Locust vs Gatling
Feature checklists rarely decide anything. What decides it is the pricing metric, who has to write the tests, and which limitation you can live with.
Entry price
Free and open source
Open sourceFrom €89 per month (indicative, Enterprise Cloud entry)
Free tierWhat drives the bill
The metric that actually scales your invoice.
Free and open source. You pay in engineering time and CI minutes.
Open source is free; Enterprise adds distributed injectors and reporting, billed by virtual-user-hours.
Free tier
Everything, self-hosted
Gatling open source is complete for single-machine load generation.
Open source
MIT
Apache-2.0
Self-hostable
No-code authoring
Languages
Platforms
Fits teams
Adoption
Widely used
OSS community · since 2011
Widely used
Gatling Corp · since 2012
Strengths
- Test scenarios are plain Python, so any logic is expressible without workarounds.
- Distributed mode is conceptually simple and works well on Kubernetes.
- Live web UI is useful during an exploratory ramp-up.
- Very high throughput per load generator thanks to the async architecture.
- Code-first scenarios that review, diff and refactor properly.
- The default HTML report is genuinely good enough to hand to a stakeholder.
Limitations
- Python's per-user overhead is higher than k6 or Gatling; big loads need more machines.
- Reporting is basic — plan on exporting to Grafana.
- HTTP-centric; other protocols require writing your own client.
- Historically Scala-first; the Java and JavaScript DSLs are newer and less documented.
- Fewer protocols than JMeter.
- Distributed load without the enterprise product means building your own orchestration.
Best for
Python teams who want load scenarios expressed in their own language.
JVM teams wanting code-first performance tests with excellent reporting.
All of these integrate with: Grafana, GitHub Actions.
Pricing last reviewed August 2026 and is indicative only — confirm on each vendor’s own page.