Compare the Top Load Testing Tools as of September 2025

What are Load Testing Tools?

Load testing tools are used to test and verify the quality and performance of an application under workload in order to get rid of problems. Compare and read user reviews of the best Load Testing tools currently available using the table below. This list is updated regularly.

  • 1
    Appvance

    Appvance

    Appvance.ai

    Appvance IQ (AIQ) delivers transformational productivity gains and lower costs in both test creation and execution. For test creation, it offers both AI-driven (fully machine-generated tests) and also 3rd-generation, codeless scripting. It then executes those scripts through data-driven functional, performance, app-pen and API testing — for both web and mobile apps. AIQ’s self-healing technology gives you complete code coverage with just 10% the effort of traditional testing systems. Most importantly, AIQ finds important bugs autonomously, with little effort. No coding, scripting, logs or recording required. AIQ is easy to integrate with your current DevOps tools and processes. Appvance IQ was developed by a pioneering team who envisioned a better way to test. Their innovative vision has been made possible by applying differentiated, patented AI methods to test creation while leveraging today’s high-availability compute resources for massive levels of parallel execution.
  • 2
    NeoLoad

    NeoLoad

    Tricentis

    Continuous performance testing software to automate API and application load testing. Design code-less performance tests for complex applications. Script performance tests <as:code /> within automated pipelines for API testing. Design, maintain and run performance tests as code and analyze results within continuous integration pipelines using pre-packaged plugins for CI/CD tools and the NeoLoad API. Create test scripts quickly for large, complex applications using a graphical user interface and skip the complexity of hand coding new and updated tests. Define SLAs based on built-in monitoring metrics. Put pressure on the app and compare SLAs to server-level statistics to determine performance. Automate pass/fail triggers based on SLAs. Contributes to root cause analysis. Update test scripts faster with automatic test script updates. Update only the part of the test that’s changed and re-use the rest for easy test maintenance.
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