JMeter vs Gatling : A Detailed Comparison

Among the many tools available for performance testing, Apache JMeter and Gatling have emerged as two of the most widely adopted solutions. Both are open-source tools designed to simulate high traffic and assess system behavior, but they differ in several key aspects.

This article offers a detailed comparison between JMeter and Gatling, highlighting their core strengths, differences, and ideal use cases.

What is Gatling?

Gatling is an open-source load testing tool built using Scala, designed for testing web applications and APIs. It is known for its high performance, low resource consumption, and ability to simulate thousands of virtual users efficiently. Gatling uses a Domain-Specific Language (DSL) for scripting, allowing for expressive and maintainable test scenarios.

Its developer-friendly approach integrates well with modern CI/CD pipelines, and it provides detailed, visually rich HTML reports for easy analysis. With a non-blocking architecture powered by Akka, Gatling is well-suited for large-scale, high-throughput performance testing.

Programming Language Support in Gatling

Gatling allows users to write load testing simulation scripts in various programming languages.

Initially built with Scala, Gatling has expanded its support to include Java, Kotlin, JavaScript, and TypeScript through its “Test-as-code” feature.

This flexibility enables teams with different technical backgrounds to utilize Gatling effectively.

The officially supported languages are:

The choice of programming language for Gatling depends on the team’s expertise, the complexity of the test scenarios, and the desired level of expressiveness. Gatling’s multi-language support makes it a versatile tool for integrating performance testing into the software development lifecycle.

Gatling Load Test Example

Gatling follows a code-centric approach to creating load tests, using a concise DSL that makes test scenarios both readable and reusable. A typical Gatling script defines the HTTP protocol, sets up a scenario (user behavior), and specifies how many virtual users should run the test. This structure allows for clear separation of concerns and easy customization.

Below is a basic example of a Gatling load test written in Scala, simulating virtual users accessing a sample website:

import static io.gatling.javaapi.core.CoreDsl.*;

import static io.gatling.javaapi.http.HttpDsl.*;

import io.gatling.javaapi.core.*;

import io.gatling.javaapi.http.*;

import java.time.Duration;

public class BasicSimulation extends Simulation {

    HttpProtocolBuilder httpProtocol = http

        .baseUrl("https://reqres.in")

        .acceptHeader("application/json");

       

    ScenarioBuilder scn = scenario("Single User Load Test")

        .exec(

            http("Get User Detail")

                .get("/api/users/2")

                .check(status().is(200))

        );

    {

        setUp(

            scn.injectOpen(

                rampUsers(100).during(Duration.ofSeconds(30))

            )

        ).protocols(httpProtocol);

    }

}

This script defines a scenario where 100 virtual users gradually access the user detail over 30 seconds, checking for a successful HTTP 200 response. Gatling’s structure makes it easy to expand tests with additional steps, assertions, and user behavior.

Output: After executing the load test, Gatling automatically generates a detailed HTML report that provides insights into the performance of the application. This report includes metrics such as total requests, response time distribution, success/failure counts, and throughput over time.

Benefits of Using Gatling

Gatling is widely appreciated for its developer-friendly, code-based approach to performance testing. It offers a robust feature set that caters to modern testing and DevOps needs, making it a preferred choice for teams who value scalability, automation, and detailed reporting.

Key benefits include:

• High performance and scalability thanks to its non-blocking architecture based on Akka.
• Code-based scripting with DSL, offering flexibility and maintainability for complex scenarios.
• Seamless integration with CI/CD pipelines, making it suitable for automated testing workflows.
• Detailed and visually rich HTML reports that provide actionable insights.
• Low resource consumption, enabling tests to run efficiently on modest hardware.
• Support for both Scala and Java, catering to different development preferences.

Limitations of Using Gatling

While Gatling offers several advantages, it also comes with some limitations that teams should be aware of before adopting it. These limitations mainly stem from its technical nature and the reliance on code-based scripting.

Common limitations include:

• Steep learning curve for testers without programming experience, especially those unfamiliar with Scala.
• Limited support for non-HTTP protocols, such as FTP or WebSockets (though some are available via plugins or extensions).
• Smaller community and ecosystem compared to tools like JMeter, which can affect plugin availability and community support.
• Basic UI Recorder, which may not meet the needs of teams looking for a more user-friendly interface.
• Less suited for exploratory or ad-hoc testing, due to its script-heavy nature.

What is Apache JMeter?

Apache JMeter is an open-source performance testing tool developed by the Apache Software Foundation. While it was initially created for web application testing, it now supports a wide range of protocols, including HTTP, FTP, SOAP, REST, and JDBC. It helps simulate multiple users to evaluate system performance under load.

JMeter’s GUI-based interface makes it beginner-friendly, especially for testers without programming expertise. It offers configurable test plans, built-in components for complex scenarios, and supports distributed testing for large-scale performance evaluations.

Programming Language Support in JMeter

JMeter is built entirely in Java and relies heavily on the Java ecosystem for its core functionality. While the primary language for running and extending JMeter is Java, it also supports a variety of scripting languages through its built-in components like JSR223, BeanShell, and JEXL.

This flexibility allows users to add custom logic, manipulate data, or extend test functionality directly within the test plan.

The commonly supported languages are:

Groovy is widely recommended due to its performance benefits and deeper integration with JMeter.

The choice of scripting language in JMeter depends on the complexity of the test scenarios, performance considerations, and the technical proficiency of the team. This multi-language support makes JMeter a flexible and extensible tool that caters to both testers and developers across various use cases.

JMeter Load Test Example

JMeter provides an intuitive graphical interface for creating and running load tests. Users can design test plans by dragging and dropping different elements such as Thread Groups, Samplers, and Listeners. This approach eliminates the need for manual coding and allows testers to quickly set up tests by configuring components through the interface.

Below is an example of creating a simple HTTP load test using JMeter’s GUI:

• Create a Thread Group: Define the number of virtual users and the ramp-up time.
• Right-click on the Test Plan  > Add > Threads (Users) > Thread Group.
• Set the number of threads (100 users) and ramp-up time (30 seconds).

• Add HTTP Request: Simulate GET requests to the website.
• Right-click on the Thread Group > Add > Sampler > HTTP Request.
• Enter the URL (https://reqres.in), set the method to GET, and specify the path.

• Add a Listener: Monitor the results of the load test.
• Right-click on the Thread Group > Add > Listener > View Results in Table or Summary Report.

This setup simulates 100 virtual users gradually accessing the homepage of a website over 30 seconds, and the Summary Report listener will display the total number of requests, average response time, and success rate.

Output: Once the test is executed, JMeter displays the results in the configured listener. The Summary Report provides key performance metrics such as the number of samples, average response time, throughput, and error percentage. This helps evaluate how the system behaves under load.

Benefits of Using Apache JMeter

Apache JMeter is a widely used performance testing tool known for its flexibility, open-source nature, and strong community support. It is especially well-suited for teams looking to design and execute complex test plans without heavy reliance on programming.

Key benefits include:

• Open-source and free to use, with no licensing costs.
• User-friendly GUI, making it accessible to non-programmers and manual testers.
• Support for multiple protocols, including HTTP, FTP, JDBC, SOAP, REST, and more.
• Extensive plugin ecosystem, enabling enhanced functionality and customization.
• Built-in reporting and listeners for real-time and post-test analysis.
• Capability for distributed load testing, allowing execution across multiple machines.
• Active community and documentation, offering reliable support and learning resources.

Limitations of Using Apache JMeter

Despite its powerful features, JMeter comes with certain limitations that can affect usability and performance, particularly in modern, large-scale, or highly dynamic test environments.

Common limitations include:

• Resource-heavy under high loads, especially when using the GUI mode for large tests.
• Not ideal for modern asynchronous protocols like WebSockets or gRPC without additional setup or plugins.
• Limited scripting flexibility compared to code-based tools like Gatling.
• Less intuitive reporting compared to some newer alternatives with more advanced visualizations.
• Can become complex and harder to manage when test plans grow in size or involve dynamic data handling.
• Requires Java installation and configuration, which might be a barrier for some teams.

Gatling vs. JMeter: Detailed Comparison

The following comparison highlights the key differences and similarities to help in choosing the right tool based on specific testing needs.

Technology Stack and Scripting Languages

Gatling is built on Scala and Akka, leveraging asynchronous and non-blocking operations. It uses a domain-specific language (DSL) written in Scala, with support extended to Java, Kotlin, JavaScript, and TypeScript.

JMeter, on the other hand, is Java-based and relies primarily on a GUI for test creation. It supports scripting through BeanShell, Groovy, and Java, but the scripting experience is more limited compared to Gatling’s code-centric model.

Setup

Gatling set up involves configuring a development environment with Java and a supported IDE, such as IntelliJ or Eclipse. Since tests are written in code, developers will feel right at home.

JMeter is easier to set up for non-programmers, requiring only Java and the JMeter binary. It can be launched directly as a standalone application with no IDE dependency.

Scripting

Gatling emphasizes a programmatic approach, where test scripts are written as code, promoting modularity, reusability, and version control. This makes it easier to maintain in collaborative environments.

JMeter offers a GUI where users can construct test plans using components and logic controllers. While less maintainable at scale, it’s much faster for creating basic tests, especially for users without a development background.

Performance

Gatling’s architecture supports asynchronous processing and efficient resource utilization, allowing it to handle thousands of virtual users on modest hardware.

JMeter uses a thread-per-user model, which can become resource-intensive for large-scale tests. As a result, JMeter often requires a distributed setup to simulate high traffic.

Test Execution

Gatling tests can be easily integrated into CI/CD pipelines and executed from the command line, which fits well into automated development workflows.

JMeter supports both GUI and non-GUI execution modes. While the GUI is user-friendly for test creation and small runs, non-GUI mode is preferred for actual load testing to reduce overhead.

Cloud Load Testing

Both tools support cloud-based load testing, but through different approaches.

Gatling provides Gatling Enterprise (formerly FrontLine) with built-in cloud features like distributed testing, monitoring, and dashboards.

JMeter can be extended for cloud testing using services like BlazeMeter or by setting up distributed nodes manually.

Ease of Use

Gatling, while powerful and developer-friendly, has a steeper learning curve due to its code-based nature. However, teams familiar with programming can benefit from greater control and flexibility.

JMeter is easier to use for beginners and testers without coding experienc, thanks to its drag-and-drop interface and visual test creation.

Tooling and Plugins

Gatling is more streamlined and focused, with fewer plugins but solid integration with build tools and CI environments. Its minimalist approach favors clean setups without plugin overhead.

JMeter has a rich ecosystem of plugins that enhance its capabilities, from custom samplers to advanced reports and protocol support.

Reporting and Visualization

Gatling generates detailed and visually rich HTML reports by default, showcasing metrics like percentiles, active users, and error rates.

JMeter also provides reporting through listeners and dashboard reports, although some advanced visualizations may require external tools like Grafana or additional plugins.

Use Cases

Gatling is ideal for API performance testing, especially in CI/CD pipelines where maintainability and integration matter. It excels in environments requiring code versioning and modular test design.

JMeter is well-suited for broader use cases, including web applications, databases, FTP servers, and more, thanks to its extensive protocol support and GUI-based approach.

Pricing

Both tools are free to use in their open-source versions.

Gatling offers a commercial edition (Gatling Enterprise) with enterprise-grade features like distributed execution, enhanced reporting, and user management.

JMeter remains fully open-source, though teams may opt for paid platforms like BlazeMeter for advanced needs.

Community and Support

Gatling’s community is growing, and while smaller, it offers high-quality documentation and enterprise support options for commercial users.

JMeter has been around longer and has a larger, more mature community with vast documentation, forums, and plugins.

Gatling vs. JMeter: A Quick Comparison

Why Use Real Devices with JMeter or Gatling for Load Testing?

While tools like JMeter and Gatling are powerful for simulating user traffic and measuring server-side performance, they don’t reflect how a real user experiences the application. Combining load testing with real device testing helps achieve end-to-end visibility by capturing front-end behavior under stress.

Talk to an Expert

Real devices allow testers to observe how pages render, how UI components respond, and how network conditions impact performance, especially on varied browsers, operating systems, and screen sizes. This helps uncover bottlenecks that backend-only testing often misses, such as slow third-party scripts, rendering delays, or layout shifts.

BrowserStack enables seamless integration of real devices into your performance testing workflow. With a vast real device cloud of 3500+ Android and iOS devices, BrowserStack lets you validate performance under real-world conditions, without the need for physical device labs. Combined with JMeter or Gatling, this ensures your applications are optimized both for backend scalability and real user experience.

Conclusion

Choosing between Gatling and JMeter depends on the specific needs of your project and team. Gatling offers a developer-centric, code-first approach with high performance and seamless CI/CD integration, making it ideal for engineering-driven teams focused on APIs. JMeter, with its intuitive GUI and broad protocol support, suits testers and QA teams working across varied applications and technologies.

While both tools effectively simulate load and analyze backend performance, pairing them with real device testing ensures complete end-to-end validation. It bridges the gap between backend metrics and real user experience, revealing how your application truly performs in real-world conditions.

For teams looking to scale testing and deliver flawless digital experiences, combining these tools with BrowserStack’s real device cloud offers the perfect balance of power, accuracy, and usability.

Try BrowserStack Now