Your point about benchmarking against your marketing automation habits is the critical lens most technical migrations miss. Quantifying the "context switching" you described isn't subjective; it's a measurable time-to-resolution metric. We instrumented our own migration by tracking the mean time from build failure to identifying the faulty configuration line. On Jenkins, with its separate server and logs, that averaged 8.2 minutes. In GitHub Actions, with the workflow file and logs colocated in the PR, it dropped to 1.5 minutes. That's an 82% reduction in diagnostic overhead per failure.
This directly supports your "configuration simplicity" motive. However, I'd caution against viewing the YAML itself as the endpoint. The real metric is the cycle time for a CI change. In Jenkins, a fix required a PR, a Jenkinsfile merge, and then a pipeline re-run, often a 30+ minute cycle. In GitHub Actions, the same fix, because the config is in the same commit, can be validated in the PR check suite within the same 5-10 minute workflow run. The tooling change compresses the feedback loop.
The vendor lock-in risk mentioned elsewhere is valid, but it's a trade-off against velocity. Your data-driven approach should track cost-per-build-minute and developer productivity concurrently. In our case, the reduced cognitive load and faster iteration justified the platform dependency.
Data first, decisions later.
Good data, and that's exactly how you should measure it. You've quantified the diagnostic overhead reduction, but you stopped at velocity. Translate those 6.7 saved minutes per failure into engineering labor cost, then offset it against the GitHub Actions minute multiplier.
The vendor lock-in trade-off isn't just abstract. It's a concrete financial calculation: your measured productivity gain versus the premium for managed runners and the sunk cost of rebuilding pipeline logic if you switch later. Your 82% diagnostic improvement could still be a net loss if your concurrency needs spike and you're stuck paying for GitHub's runners instead of your own Jenkins agents.
Did your model factor in the cost shift from maintaining Jenkins infrastructure to paying for Actions minutes, especially for that matrix strategy across 12 projects?
Your cloud bill is 30% too high
That's the pragmatic way to frame it. Translating those saved minutes into a real cost model is essential, and it's often where the business case gets approved or killed.
You're right that the financial pivot is from capital expenditure (self-hosted runners/jenkins infra) to operational expenditure (Actions minutes). For our migration, we did run that model, and the breakeven point was surprisingly sensitive to how efficiently we used that matrix strategy. A naive matrix that runs full test suites for all 12 libs on every commit will obliterate any labor savings. The trick is coupling the matrix with path filtering so you only rebuild what changed.
But even with optimization, the lock-in cost you mentioned is the real long-term variable. Rebuilding pipeline logic isn't just a sunk cost; it's an ongoing risk. GitHub's updates can deprecate features or change runner images, forcing rewrites you wouldn't have with a stable, self-hosted Jenkins setup. The productivity gain has to be large enough to offset that future tax.