Gatling is a useful tool for performance and load testing, and I found it relatively easy to work with. I like that the tests can be defined and maintained in a structured way, making it easier to reuse and update scenarios. The reporting and performance metrics are helpful for identifying potential bottlenecks and understanding how an application behaves under load. Gatling provides a clear and repeatable approach to performance testing, making it easier to analyze the results after a test run. Additionally, I appreciate how well Gatling fits into CI/CD workflows with its automation capabilities, and how it integrates with existing development and testing workflows, which makes it easier to include performance testing as part of the overall QA process. The initial setup was relatively straightforward, making it easy to get started with creating and running test scenarios.

What I liked most about Gatling is its code-based approach to performance testing. It makes tests easy to maintain, and integrate into existing CI/CD pipelines (in my case Jenkins). The DSL is clear and flexible, which allows us to create realistic load scenarios and reuse common logic efficiently. I also appreciate the detailed reports and metrics, which makes it easier to identify performance bottlenecks and understand how the system behaves under different levels of load. Overall, Gatling fits very well into modern QA and development workflows and makes performance testing feel much more like part of the engineering process rather than a separate activity.

How well it scales for serious load testing. I’ve used it to run performance tests up to 150k RPS without needing to manage my own distributed injector infrastructure, thanks to Gatling Cloud. The Scala/Java DSL gives precise control over realistic load simulations, and the reporting makes it easy to pinpoint bottlenecks and share results with both engineering and non-technical stakeholders.