Gatling vs Locust: JVM simulations or Python locustfiles?

Gatling and Locust are both free, open source tools that keep load tests as code in your repository. Pick Gatling if your team works on the JVM or you want open and closed injection profiles, assertions and an HTML report in one package. Pick Locust if your team writes Python, wants a web UI to start and watch a run, and needs distributed mode without a commercial edition.

The facts in this Gatling vs Locust comparison come from each project’s own documentation and repository, checked on 13 October 2026.

What is the difference between Gatling and Locust?

Gatling runs simulations on the JVM, and Locust runs plain Python programs. Gatling gives you a load model and a report out of the box, and Locust gives you test logic you write yourself.

Gatling’s README describes a non-blocking engine built on Netty and an SDK in five languages: Java, Kotlin and Scala run through Maven, Gradle or sbt, and JavaScript and TypeScript through a separate SDK. Its installation page asks for a 64-bit OpenJDK LTS from 11 to 25, and for Node.js 24 or later with the JavaScript SDK.

Locust’s README calls it a way to write scalable load tests in plain Python, and its package metadata requires Python 3.11 or later. The What is Locust page says it is event-based on gevent, so one process can handle many thousands of concurrent users (checked on 13 October 2026).

How do you write the same test in each tool?

In Gatling you chain requests into a scenario and give it an injection profile. In Locust you write a user class with tasks, and a wait time between them.

Fifty concurrent users who list users, then pause one to three seconds, for five minutes. In Gatling’s Java SDK (imports left out):

public class ApiSimulation extends Simulation {
  HttpProtocolBuilder api =
      http.baseUrl("https://staging.example.com");

  ScenarioBuilder users = scenario("API users")
      .exec(http("list users").get("/api/users"))
      .pause(1, 3);

  {
    setUp(users.injectClosed(
        constantConcurrentUsers(50).during(300)))
      .protocols(api);
  }
}

The same test in Locust:

from locust import HttpUser, task, between


class ApiUser(HttpUser):
    wait_time = between(1, 3)

    @task
    def list_users(self):
        self.client.get("/api/users")

Gatling’s injection reference lists constantConcurrentUsers among the closed-model steps. Locust takes the user count and run time as flags (checked on 13 October 2026):

locust -f locustfile.py --headless -u 50 -r 5 -t 5m \
  --host https://staging.example.com

How do Gatling and Locust model load?

Gatling offers both open and closed models, chosen by the method you call. Locust runs a pool of users, which is a closed model.

Gatling’s workload models page defines closed systems as ones that cap concurrent users and open systems as ones where users keep arriving however slowly the application responds, and says most websites are open. injectOpen takes arrival rates and injectClosed takes concurrency targets.

In Locust, the locustfile guide says wait time can only constrain throughput and cannot launch new users to reach a target rate. If an iteration takes longer than the wait allows, the rate drops below the target (checked on 13 October 2026). Our guide to the open vs closed workload model explains why that matters.

How do you run a bigger test than one machine can generate?

Locust has distributed mode built in. Gatling’s open source edition generates load from one machine, and distributed runs need Gatling Enterprise.

Locust starts one process with --master and any number of --worker processes. Its distributed docs say a Python process cannot use more than one core, so you run one worker per core, and --processes starts several on one machine. A Kubernetes operator manages distributed runs inside a cluster.

Gatling’s FAQ says the Community Edition does not support distributed testing, and the README describes Enterprise as running on managed load generators or private infrastructure (checked on 13 October 2026).

There is a way out of both. LoadFocus, which publishes this site, does not run Gatling simulations or Locust tests. If you would rather not run either at scale, it runs k6 scripts and JMeter test plans from 25 cloud regions. Its k6 validator warns that arrival-rate scenarios run there with a fixed number of users, so they do not hold a request rate. Moving from either tool means rewriting the test.

How do they decide whether a test passed, and what reports do you get?

Gatling evaluates assertions after the run and writes an HTML report every time. Locust exits with code 1 if any request failed, and you add your own checks as a listener.

Gatling’s assertions are evaluated after the simulation, and one failed assertion fails the simulation. Its HTML report charts response time percentiles, users started per second, concurrent users and requests per second.

Locust’s headless docs set exit code 1 when any sample failed, and show a listener that fails the run on failure ratio, average response time or 95th percentile. The configuration reference lists --csv for statistics files and --html for a report (checked on 13 October 2026).

Which protocols can each tool test?

Gatling ships more protocols, with limits on the newer ones. Locust ships HTTP and HTTPS and expects a Python client for the rest.

The Gatling README lists HTTP/1.1, HTTP/2, WebSocket, Server-Sent Events and JMS under Apache 2.0 with no limits. gRPC, MQTT and GraphQL sit under the Gatling Enterprise Component License, and Community Edition tests with them are capped at 5 concurrent users and 5 minutes.

Locust’s page on other systems shows how to wrap a protocol library and report each call, with gRPC and MQTT examples. The library has to work with gevent, which cannot patch I/O done in compiled C code, and locust-plugins adds Kafka, Selenium and Playwright users (checked on 13 October 2026).

Which one should you choose?

Choose by language and by how you load the system. Gatling suits JVM teams and open-model tests, and Locust suits Python teams and tests with custom logic.

Choose Gatling instead of Locust when your team lives on the JVM and wants Java, Kotlin or Scala tests built with Maven, Gradle or sbt, when you need an open model such as a fixed arrival rate, when you need JMS or Server-Sent Events, or when you want assertions and a report with no extra code.

Choose Locust instead of Gatling when your team writes Python and wants tests that reuse an internal Python client, when you want a web UI to start a run and change the load while it runs, when you need distributed mode in the free tool, or when the system is mostly waiting users and your protocol has a gevent-friendly Python library.

For a GUI test builder look at JMeter, and for a single binary with no JVM or Python look at k6.

FAQ

Is Gatling faster than Locust? We have not measured this ourselves, so we will not name a winner. The fair test is the same scenario with both tools on your own hardware.

Can Locust run Gatling simulations? Not according to Locust’s documentation, which describes tests only as Python code. A Gatling simulation has to be rewritten as a locustfile, and the reverse is true as well.

Can LoadFocus run Gatling or Locust tests? No. LoadFocus runs JMeter test plans and k6 scripts. To spread a Gatling simulation across machines use Gatling Enterprise, and for Locust use its master and worker mode or its Kubernetes operator.

Related reading

• Gatling load testing: a review of the JVM tool
• Locust load testing: a review of the Python tool
• Gatling vs k6: JVM simulations or a single Go binary?
• Locust vs JMeter: Python scripts or JMeter test plans?
• Open vs closed workload model: which one should you use?

The technical claims in this article have not been independently reviewed.