Performance checked once a year
A big load test before launch, then nothing, while every release changes how the system behaves.
[ What k6 is, and why it matters ]
k6 is an open-source load testing tool from Grafana Labs. Tests are written in JavaScript or TypeScript, live in your repository and run from the command line, your CI pipeline or the cloud. Its engine uses little memory per virtual user, and built-in checks and thresholds turn performance goals into pass or fail results.
That makes performance part of everyday delivery, rather than a one-off project before launch.
Looking for load testing in general? See our load testing services or compare all our testing tools →
The problems it solves
A big load test before launch, then nothing, while every release changes how the system behaves.
Tests in a separate tool drift from the code and nobody on the dev team maintains them.
One slow service can drag down the rest, and it’s hard to see which one.
Tools that use a lot of memory per user make large tests expensive to run.
[ How it gives you an advantage ]
k6 wins on developer experience and CI. JMeter wins on protocol coverage. We’ll tell you honestly which fits.
| k6 | JMeter | Gatling | Locust | |
|---|---|---|---|---|
| How tests are built | JavaScript or TypeScript code | Visual builder, with Groovy scripting | Scala, Java or Kotlin code | Python code |
| Where tests live | In your repository, reviewed like code | XML test plans | In your repository | In your repository |
| Resource use per user | Low | Higher | Low | Moderate |
| CI pass/fail | Built-in thresholds | Through Taurus or plugins | Built-in assertions | Custom code |
| Protocols | HTTP, WebSockets, gRPC, browser; more via xk6 | Widest: HTTP, SOAP, JDBC, JMS, FTP, SMTP | HTTP, WebSockets and more | HTTP by default |
| Best for | Developer-led teams, load as code | Broad protocols, visual tool | High-scale tests from code | Python teams |
Reviewed, versioned and reused like the rest of your codebase.
Performance budgets that pass or fail your pipeline automatically.
More virtual users per machine, so large tests cost less.
APIs, gRPC, WebSockets, browsers and Kubernetes.
Honest note: browser-level testing in k6 is newer, and most k6 tests work at the API level. For SOAP, databases, JMS or FTP, JMeter is usually the better fit.
[ Where it can be used ]
Modern, API-driven systems built by teams who ship often. These are the cases where it pays off most.
REST, GraphQL and gRPC services tested alone and together.
Concurrent users, busy dashboards and multi-tenant traffic.
Chat, live updates and event streams over WebSockets or Kafka.
Performance budgets checked on every pull request or release.
Load generated inside your cluster or from Grafana Cloud k6.
Sales, campaigns and announcements tested at full scale.
Performance tests we run with k6
[ How we use k6 ]
Start with your busiest APIs and journeys, then add CI gates and full-scale runs. Each card lists what we deliver, when you need it and the problem it prevents.
Your APIs and services put under realistic traffic, with response times, error rates and throughput measured per endpoint and per service.
Before launches, scaling events and major API changes.
We load WebSocket connections and, through xk6 extensions, message systems such as Kafka, so real-time features hold up under pressure.
When chat, live updates or event streams are core to your product.
A small number of real browser sessions run alongside API load, so you see what users actually experience while the backend is under pressure.
When API numbers look fine but users still say it’s slow.
Traffic modelled with k6 scenarios and executors, such as arrival-rate and ramping users, for every test type you need, from a quick smoke test to a breakpoint run.
Whenever results need to reflect how traffic really arrives.
Smaller k6 tests run on every build in your pipeline, with thresholds that fail the build when performance drops below your agreed budget.
When performance needs checking on every release, not once a year.
When you need serious load, we run k6 in Grafana Cloud k6, across Kubernetes with the k6 Operator, in Docker, or on AWS and Azure load generators.
When you need thousands of virtual users or traffic from several regions.
Results stream to live dashboards linked to your servers and APM, so each slowdown is traced to its cause: code, database, configuration or infrastructure.
On every run, so results lead to fixes rather than just charts.
We convert JMeter plans or Postman collections into modular k6 scripts with shared libraries, and run old and new side by side until results match.
When you want load tests in code, reviewed like the rest of your repo.
[ Our approach ]
A load test is only as good as its model of real traffic. We get that right first, then turn your goals into thresholds your pipeline can enforce.
Real traffic, peak patterns and the APIs and journeys that matter most, from analytics, logs or your team.
You get: Workload modelLatency, error and throughput targets agreed with you, written as k6 thresholds.
You get: Agreed thresholdsServices, databases, queues and third parties, so we know what to monitor and where limits may be.
You get: Monitoring planModular scripts, shared libraries, scenarios and executors, and the right place to run them.
You get: Script blueprint[ Structure & tool setup ]
Each layer is chosen for your system and team. On the right is a typical project layout, so scripts stay easy to review and reuse.
perf/
scenarios/
checkout-peak.js
api-smoke.js
soak-overnight.js
lib/ auth.js checks.js config.js
data/ users.csv products.json
thresholds/ budgets.js
k8s/ k6-testrun.yaml
.github/workflows/
k6-smoke.yml
dashboards/ grafana-k6.json
Example only. Your layout follows your system and team.
[ How we work ]
You agree the budgets and test windows before any load is applied, and see results after every run.
Real traffic, peaks and key APIs turned into a workload model and budgets.
Modular k6 scripts with shared libraries, realistic data and scenarios.
Execution sized for your scale, dashboards linked to your systems.
Smoke and baseline first, then the agreed load, stress, spike or soak runs.
Each slowdown traced to its cause, with clear fix suggestions.
Smaller k6 checks added to CI, fixes retested, everything handed over.
[ Benefits you’ll see ]
Performance stops being a once-a-year project and becomes a check that runs with every release.
Thresholds in CI catch slower releases before they reach customers.
Each slowdown traced to code, database, configuration or infrastructure.
Scripts in your repository, in the language your team already writes.
Low resource use per virtual user means fewer load generators.
You know how much traffic your system handles, and what breaks first.
Open source, documented, and yours to keep.
[ Case Studies ]
These load testing projects were run with JMeter, not k6; they show our approach to workload modelling and bottleneck analysis. Each links to the full write-up.
[ Ways to work ]
A one-off test before a big date, a k6 engineer in your team, or performance gates on every release.
Not sure which fits? Find your model in four questions, compare all six or estimate your cost.
[ FAQ ]
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[ Contact us ]
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