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Comparison
2026-06-29

Gatling vs k6: Load Testing Tools Compared (2026)

A deep, code-first comparison of Gatling and k6 in 2026: architecture, scripting, thresholds, CI/CD, Grafana observability, pricing, and when to pick each.

Gatling vs k6: Load Testing Tools Compared (2026)

Load testing has firmly moved into the developer's lap. The old model of recording clicks in a GUI, handing the script to a "performance team," and waiting a week for a PDF report is dead. In 2026 the two tools that define the modern, code-first approach to performance testing are Gatling and k6. Both let you describe load scenarios as version-controlled source code, run them in CI, and stream results into Grafana. Both are open source at their core, both have commercial cloud offerings, and both can comfortably push tens of thousands of requests per second from modest hardware.

But they are built on radically different foundations. Gatling runs on the JVM and asks you to write scenarios in a Scala, Java, or Kotlin DSL. k6 runs on a custom Go runtime and asks you to write scripts in JavaScript or TypeScript. That single architectural fork ripples through everything: how many virtual users a single machine can sustain, how you integrate with your existing test suite, how protocol support is extended, and which engineers on your team will actually enjoy maintaining the scripts.

This guide is a practical, side-by-side comparison aimed at engineers who have to choose. We will look at real runnable scripts for both tools, the threshold and SLO models, CI/CD wiring, observability with Prometheus and Grafana, reporting, the pricing of the cloud editions, and a clear decision framework for when each tool wins. If you are coming from an older tool, our k6 vs JMeter performance testing guide and the load testing for beginners guide provide useful background before you dive in here.

At a Glance: Gatling vs k6

Before the deep dive, here is the headline comparison. Numbers for virtual user (VU) capacity are per single load-generator instance with typical scenarios; real figures depend heavily on script complexity and payload size.

DimensionGatlingk6
RuntimeJVM (Java Virtual Machine)Go, single static binary
Scripting languageScala, Java, Kotlin DSLJavaScript / TypeScript (ES2015+)
Concurrency modelAkka actors, async non-blockingGoroutines, one per VU
VUs per instance3,000-5,000+ (CPU bound)30,000-50,000+ on ~512MB-1GB RAM
Memory per VUHigher (JVM heap, ~tens of KB-MB)Very low (~1-5KB per idle VU)
Install footprintJVM + Gatling bundle (~100MB+)One binary, no runtime dependency
Native protocolsHTTP, WebSocket, SSE, JMS, gRPC, MQTTHTTP, WebSocket, gRPC, browser (Chromium)
ExtensibilityJVM libraries, custom protocolsxk6 extensions (Go), JS modules
Built-in HTML reportYes, rich static reportSummary in terminal; HTML via handleSummary
Cloud productGatling EnterpriseGrafana Cloud k6
LicenseApache 2.0 (core)AGPL-3.0 (core)

A quick way to read this table: Gatling trades raw VU density for a mature JVM ecosystem and a beautiful out-of-the-box report, while k6 trades the JVM ecosystem for extreme efficiency, a familiar scripting language, and tight Grafana integration. Neither is universally "better." The right answer depends on your stack, your team, and the scale you need.

Architecture: JVM Actors vs Go Goroutines

Gatling's engine is built on top of an asynchronous, non-blocking core historically powered by Akka and Netty. A scenario does not map one operating-system thread to one virtual user. Instead, thousands of virtual users are multiplexed over a small pool of threads using an event-driven model. This is why a single Gatling instance can sustain several thousand active VUs without the thread-per-user explosion that sinks naive load tools. The cost is the JVM itself: heap tuning, garbage collection pauses under extreme load, and a larger memory baseline.

k6 takes a different route. Each virtual user is a goroutine running an isolated JavaScript VM (a Go implementation of a JS interpreter, not Node.js). Goroutines are extremely cheap, scheduled by the Go runtime over a small number of OS threads. An idle VU costs on the order of a few kilobytes, so a single well-provisioned machine can hold tens of thousands of VUs. There is no JVM and no garbage-collection cliff in the same sense. The trade-off is that the JavaScript runtime is not Node.js: you do not have npm modules, the file system, or native async/await the way you would in a Node service. You get a fast, sandboxed, single-purpose scripting environment.

This difference is the single most important thing to internalize. If you need 50,000 VUs from one box, k6 gets you there with less fuss. If you need rich JVM-side logic, shared Java libraries, or protocols like JMS that live in the Java ecosystem, Gatling is the natural home.

Writing a Test in k6

Let's get concrete. Here is a complete, runnable k6 script that load-tests a JSON API, applies checks, ramps load through stages, and defines thresholds that fail the run if the service is too slow or too error-prone.

import http from 'k6/http';
import { check, sleep } from 'k6';
import { Trend, Rate } from 'k6/metrics';

// Custom metrics
const loginDuration = new Trend('login_duration', true);
const errorRate = new Rate('errors');

export const options = {
  stages: [
    { duration: '30s', target: 50 },   // ramp up to 50 VUs
    { duration: '1m', target: 200 },   // ramp to 200 VUs
    { duration: '2m', target: 200 },   // hold at 200 VUs
    { duration: '30s', target: 0 },    // ramp down
  ],
  thresholds: {
    http_req_duration: ['p(95)<500', 'p(99)<1000'],
    http_req_failed: ['rate<0.01'],
    errors: ['rate<0.05'],
    login_duration: ['p(95)<800'],
  },
};

const BASE = __ENV.BASE_URL || 'https://test-api.k6.io';

export default function () {
  // Login request, measure it with a custom Trend
  const loginRes = http.post(\`\${BASE}/auth/token/login/\`, {
    username: 'load_user',
    password: 'superCroc2026',
  });
  loginDuration.add(loginRes.timings.duration);

  const ok = check(loginRes, {
    'login status is 200': (r) => r.status === 200,
    'token returned': (r) => r.json('access') !== undefined,
  });
  errorRate.add(!ok);

  if (ok) {
    const token = loginRes.json('access');
    const params = { headers: { Authorization: \`Bearer \${token}\` } };
    const listRes = http.get(\`\${BASE}/my/crocodiles/\`, params);
    check(listRes, { 'list status is 200': (r) => r.status === 200 });
  }

  sleep(Math.random() * 2 + 1); // think time 1-3s
}

Run it locally with:

k6 run script.js
# Override an environment variable
BASE_URL=https://staging.example.com k6 run script.js
# Run with a fixed VU count instead of stages
k6 run --vus 100 --duration 5m script.js

A few things to notice. The options object is declarative: stages describe the load shape, thresholds describe the pass/fail criteria. The check function records assertions but does not abort the iteration on failure (unlike a unit-test assertion) which is correct behavior for load testing where you want to keep generating load and measure the failure rate. Custom metrics like Trend and Rate let you measure exactly the slices of behavior you care about.

Writing the Same Test in Gatling (Java DSL)

Now the equivalent in Gatling. Modern Gatling supports Java and Kotlin in addition to Scala, which has made it far more approachable for teams without Scala experience. Here is the Java DSL version.

import io.gatling.javaapi.core.*;
import io.gatling.javaapi.http.*;

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

import java.time.Duration;

public class ApiLoadSimulation extends Simulation {

  String baseUrl = System.getProperty("BASE_URL", "https://test-api.k6.io");

  HttpProtocolBuilder httpProtocol = http
      .baseUrl(baseUrl)
      .acceptHeader("application/json")
      .userAgentHeader("Gatling/Load-Test");

  ScenarioBuilder scn = scenario("API load scenario")
      .exec(
          http("login")
              .post("/auth/token/login/")
              .formParam("username", "load_user")
              .formParam("password", "superCroc2026")
              .check(status().is(200))
              .check(jsonPath("$.access").saveAs("token")))
      .pause(Duration.ofSeconds(1), Duration.ofSeconds(3))
      .exec(
          http("list crocodiles")
              .get("/my/crocodiles/")
              .header("Authorization", "Bearer #{token}")
              .check(status().is(200)));

  {
    setUp(
        scn.injectOpen(
            rampUsersPerSec(1).to(50).during(Duration.ofSeconds(30)),
            constantUsersPerSec(50).during(Duration.ofMinutes(2)),
            rampUsersPerSec(50).to(0).during(Duration.ofSeconds(30))))
        .protocols(httpProtocol)
        .assertions(
            global().responseTime().percentile(95).lt(500),
            global().responseTime().percentile(99).lt(1000),
            global().failedRequests().percent().lt(1.0));
  }
}

Run it with the Gatling bundle or a build tool:

# With Maven
mvn gatling:test -Dgatling.simulationClass=ApiLoadSimulation -DBASE_URL=https://staging.example.com

# With Gradle
./gradlew gatlingRun --simulation=ApiLoadSimulation

The structural parallels are clear. Gatling's injectOpen profile is the analog of k6 stages; assertions are the analog of thresholds; check plays the role of k6's check. The difference is style: Gatling chains a fluent builder and uses an open workload model (rampUsersPerSec, where you control the arrival rate of new users) by default, which many performance engineers consider more realistic for modeling real traffic. k6 historically defaulted to a closed model (a fixed pool of VUs looping) but also supports open models via the ramping-arrival-rate and constant-arrival-rate executors.

Open vs Closed Workload Models

This distinction matters enough to call out explicitly because it changes the meaning of your results.

ModelWhat you controlReal-world analogRisk
Closed (fixed VUs)Number of concurrent usersA call center with N agentsCoordinated omission; slow responses throttle load
Open (arrival rate)New requests/users per secondWeb traffic hitting a public siteCan overwhelm a struggling system as designed

In a closed model, when the system under test slows down, your VUs wait longer per iteration, so they naturally issue fewer requests. This masks the true severity of a slowdown, a phenomenon called coordinated omission. An open model keeps injecting new arrivals regardless of how the system is coping, which is usually what you want when modeling internet-facing traffic. Gatling makes the open model the default and idiomatic choice. In k6, reach for the arrival-rate executors:

export const options = {
  scenarios: {
    constant_request_rate: {
      executor: 'constant-arrival-rate',
      rate: 1000,           // 1000 iterations
      timeUnit: '1s',       // per second
      duration: '5m',
      preAllocatedVUs: 200, // pool to draw from
      maxVUs: 1000,
    },
  },
};

If you take one practical tip from this article: for public-facing services, prefer an open/arrival-rate model in whichever tool you choose. It is the closest thing to honest traffic simulation.

Thresholds, SLOs, and Pass/Fail

Both tools let your load test act as a gate: green when performance SLOs are met, red when they are not. This is what makes performance testing a CI activity rather than a manual ritual.

In k6, thresholds live in options.thresholds and map metric names to expressions. A failed threshold makes k6 run exit with a non-zero status, which fails the CI job. You can also use abortOnFail to stop a doomed run early:

thresholds: {
  http_req_duration: [
    { threshold: 'p(95)<500', abortOnFail: true, delayAbortEval: '1m' },
  ],
  http_req_failed: ['rate<0.01'],
  checks: ['rate>0.99'],
},

In Gatling, the equivalent is the assertions block on setUp. Failed assertions cause a non-zero exit code and a clearly marked failure in the HTML report:

.assertions(
    global().responseTime().percentile(95).lt(500),
    global().responseTime().mean().lt(250),
    global().successfulRequests().percent().gt(99.0),
    forAll().failedRequests().count().lt(50L));

Both models are expressive enough to encode real SLOs. The k6 syntax is slightly more compact and the per-metric granularity (any custom Trend or Rate can have a threshold) is excellent. Gatling's assertion DSL reads naturally and the forAll() / details() scopes let you assert on individual request groups.

CI/CD Integration

Both tools are designed to live in a pipeline. Here is a GitHub Actions job for k6 using the official action:

name: Load Test
on: [pull_request]
jobs:
  k6:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run k6
        uses: grafana/k6-action@v0.3.1
        with:
          filename: tests/load/script.js
        env:
          BASE_URL: https://staging.example.com
      - name: Upload summary
        if: always()
        uses: actions/upload-artifact@v4
        with:
          name: k6-summary
          path: summary.html

And the equivalent Gatling job using Maven:

name: Gatling Load Test
on: [pull_request]
jobs:
  gatling:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-java@v4
        with:
          distribution: temurin
          java-version: '21'
      - name: Run Gatling
        run: mvn gatling:test -DBASE_URL=https://staging.example.com
      - name: Upload report
        if: always()
        uses: actions/upload-artifact@v4
        with:
          name: gatling-report
          path: target/gatling/**

The main practical difference: the k6 setup is leaner because k6 is a single binary with no runtime, while Gatling needs a JVM provisioned in the runner. If you already build Java in CI, the JVM is free; if your stack is otherwise JS or Go, k6 keeps the pipeline lighter. For a broader treatment of pipeline design, see our CI/CD testing pipeline with GitHub Actions guide.

Observability: Grafana and Prometheus

This is where k6 has a structural advantage in 2026: it is a Grafana Labs product, so streaming metrics into Prometheus and visualizing them in Grafana is first-class. You can output to Prometheus remote write directly:

K6_PROMETHEUS_RW_SERVER_URL=http://localhost:9090/api/v1/write \
  k6 run -o experimental-prometheus-rw script.js

From there a prebuilt Grafana dashboard renders p95/p99 trends, request rates, and error rates in real time. k6 also outputs to InfluxDB, Datadog, New Relic, and others.

Gatling is far from blind here. Gatling Enterprise ships its own real-time dashboards, and the open-source runner produces a rich static HTML report after each run with response-time distributions, percentile tables, and per-request breakdowns. For live streaming into Grafana, Gatling supports a Graphite/InfluxDB writer that you point at your time-series backend. The difference is that with k6 the Grafana path is the paved road, whereas with Gatling you either lean on its excellent built-in report or wire up the metrics writer yourself.

CapabilityGatlingk6
Built-in static HTML reportExcellent, automaticVia handleSummary, manual
Real-time Grafana dashboardGraphite/InfluxDB writerNative Prometheus remote write
Prometheus outputThrough InfluxDB/GraphiteFirst-class
Distributed run dashboardsGatling EnterpriseGrafana Cloud k6
Cloud-hosted resultsGatling EnterpriseGrafana Cloud k6

Reporting

Out of the box, Gatling wins the reporting beauty contest. After every local run it generates a self-contained HTML report with global and per-request charts, percentile tables, active-user timelines, and a clear pass/fail summary driven by your assertions. You can open it in a browser and hand it to a stakeholder with zero extra tooling.

k6's default output is a terminal summary. It is information-dense and great for CI logs, but for a shareable artifact you implement handleSummary to emit HTML or JSON:

import { htmlReport } from 'https://raw.githubusercontent.com/benc-uk/k6-reporter/main/dist/bundle.js';

export function handleSummary(data) {
  return {
    'summary.html': htmlReport(data),
    'summary.json': JSON.stringify(data, null, 2),
    stdout: textSummary(data, { indent: ' ', enableColors: true }),
  };
}

So both can produce a polished HTML report; the difference is that Gatling does it for free and k6 needs a few lines of glue (or the Grafana Cloud UI).

Protocol and Extensibility

Gatling natively speaks HTTP, WebSocket, Server-Sent Events, gRPC, JMS, and MQTT, and because it runs on the JVM you can pull in any Java library to support an exotic protocol or to generate test data. This makes Gatling especially strong in enterprise environments where JMS and other Java-native middleware are common.

k6 extends through xk6, a build system that compiles custom Go extensions into a bespoke k6 binary. There are official and community extensions for SQL, Kafka, AMQP, Redis, gRPC, browser automation, and more. k6 also ships a browser module (Chromium-based) so you can mix protocol-level load with real browser interactions, useful for measuring front-end performance under load. If you live in JS and want to script browser flows alongside API load, that integration is a genuine edge; our Playwright end-to-end testing guide pairs well with that workflow.

Pricing and Cloud

Both have a capable open-source core and a paid cloud tier for distributed runs, scheduling, team collaboration, and long-term result retention.

  • Gatling: open-source runner is Apache 2.0 and free. Gatling Enterprise (formerly Gatling FrontLine) is the commercial offering with distributed load generation, dashboards, scheduling, and trends. Pricing is subscription-based, typically quoted per organization.
  • k6: open-source binary is AGPL-3.0 and free. Grafana Cloud k6 (formerly k6 Cloud) provides distributed cloud execution, hosted dashboards, and integration with the rest of Grafana Cloud. It has a free tier with limited test runs and paid tiers that scale with virtual-user-hours.

For most teams the open-source editions are enough to run serious tests in your own CI. The cloud editions earn their keep when you need geographically distributed load generation, scheduled runs, historical trend analysis across releases, or you simply do not want to operate your own load-generator fleet.

When to Pick Each

Here is a blunt decision framework.

Choose k6 if:

  • Your team writes JavaScript or TypeScript and wants minimal context-switching.
  • You need very high VU density from limited hardware (tens of thousands of VUs per box).
  • You already run Grafana and Prometheus and want native, first-class dashboards.
  • You want a single static binary with zero runtime dependencies in CI.
  • You want to combine API load with real browser testing in one tool.

Choose Gatling if:

  • Your team is on the JVM (Java/Kotlin/Scala) and wants to reuse existing libraries.
  • You need protocols like JMS that live in the Java ecosystem.
  • You value a rich, automatic HTML report with no extra configuration.
  • You want the open workload model to be the idiomatic, default path.
  • You operate in an enterprise that already standardizes on JVM tooling.

For a wider comparison that brings JMeter into the picture as the legacy baseline, our k6 vs JMeter guide is the companion piece to this article. And if you are still deciding whether performance testing belongs in your pipeline at all, the load testing for beginners guide makes the case from first principles.

Frequently Asked Questions

Is k6 faster than Gatling?

In terms of virtual-user density, yes. k6's Go-goroutine model lets one machine sustain 30,000-50,000+ VUs on roughly 512MB-1GB of RAM, while a Gatling JVM instance typically tops out around 3,000-5,000+ VUs before CPU and garbage collection become limiting. For raw load per box, k6 is more efficient. Gatling can still generate enormous load by scaling out instances.

Can I write Gatling tests in Java instead of Scala?

Yes. Modern Gatling provides first-class Java and Kotlin DSLs in addition to the original Scala one. The Java DSL mirrors the Scala API closely, so you get the same scenario, injection, check, and assertion features without learning Scala. This has made Gatling much more approachable for JVM teams that have no Scala experience.

Does k6 support TypeScript?

Yes. As of recent releases k6 can run TypeScript directly, and you can also bundle TypeScript scripts with esbuild or webpack into k6-compatible JavaScript. You still target the k6 runtime, which is not Node.js, so npm packages that depend on Node APIs will not work, but pure-JS or pure-TS logic runs fine.

Which tool integrates better with Grafana?

k6, by a clear margin. Because k6 is a Grafana Labs product, it streams metrics natively via Prometheus remote write and ships prebuilt Grafana dashboards, and Grafana Cloud k6 hosts results directly. Gatling can write to Graphite or InfluxDB for Grafana visualization, but it is a configuration step rather than a paved road.

Are Gatling and k6 free to use?

Both have free, open-source cores. Gatling's runner is Apache 2.0 and k6's binary is AGPL-3.0; both run unlimited local and CI tests at no cost. Each also sells a commercial cloud edition (Gatling Enterprise and Grafana Cloud k6) that adds distributed execution, hosted dashboards, scheduling, and historical trend analysis.

Can either tool do browser-based load testing?

k6 has a built-in browser module based on Chromium that lets you script real browser interactions and measure front-end metrics under load, and you can mix this with protocol-level requests. Gatling focuses on protocol-level load (HTTP, WebSocket, gRPC, JMS, and more) and does not drive real browsers itself, so for combined browser-plus-API load k6 is the more direct fit.

How do thresholds in k6 compare to assertions in Gatling?

They serve the same purpose: turning a load run into a pass/fail gate. k6 thresholds attach expressions like p(95)<500 to any metric and exit non-zero on failure. Gatling assertions use a fluent DSL such as global().responseTime().percentile(95).lt(500) and also exit non-zero. Both can fail a CI job, so either works well as an SLO gate.

Should I migrate from JMeter to Gatling or k6?

If your team is JVM-centric and you value rich reports, Gatling is the most natural step up from JMeter. If your team prefers JavaScript, wants very high VU density, or already runs Grafana, k6 is the better target. Both are code-first and version-controllable, which is the main upgrade over JMeter's GUI-centric workflow. Our k6 vs JMeter guide covers the migration details.

Conclusion

Gatling and k6 are both excellent, modern, code-first load testing tools, and you will not go wrong with either for most workloads. The decision comes down to your stack and your priorities. Reach for k6 when you want JavaScript or TypeScript scripting, extreme VU efficiency, and native Grafana and Prometheus observability with a zero-dependency binary. Reach for Gatling when you live on the JVM, need protocols like JMS, want the open workload model as the default, and value an automatic, presentation-ready HTML report.

Whichever you choose, the real win is treating performance as code: scenarios in version control, SLOs as thresholds or assertions, and runs gated in CI. That is the practice that catches regressions before your users do.

Ready to add load testing and other performance skills to your AI coding agents? Browse the full catalog of QA and performance testing skills at qaskills.sh/skills and install the ones that fit your stack in seconds.

Gatling vs k6: Load Testing Tools Compared (2026) | QASkills.sh