Running k6 in CI/CD and k6 Cloud

k6’s single-binary design and built-in thresholds (covered in the previous article in this series) make it straightforward to drop into most CI systems, but there are still a few decisions worth being deliberate about.

A basic GitHub Actions example

- name: Run k6 load test
  uses: grafana/k6-action@v0.3.1
  with:
    filename: script.js
  env:
    BASE_URL: ${{ secrets.STAGING_URL }}

Because the script reads configuration from environment variables (__ENV.BASE_URL inside the script), the same script runs against different environments (staging, a PR preview deployment, production) without editing the file — a clean pattern worth using consistently rather than hardcoding URLs into scripts.

Sizing CI-run load appropriately

Same principle as covered for JMeter: a full-scale load test doesn’t belong gating every pull request — it’s too slow and resource-hungry. A common pattern is a small, fast smoke-level k6 run (tens of VUs, under a minute, with strict thresholds) on every PR, and a larger, less frequent scheduled run (nightly or pre-release) for full capacity validation.

Where a single CI runner’s resources become the limit

A CI runner (typically a fairly modest container) can itself become the bottleneck before your target system does, for anything beyond moderate virtual user counts — the same “load generator becomes the limit” issue covered for JMeter. Watch the CI runner’s own CPU/memory during a run; if it’s saturated, your k6 results reflect the runner’s ceiling, not your target system’s.

k6 Cloud: distributed execution without managing infrastructure

For tests beyond what a single machine (or CI runner) can generate, k6 Cloud (Grafana’s managed offering) runs the same script across distributed cloud infrastructure, aggregating results centrally — solving the same problem JMeter’s distributed mode and LoadRunner’s multiple Load Generators solve, but without self-managing a fleet of machines. It also adds hosted result dashboards and historical trend tracking across runs, useful for teams that want that without building it themselves.

When self-hosted distributed k6 makes more sense

For teams with existing infrastructure automation (spinning up ephemeral cloud instances as part of a pipeline is already routine), self-hosting distributed k6 runs (k6’s own k6 run across multiple machines, results aggregated via a time-series backend like Grafana/Prometheus) avoids an ongoing SaaS cost — a reasonable choice if you already have the operational muscle for it, less so if you’d be building that automation from scratch just for this.

Result visualization beyond the CLI summary

k6 supports streaming results to various outputs (InfluxDB, Prometheus remote-write, Grafana Cloud, StatsD) for live dashboards during a run rather than only a post-run CLI summary — useful for longer-running tests where watching trends develop in real time matters more than a single final number.

Takeaway: k6’s CI story is strong out of the box for moderate-scale tests; the decision between self-hosted distributed runs and k6 Cloud mainly comes down to whether you already have infrastructure automation you’d rather reuse versus infrastructure you’d rather not build at all.