JMeter vs. Locust: Which One To Choose ?

Denis Sautin

Author

Denis Sautin

Denis Sautin is a Senior Copywriter at PFLB. He works on producing and reviewing a wide range of technical and editorial content. Denis collaborates with product, marketing, and engineering teams to maintain consistency and structure. He has over 10 years of experience in managing and delivering content for tech companies.

Senior Copywriter

Reviewed by Boris Seleznev

Reviewed by

Boris Seleznev

Boris Seleznev is a seasoned performance engineer with over 10 years of experience in the field. Throughout his career, he has successfully delivered more than 200 load testing projects, both as an engineer and in managerial roles. Currently, Boris serves as the Professional Services Director at PFLB, where he leads a team of 150 skilled performance engineers.

Choosing between JMeter and Locust can feel like picking between two complex frameworks with distinct strengths. Both are popular for performance and load testing — but they come with steep learning curves, scripting needs, and manual configurations. Many organizations are turning to modern performance testing services to avoid the hassle of managing these tools directly. If you’re stuck in the JMeter vs Locust debate, here’s a thought: what if you didn’t have to choose at all? Read on to find out.

What is JMeter?

Apache JMeter is a widely used open-source tool designed for performance and load testing. Originally built for web applications, it now supports a wide range of protocols, including HTTP, HTTPS, FTP, LDAP, and SOAP, making it a versatile option for developers and QA engineers. Many teams are now leveraging cloud infrastructure for JMeter tests to overcome local resource limitations and scale their performance testing capabilities.

JMeter allows users to create and simulate heavy loads on servers to evaluate application performance under stress. Its GUI interface helps in building test plans, while advanced users can dive deeper using Groovy or BeanShell scripting. Test results can be visualized in real-time or exported for further analysis.

Because of its extensibility and plugin ecosystem, JMeter remains a go-to choice for teams seeking a robust, scriptable testing solution.

What is Locust?

Locust is an open-source, Python-based load testing tool that emphasizes simplicity, scalability, and developer-friendliness. Unlike GUI-based tools, Locust uses plain Python code to define user behavior, giving testers complete control over test logic.

It shines in environments where you need flexible scripting and seamless integration with DevOps workflows. Locust supports distributed testing through a master-worker model, making it easy to simulate millions of concurrent users with minimal system resources. It also includes a real-time web-based UI for monitoring test runs.

If you’re comfortable with Python and want a lightweight, highly scalable tool for performance testing, Locust is a strong choice — especially when comparing JMeter and Locust side by side.

JMeter vs. Locust: Comparison Table

When choosing a tool for performance and load testing, it’s crucial to understand the differences between JMeter and Locust. Both are open-source, but they take very different approaches in terms of language, architecture, and ease of use. Below is a detailed comparison of Locust load testing vs JMeter — their key features to help you evaluate which fits your workflow.

Key Differences Between JMeter vs. Locust

Although both tools are open-source and powerful, JMeter and Locust take very different approaches to performance testing. Below, we break down their core differences across several critical areas.

Architecture

JMeter is built on a multi-threaded architecture where each virtual user consumes a dedicated thread. This can lead to high memory and CPU usage, especially in large-scale tests or when testing modern gRPC services. Distributed execution is possible but requires configuring multiple remote machines.

Locust, by contrast, relies on Python’s asynchronous capabilities via gevent, using an event-driven model that is significantly more efficient. It can simulate thousands or even millions of concurrent users with less system overhead and smoother scalability.

Ease of Use

JMeter offers a GUI that lowers the entry barrier for beginners, especially for basic tests. However, more complex scenarios require manual scripting or plugin use, and the interface can feel outdated and clunky.

Locust uses plain Python code for writing tests, which appeals to developers familiar with scripting. There’s no GUI for test creation — just a simple, clean codebase that gives you complete control and flexibility.

JMeter Interface

Scalability

JMeter supports distributed testing but scaling requires extra setup. You’ll need to configure remote servers and ensure proper coordination between client and slave nodes.

Locust has built-in support for distributed load generation. Its master-worker architecture lets you scale horizontally with minimal configuration, making it a better fit for high-concurrency environments.

Reporting

JMeter includes built-in reporting features such as summary reports, graphs, and an HTML dashboard. While functional, it can be overwhelming to interpret without prior experience, especially when tests become complex.

Locust provides a lightweight web-based UI with live stats and charts during test execution. However, it lacks deep built-in reporting — most users rely on external tools like InfluxDB and Grafana for long-term metrics and visualization.

Number of Concurrent Users

JMeter struggles with very high user counts due to its thread-per-user model. Resource consumption becomes a bottleneck as the number of virtual users increases.

Locust handles high concurrency with greater efficiency thanks to its asynchronous design. It performs well in large-scale scenarios, especially when testing APIs or web apps with many simultaneous users.

Locust Interface

Script Recording

JMeter offers a built-in HTTP(S) Test Script Recorder that captures browser actions, making it easier for non-developers to create tests.

Locust lacks a native recorder. All user behavior must be defined manually in Python code. This makes it less beginner-friendly but more powerful and transparent for those comfortable with programming.

Why You Don’t Need to Compare JMeter and Locust If You Use PFLB

After comparing JMeter and Locust, one thing becomes clear — both come with trade-offs. JMeter gives you power, but at the cost of complexity. Locust is lighter and more flexible, but expects you to code and configure everything. Neither is truly plug-and-play — and both can take hours (or days) just to get a test off the ground.

That’s exactly why PFLB exists.

PFLB Interface

PFLB removes the pain points testers and developers face with traditional tools. There’s no steep learning curve, no complicated setup, and no need to choose between script-based or GUI-based approaches — it supports both. You can import request collections, run tests instantly in the cloud, and get detailed performance reports without writing a single line of code. And it offers its own API load testing tool.

With PFLB, you get:

• No-code and low-code test creation
• Integration with Google Analytics, Postman, and Insomnia
• Cloud-based load generation across multiple geographic regions
• Real-time monitoring via Grafana dashboards
• AI-powered analysis and detailed reporting
• Seamless integration with your CI/CD pipelines
• Support for complex testing scenarios — without the scripting overhead

So if you’re torn between JMeter vs. Locust, the answer might be: neither. With PFLB, you’re skipping the headache and going straight to testing.