---
title: "JMeter vs Gatling"
description: "Compare Apache JMeter with Gatling: architecture, Scala/Java/Kotlin DSL, async VUs, protocols, distributed load, reporting, and when to choose each tool."
url: https://docs.jmeter.ai/topics/jmeter-vs-gatling/
lastUpdated: 2026-10-01
source: docs.jmeter.ai
---

# JMeter vs Gatling

This page compares **Apache JMeter** with **Gatling**, a JVM-based load testing tool focused on high-performance HTTP load generation with a typed DSL (Scala historically; Java/Kotlin DSLs widely used) and polished HTML reports. It is a decision aid for teams weighing JMeter’s GUI and protocol breadth against Gatling’s async efficiency and report quality. Always validate with a proof-of-concept on *your* APIs and infrastructure.

For the broader comparison including k6 and Locust, see [JMeter vs Alternatives](/topics/jmeter-vs-alternatives/).

## Quick comparison

| Dimension | JMeter | Gatling |
| --- | --- | --- |
| **Primary runtime** | JVM | JVM |
| **Scenario authoring** | GUI + XML `.jmx` (+ DSL options in modern JMeter) | Code (Scala/Java/Kotlin DSL) |
| **Virtual users model** | Thread-oriented (1 VU ≈ thread in classic Thread Group) | Async event-driven |
| **Protocols (typical OSS)** | Broad: HTTP(S), JDBC, JMS, LDAP, FTP, mail, etc. | HTTP-focused; WebSocket/JMS in ecosystem |
| **GUI for design** | Full test-plan GUI | No classic record-and-click design GUI |
| **Recording** | [HTTP(S) Test Script Recorder](/topics/http-recorder/) | Recorder offerings vary by edition/ecosystem |
| **Distributed load** | Native controller-worker ([remote testing](/topics/distributed-testing/)) | OSS local; enterprise features for large grid |
| **Reporting** | HTML [dashboard](/user-manual/generating-dashboard/), CSV/JTL, listeners | Rich HTML reports |
| **License (core)** | Apache License 2.0 | Apache License 2.0 (Gatling OSS) |

## Gatling positioning

Gatling focuses on **high-performance HTTP load generation** with a **typed DSL** and polished HTML reports. Enterprise editions add collaboration and distributed features. It is popular in JVM teams that want code-centric simulations with compile-time checks.

## Strengths of Gatling relative to JMeter

- **Async engine efficient** for many concurrent HTTP users per injector, often achieving higher VU density per CPU than thread-per-VU models.
- **Scenario-as-code with compile-time checks** in typed DSLs (Scala, Java, Kotlin), which improves maintainability and review.
- **Report quality** is a frequent reason teams adopt Gatling - rich HTML reports with clear metrics.
- **Apache 2.0 for Gatling OSS** aligns with many compliance needs.
- **JVM ecosystem** means teams can leverage existing build tools (Maven, Gradle, sbt) and dependency management.

## Trade-offs of Gatling relative to JMeter

- **Steeper ramp for non-developers** than JMeter GUI; the typed DSL assumes developer authors.
- **Protocol breadth of JMeter’s sampler catalog is wider** in classic enterprise packs (LDAP, JMS, FTP, etc.).
- **Large distributed grids may push teams toward commercial offerings** - evaluate current OSS limits honestly.
- **Recording/onboarding path differs** from JMeter’s long-standing proxy tutorial tradition.
- **Scala learning curve** for teams unfamiliar with functional programming, though Java/Kotlin DSLs are more accessible.

## When teams choose Gatling over JMeter

- **JVM + typed DSL preference** where the team is comfortable with Scala, Java, or Kotlin.
- **HTTP-heavy systems at large concurrency per box** where async VUs matter.
- **Report aesthetics and code-centric review culture** drive the decision.
- **Teams that want compile-time validation** of test scenarios.
- **Existing JVM build pipelines** (Maven, Gradle) that can integrate Gatling tasks.

## Decision criteria

| Need | Lean toward |
| --- | --- |
| HTTP at huge VU density per CPU | Gatling (async VUs) |
| Typed DSL with compile-time checks | Gatling |
| Rich HTML reports | Gatling |
| Non-HTTP protocols (JDBC, LDAP, JMS) | **JMeter** |
| GUI recording for mixed-skill teams | **JMeter** |
| Apache 2.0 licensing | Both (verify current terms) |

## Architecture implications

Gatling’s async event-driven model differs from JMeter’s thread-per-VU model:

- **Higher VU density per CPU** on HTTP workloads, since async VUs are not tied to OS threads.
- **No JVM heap sizing concerns per VU** in the same way as JMeter threads, though the JVM still needs proper sizing.
- **No native JMeter-grade remote testing** experience in the OSS edition; distributed grids may require commercial features.
- **JVM ecosystem** means teams can leverage existing build tools (Maven, Gradle, sbt) and dependency management.

## CI/CD and reporting

All tools run headlessly. Gatling scenarios are code, so they integrate with existing JVM CI pipelines and can use the same build tools as the application under test.

Gatling reporting is strong HTML reports. JMeter reporting is offline HTML dashboard from CLI (`-e -o`) with APDEX and percentile tables. If your compliance process requires an **offline HTML artifact** from an air-gapped runner, both tools can emit full reports without a SaaS account, but verify the specific edition and configuration.

## Migrating from JMeter to Gatling

- **Re-implement scenarios** in the Gatling DSL; do not expect 1:1 `.jmx` conversion.
- **Rebuild correlation and data feeds** (CSV → Gatling feeders).
- **Re-validate think time and workload models** - defaults differ.
- **Map JMeter listeners to Gatling assertions** and checks.
- **Train the team on the Gatling DSL** if they are not already JVM-fluent.

## Migrating from Gatling to JMeter

- **Use the [HTTP(S) recorder](/topics/http-recorder/) or cURL import** for HTTP scenarios.
- **Map Gatling feeders to CSV Data Set** + [functions](/topics/functions-and-variables/).
- **Train the team on CLI-only load runs** per [best practices](/user-manual/best-practices/).
- **Use programmatic plans** if you want code review workflows in JMeter.

## Fair bake-off checklist

When you trial Gatling and JMeter on the same API:

1. Same **workload model** (arrival rate vs closed-loop users).
2. Same **think time** and data cardinality.
3. Same **environment** and monitoring on the server side.
4. Measure **injector CPU/RAM**, not only server latency.
5. Include **failure modes** (timeouts, 500s) and assertion strictness.
6. Include **team time** to author and maintain the script.
7. Check **license** and support explicitly.

A tool that looks fastest in a blog chart can lose if your team cannot maintain scenarios.

## Continue Learning

→

### Next Practical Step

If JMeter fits, start with [Getting Started](/getting-started/get-started/) and a small [web test plan](/user-manual/build-web-test-plan/), then enforce CLI runs via [best practices](/user-manual/best-practices/).

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### Related Reference

- [JMeter vs Alternatives](/topics/jmeter-vs-alternatives/) - hub page with all tool comparisons
- [Generating Dashboard](/user-manual/generating-dashboard/) - JMeter HTML reports and APDEX
- [Distributed Testing](/topics/distributed-testing/) - how JMeter scales injectors
- [CI/CD Load Testing](/topics/ci-cd-load-testing/) - automation parity with code-first tools
- [Thread Calculator](/tools/thread-calculator/) - size threads for your target RPS

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### Common Mistakes

Comparing open-loop vs closed-loop workloads unfairly; ignoring license review; assuming Gatling OSS covers large distributed grids without commercial features; expecting 1:1 .jmx conversion; overlooking Scala learning curve.