a window, relative to other windows and screens as you build the test, so that by the time the test is complete, you know it’s correct and ready to run load against it. No need to re-script tests over and over.

How NeoLoad Helps Citrix Perform

Agentless Load Generators. Less Steps, Quicker Testing

Fewer Functions to Maintain, Same Strength

Optical Character Recognition, Test Helpers

Run Citrix tests at load without requiring agents to be installed on load testing infrastructure and have one less item to run by the IT team before you can be productive. What takes 40+ functions in legacy testing tools, is defined in 13 simple functions. The set of Citrix actions is compact and consistent for a better readability and easier to maintain scripts. Design tests faster with live detection of text and more reliable text recognition at runtime with modern and configurable Optical Character Recognition (OCR) engine based on neural networks.