Gatling vs. JMeter: What Tool to Use?

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.

Imagine launching a new product, only to have your website crash the moment traffic spikes. Or rolling out an update, confident in its stability — until real users start complaining about slow response times and timeouts. Performance issues can make or break an application, and that’s why performance testing is non-negotiable.

Now, the question is: What’s the best tool for load testing? 

There’s no shortage of options, but two names stand out:

Both tools has its strengths and trade-offs. But if you’re wondering which one fits your team’s workflow, technical expertise, and performance testing needs, this guide will break it all down for you.

Performance testing tools come in different flavors, each catering to specific use cases, technical expertise, and scalability needs. Let’s compare the tools to understand what they bring to the table.

What is JMeter?

JMeter is one of the most well-known open-source performance testing tools, developed by the Apache Foundation. It has been around for years and remains a go-to solution for testing web applications, APIs, databases, and various other services.

Best For: Teams that need an open-source, highly extensible tool for multi-protocol testing, provided they’re comfortable with manual configuration and infrastructure setup.

For teams looking to avoid the complexity of setting up and maintaining infrastructure manually, exploring options like running JMeter tests in the cloud can significantly reduce overhead and improve scalability. Meanwhile, those focused on backend robustness, especially in microservices or complex API ecosystems, leveraging advanced methods to ensure API stability under load becomes essential to validate performance under real-world conditions.

What is Gatling?

Gatling is a developer-centric performance testing framework designed for modern web applications. Unlike JMeter, which provides a GUI, Gatling is entirely code-driven, meaning tests can be written as Scala, Kotlin, Java and JavaScript code.

Best For: Developers and DevOps teams who prefer a “load testing as code” approach, need high-performance test execution, and want to integrate performance testing directly into CI/CD pipelines. Learn more about Gatling alternatives in our article.

JMeter vs Gatling: Comparison Table

How does Apache JMeter compare to Gatling for load testing? Check out the JMeter and Gatling comparison below:

What Are The Key Differences Between JMeter and Gatling?

Every performance testing tool has its strengths and trade-offs. Let’s explore Gatling vs Jmeter comparison in more detail:

JMeter: Pros and Cons

One of the key Apache Jmeter and Gatling differences is that JMeter is a go-to open-source solution for performance testing, widely used in enterprise environments. It’s flexible but comes with notable challenges. Learn more about JMeter alternatives.

✅ Pros:

• Free and open-source – No licensing costs, making it accessible to all teams.
• Broad protocol support – Works with HTTP(S), FTP, JDBC, JMS, SOAP, REST, SMTP, LDAP, and more, making it ideal for testing complex system architectures.
• Highly extensible – Large plugin ecosystem and support for scripting (Groovy, Java, JSR223) allow customization for advanced scenarios.
• Integration-ready – Works with Jenkins, GitLab CI/CD, InfluxDB, Grafana, and other monitoring tools for test execution and reporting.

❌ Cons:

Gatling: Pros and Cons

Gatling is a high-performance testing framework designed for developers and DevOps teams. It excels in efficiency and automation, but its code-driven approach limits accessibility.

✅ Pros:

• Lightweight and scalable – Uses asynchronous non-blocking IO, enabling it to simulate high user loads with minimal resource consumption.
• Great for CI/CD – Designed to fit seamlessly into DevOps workflows, making it ideal for continuous performance testing.
• Rich reporting – Generates detailed HTML reports with response time distributions, error rates, and percentiles.
• Good scripting capabilities – Written in Scala, Kotlin, Java and JavaScript, allowing for precise control over test scenarios.
• Efficient for high-traffic services – Gatling’s architecture allows it to handle thousands of virtual users per instance, reducing hardware needs.

❌ Cons:

• Requires programming knowledge – Tests are written in Scala (or Java/Kotlin), which creates a steep learning curve for non-developers.
• No built-in GUI – Unlike JMeter or PFLB, Gatling lacks a native graphical test builder, meaning users must define test flows entirely in code.
• Limited protocol support – Primarily supports HTTP(S), WebSockets, JMS, and SSE, making it less versatile than JMeter for database, email, or FTP testing.
• Scaling requires manual setup – While efficient, running tests across multiple machines requires Gatling Enterprise or manual orchestration.

What If Neither Is For You?

If neither Gatling nor Locust fits you needs — consider PFLB. PFLB is a next-generation, cloud-based performance testing platform that takes a radically different approach from traditional tools like JMeter and Gatling. Instead of requiring complex scripting or infrastructure setup, PFLB provides an easy-to-use, AI-powered environment for large-scale performance testing.

• No-Code Test Creation
  Unlike JMeter or Gatling, PFLB allows users to design load tests without any coding or scripting. Its drag-and-drop UI makes it accessible for testers, developers, and even non-technical teams—while also supporting integrations like secure data masking for compliance and testing, which ensures that sensitive information remains protected throughout test cycles.
• Cloud-Based Scalability
  While JMeter and Gatling require manual setup for distributed testing, PFLB runs entirely in the cloud, allowing users to simulate millions of virtual users across multiple geographic locations instantly. No hardware provisioning or setup is needed.

• AI-Powered Performance Insights
  PFLB doesn’t just execute load tests—it analyzes results in real-time using AI to identify bottlenecks, trends, and potential system weaknesses before they become real-world problems.
• Modern Protocol Support
  In addition to HTTP(S), PFLB supports gRPC (used in microservices), Kafka (streaming data services), AMQP/JMS (messaging queues), and REST APIs, making it ideal for modern cloud-native applications.

Best For: Enterprises and teams that want fast, scalable, and effort

Final Thoughts

Looking closely at JMeter vs Gatling you can see that traditional performance testing tools still have their place, but they come with trade-offs—whether it’s complex setup, scripting requirements, or infrastructure limitations. They work well if you have a dedicated team of engineers ready to fine-tune and maintain your testing environment. But for businesses that need scalability, efficiency, and real-time insights without the hassle, PFLB is the smarter choice.

With cloud-based scalability, AI-driven analysis, and a no-code interface, PFLB removes technical barriers while delivering enterprise-level performance testing. Whether you’re simulating millions of users, running continuous CI/CD tests, or preparing for a high-traffic event, it provides everything you need without the complexity.

If performance testing should be seamless, scalable, and insightful — PFLB is the way forward.

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