Locust vs JMeter: Python scripts or JMeter test plans?

Locust and Apache JMeter are both free, open source load testing tools, and the better fit depends mostly on how your team wants to build tests. Pick Locust if your team writes Python and wants test logic as ordinary code, run by lightweight event-based users. Pick JMeter if you need protocols beyond HTTP such as JDBC, JMS or LDAP, prefer to build tests in a GUI, or already own a library of .jmx test plans.

The facts in this Locust vs JMeter comparison come from each project’s own documentation and repository, checked on 9 October 2026.

What is the difference between Locust and JMeter?

In Locust a test is a Python program. In JMeter a test is a plan you assemble in a desktop GUI and save as a file, and most other differences follow from that.

Locust’s README describes tests written with plain Python loops, conditions and libraries. It installs with pip install locust on Python 3.11 or later, according to its package metadata.

A JMeter test is a tree of elements: a thread group for the users, samplers for the requests, and assertions and listeners for the results. JMeter 5.6.3, the current release on the download page, requires Java 8 or later. The README on JMeter’s development branch already calls for Java 17 (checked on 10 October 2026). When a step needs real logic, a JSR223 sampler runs a Groovy script inside the plan.

How do you write a test in each tool?

In Locust you write a Python class for each kind of user, with methods marked as tasks. In JMeter you add a thread group and samplers in the GUI, save the plan and run it from the command line.

A Locust user that waits one to three seconds between tasks and lists users three times as often as it opens one:

from locust import HttpUser, task, between


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

    @task(3)
    def list_users(self):
        self.client.get("/api/users")

    @task
    def view_user(self):
        self.client.get("/api/users/42", name="/api/users/[id]")

Run it headless with 50 users, starting five per second, for five minutes:

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

In JMeter the same test is a Thread Group with 50 threads and a ramp-up period, holding two HTTP Request samplers. The getting started guide runs a saved plan in CLI mode, and -e -o adds the HTML report at the end:

jmeter -n -t api-users.jmx -l results.jtl -e -o report/

The .jmx file is XML, as the examples shipped with JMeter show, and harder to review in a pull request than Python. JMeter can also record a plan from a browser; Locust’s closest match is har2locust, a beta HAR-to-locustfile converter (checked on 9 October 2026).

Which protocols can each tool test?

JMeter covers many more protocols out of the box. Locust ships with HTTP and HTTPS and expects you to bring a Python client for anything else.

JMeter’s README lists HTTP and HTTPS, SOAP and REST, FTP, databases through JDBC, LDAP, JMS messaging, SMTP, POP3 and IMAP mail, TCP, native commands and Java objects. JDBC and JMS need driver jars from your database or JMS provider.

Locust’s page on other protocols shows how to wrap a protocol library, with examples for gRPC, XML-RPC and MQTT, and points to locust-plugins for Kafka, Selenium and Playwright (checked on 9 October 2026). The library must cooperate with gevent, because I/O in compiled C code blocks the whole process; the docs name the PostgreSQL driver psycopg2 as one that needs an extra package. For database load tests, JMeter’s JDBC sampler is the shorter path.

How do virtual users work in Locust and JMeter?

JMeter runs each virtual user as a Java thread. Locust runs each user as a greenlet on gevent’s event loop, which keeps the cost of each user low.

Each thread in a JMeter thread group runs the whole plan independently. JMeter starts with a 1 GB heap by default, which its guide says may not be enough for larger plans and thread counts.

Locust’s README says a single process can handle many thousands of concurrent users. Its distributed load docs add that a Python process cannot fully use more than one CPU core, so you run one worker per core, which --processes sets up on a single machine.

How do you run a test across several machines?

Both have a distributed mode built in. Locust’s master directs workers that run the users, and JMeter’s client starts the full test plan on every remote server.

In Locust, one process started with --master runs the web UI and tells the --worker processes when to start and stop users. The Locust docs also cover a Kubernetes operator and Azure Load Testing, a Microsoft service that runs Locust tests in the cloud (checked on 9 October 2026).

JMeter’s remote testing mode lets one client control any number of servers over RMI. Each server runs the whole plan, so 1,000 threads on six servers means 6,000 threads. All nodes should run the same JMeter and Java versions, RMI needs a keystore unless SSL is disabled, and data files must be copied to each server.

There is a third route for JMeter test plans. LoadFocus, which publishes this site, runs existing JMeter test plans unchanged from 25 cloud regions, with no remote servers to configure. LoadFocus does not run Locust tests, so for Locust the options above remain the ways to scale.

How do they fit into CI, and what reports do you get?

Both run headless from the command line. Locust also exits with code 1 when requests fail, and JMeter produces the richer HTML report.

Locust’s headless docs show a short event listener that sets the exit code from the failure ratio, the average response time or the 95th percentile, plus a GitHub Actions example. Its --csv and --html options save results and a report after the run.

JMeter’s documentation is firm that load tests run in CLI mode, with the GUI kept for building and debugging, and its README points to third-party libraries for Maven, Gradle and Jenkins. The JMeter dashboard adds an APDEX table, a statistics table with three configurable percentiles, error summaries and response time charts.

Which one should you choose?

Choose Locust when your team writes Python and wants load tests reviewed like the rest of its code. Choose JMeter instead when you need protocols beyond HTTP, when your testers prefer a GUI, or when you already have .jmx plans.

Locust suits scenarios with loops, branching and generated data, tests that reuse an internal Python client for your API, and high concurrency from users who spend most of their time waiting.

JMeter is the better choice for JDBC, JMS, LDAP, FTP or mail testing, where Locust would need a client you maintain yourself, and for testers who record a flow in a browser and refine it visually. A company that already runs JMeter also keeps its plans and know-how.

FAQ

Is Locust faster than JMeter? 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 JMeter .jmx files? Not according to its documentation, which describes tests only as Python locustfiles. Moving a JMeter test plan to Locust means rewriting it in Python.

Can LoadFocus run Locust tests? No. LoadFocus runs JMeter and k6 scripts. To scale a Locust test beyond one machine, use Locust’s master and worker mode, its Kubernetes operator or Azure Load Testing.

Related reading

• Locust load testing: a review of the Python tool
• Gatling vs JMeter: tests as code or a GUI test plan?
• k6 vs Artillery: which load testing tool fits your team?
• k6 vs Locust: JavaScript scripts or Python users?
• Gatling vs Locust: JVM simulations or Python locustfiles?
• JMeter vs Postman: which tool for load testing?

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