Gatling represents a different philosophy from GUI-first tools like JMeter, performance tests written entirely as code, version-controlled alongside the application, and designed from the ground up to run efficiently inside a CI/CD pipeline rather than as a separate, manually-triggered activity.
What sets Gatling apart
- Code-first test definitions, written in a Scala-based DSL, that live in version control just like application code, reviewable in a pull request like anything else.
- High performance with efficient resource use. Gatling's asynchronous, non-blocking architecture can simulate large numbers of concurrent users from comparatively modest hardware, an efficiency advantage over some GUI-based tools.
- Clear, readable test definitions. The DSL is designed to read almost like a specification of user behavior, not just a list of technical requests.
- Built-in CI/CD friendliness. Designed from the start to run as part of an automated pipeline, with reporting that fits naturally into that workflow.
A simple example
class CheckoutSimulation extends Simulation {
val httpProtocol = http.baseUrl("https://example.com")
val checkoutScenario = scenario("Checkout Flow")
.exec(http("Load cart").get("/cart"))
.pause(2)
.exec(http("Submit payment").post("/checkout").body(StringBody("""{"card":"4242..."}""")))
.exec(http("Confirm order").get("/order-confirmation"))
setUp(
checkoutScenario.inject(rampUsers(500).during(60.seconds))
).protocols(httpProtocol)
}
This reads almost like a specification: load the cart, pause, submit payment, confirm the order, ramping up to 500 concurrent users over sixty seconds. That readability is a deliberate design choice, not an accident.
Gatling's real innovation wasn't just performance testing as code, it was making performance testing something a developer could review in a pull request, the same way they'd review any other test.
Where the learning curve shows
Gatling's Scala-based DSL is genuinely more approachable than raw Scala, but it's still a steeper initial learning curve than a pure GUI tool like JMeter, particularly for testers without a programming background. Teams without existing code-first testing culture sometimes find the transition takes real investment upfront, though that investment pays off quickly once the team is comfortable with the pattern.
Where we reach for Gatling
We recommend Gatling for teams that already have, or want to build, a code-first testing culture, and specifically want performance tests integrated tightly into CI/CD, running automatically rather than as a separate manual activity someone has to remember to trigger before a release.
If you want performance testing that lives in your pipeline rather than beside it, that's exactly the kind of setup QA Solucity's performance testing service builds. Get in touch to talk through your CI/CD workflow.
