I’m in the early stages of building out a load testing strategy at my company and want to hear what tools and approaches others are using in production.
What tools do you use regularly (CLI/GUI)?
Are you relying on any underrated tools that worked surprisingly well?
Do you use paid tools—and are they worth the investment?
Have you integrated load testing into your CI pipelines?
How realistic is your test traffic in terms of user behavior simulation?
Are you trying any new or emerging tools/techniques in this space?
Looking forward to hearing what’s worked (or not) in your teams!
K6 – Lightweight, Scriptable, and CI-Ready Load Testing Tool
When it comes to building an effective load testing strategy, we standardized on K6, and honestly, it’s been a game-changer. It’s an open-source, CLI-based tool that lets us write performance scripts using JavaScript—making it super approachable for our engineers.
What I really like is how lightweight K6 is. It fits neatly into our GitLab CI pipeline, so we can run performance tests automatically after every major build. That kind of automation is a huge win for catching bottlenecks early.
But it’s not just about sending raw traffic—K6 helps simulate real user behavior using logic, iterations, and dynamic data. This makes our tests way more realistic compared to flat-load tools.
We started with the open-source version, which was more than enough initially. Later, we upgraded to K6 Cloud for large-scale test runs and access to historical dashboards. It’s a paid add-on, but we only use it for high-impact releases.
If you’re starting fresh or revamping your load testing setup, K6 strikes that perfect balance between developer-friendly scripting and production-grade observability.
Curious where to begin? Check out this hands-on tutorial: K6 Testing Tutorial – LambdaTest Blog
Building on what @raimavaswani shared about k6, if your environment demands more complex user journeys and logic branching, Gatling might be your go-to. We used it in a fintech app with multi-step flows, and its Scala-based DSL let us replicate everything—from login sessions to conditional workflows—precisely.
It takes a little time to ramp up, but once you’re fluent, it’s a performance powerhouse. We used Gatling Enterprise for scheduled runs, Jenkins integration, and a GUI-based dashboard. The visibility across long-term trends and collaboration between QA, DevOps, and product helped us make performance part of every sprint.
If your team has some Java/Scala comfort and wants deeper scenario modeling, Gatling brings serious long-term value. That’s how we approached load testing in our environment when complexity mattered.
In early-stage startups, simplicity often wins. I’ve worked in places where speed and budget shape all tooling decisions.
Artillery + Custom Monitoring for Lightweight Scenarios
Following on from the heavier tools like k6 and Gatling—at a lean startup I was part of, we needed something fast and scriptable with almost zero learning curve. Artillery fit perfectly. It’s Node.js-based and defines test cases in simple YAML. For us, the CLI + Grafana combo was enough to visualize test metrics without extra spend.
We didn’t need massive load tests—just solid peak simulations and flow modeling. Artillery let us throttle concurrency, add basic thresholds, and still keep tests easy to maintain. No fancy dashboards or cloud services—just focused, fast feedback.
It’s definitely not for deep enterprise-scale simulation, but for teams prioritizing cost-efficiency, clarity, and fast iteration, this is how we answered how are you approaching load testing in your environment—without overengineering the solution.