Simple yet realistic load testing

We've taken the complexity out of load testing and have features that will help even the novice tester know their real performance limits.

Realistic simulated users

We simulate traffic exactly how it would happen in real life. No hidden caching or romanticized user behavior.

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Simultaneous multi-geo load generation

In a single test, we can generate load from up to 10 different locations simultaneously. We can add even more locations upon request.

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Automatic and advanced scripting


Script user scenarios in Lua, using our IDE with code completion, JSON and XML parsing, data parameterization and more. Or analyze a web page and generate a script automatically - no programming required.

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Easy-to-use scenario recorders

Record an HTTP session using our proxy recorder or our Chrome Extension and let the simulated users perform the same actions during the test.

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Server metrics

Install our Server Metric Agents on your machines and collect backend information from the system being tested, such as:

  • CPU usage
  • Memory usage
  • Disk I/O
  • Network I/O

Plus any custom metrics available via Nagios plugins

https://loadimpact-cms.s3.amazonaws.com/cmsapp-salesfeature/screenshot-server-metrics.png

Test scheduling and automation


Configure your load tests to run in the middle of the night or once per week - you don't have to be around at all! You can also include Load Impact as part of your Continuous Delivery process with the use of our Jenkins and TeamCity plugins, as well as our open API and various SDKs.

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Multiple data stores


Make your tests data-driven. Simulate real users by including several sources of parameterized data in your scenario scripts. Simply upload CSV files containing the data you wish to use - such as login credentials, product IDs, URLs, etc. - and associate it with the desired script. 

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Performance trending


Plot a single, high-level performance metric collected over multiple test runs to locate patterns of performance degradation or improvement, and more easily validate the performance impact of code and infrastructure changes over time.

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