Alright folks, I’ve been down this rabbit hole for the last two weekends and I think I’ve finally nailed a repeatable method for comparing CI/CD platforms head-to-head. We all have our tribal preferences (GitHub Actions forever! 😉), but I wanted something more objective than "it feels snappier."
The core idea is to create a **benchmark pipeline**—a controlled, identical workload—that you can run on different platforms. This lets you measure what actually matters: execution time, cost, and configuration complexity. I’m focusing on three heavyweights: GitHub Actions, GitLab CI, and Jenkins (with a pipeline-as-code approach). The benchmark repo is a simple Node.js app with a realistic pipeline pattern.
Here’s the standardized workload I’m proposing for comparison:
* **Checkout:** Standard depth=1 clone.
* **Setup:** Installing Node.js (LTS version) via the platform’s native method.
* **Dependency Cache:** Restore `node_modules` using the platform’s caching mechanism.
* **Install:** Run `npm ci`.
* **Lint & Unit Tests:** Run `eslint` and `jest` (about 50 trivial tests).
* **Build:** Create a production `npm run build`.
* **Security Scan:** Run a trivial OWASP Dependency Check (simulated with a small script that sleeps to mimic a real scan).
* **Artifact Upload:** Package the build output and store it.
The key is to **mirror this exact sequence and tooling** across all three configs. You must also standardize the runner environment. For a fair fight, I’m using the closest equivalent machine specs on each platform’s hosted runners: 2-core Linux VMs.
Here’s the GitHub Actions job definition as an example baseline:
```yaml
name: Benchmark Pipeline
on: [push]
jobs:
benchmark-job:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: '20'
cache: 'npm'
- name: Install Dependencies
run: npm ci
- name: Lint and Test
run: |
npm run lint
npm test
- name: Build
run: npm run build
- name: Security Scan (Simulated)
run: ./scripts/simulate-scan.sh
- name: Upload Artifact
uses: actions/upload-artifact@v4
with:
name: production-build
path: dist/
```
For GitLab CI, you’d translate this into a `.gitlab-ci.yml` with equivalent `cache:` keys and `services`. For Jenkins, you’d write a declarative `Jenkinsfile` using the `node` directive and `stash`/`unstash` for artifacts.
Now, for the **measurement strategy**. You need to run each pipeline at least 5-10 times to account for runner variability. Capture these metrics:
* **Total Pipeline Duration:** From the commit push to the final artifact upload. Use the platform’s native timing info.
* **Queue Time:** How long the job waits for an available runner. This is huge for busy orgs.
* **Cost Calculation:** Use the platform’s pricing calculator. For GitHub, it’s free minutes on public repos but factor in private. For GitLab, look at pipeline minutes. For Jenkins, estimate the EC2/VM cost per minute for a comparable instance.
* **Configuration Lines of Code (LOC):** Count the non-comment lines in your config file. It’s a rough proxy for complexity.
My initial runs on a small demo repo showed some interesting trends! GitHub Actions had the fastest startup (almost no queue), but its caching story felt a bit slower to restore. GitLab’s caching was blazing fast once populated, but I occasionally hit longer queue times on the shared runners. Jenkins, of course, had zero queue time on my dedicated agent, but the setup and maintenance overhead is the real "cost" there.
What’s your experience? Have you tried a similar A/B/C test? I’m particularly curious if anyone has solid numbers on how these scale with monorepos or massive dependency trees. Let’s get some data on the table!
pipeline all the things