Hey everyone. I've been knee-deep in our team's CI/CD bill this quarter, trying to figure out if we're on the right platform or if we're burning cash on idle minutes. I stumbled across a few recent benchmark reports (Cloud Native Compute Foundation, some indie blogs) comparing the actual compute cost-per-minute for CI workloads across AWS, GCP, Azure, and even some niche providers.
The spread is... kind of wild? I expected some variance, but seeing it laid out with specific machine types (like `n2-standard-4` vs `m6i.xlarge`) and the different pricing models (per-second vs per-minute, sustained use discounts) really made it click. One thing that jumped out: the *assumed* cheapest isn't always, once you factor in network egress for artifacts and caching layers.
My immediate question: **has anyone run a real, recent cost comparison for their own pipeline?** Not just the list price, but the actual invoice line items. I'm particularly curious about:
- How much of your total minute consumption is actually active build vs. overhead (queuing, spinning up fresh runners, tearing down).
- Whether you've found a sweet spot with preemptible/spot instances for non-production flows, and how much that actually saves.
- If you've compared a managed service (like CircleCI, Buildkite) to a self-hosted runner setup on a cloud VM—where did the hidden costs pop up?
We're currently on a managed service but our parallel job count is creeping up, and the per-minute pricing is starting to feel heavy. The benchmarks are great, but I trust actual war stories more 😅. Maybe we can pool some data points?
Oh yeah, those hidden costs get you every time. I got bit hard a few years back when our artifact storage bucket was in a different region from our builders. The egress charges for pulling dependencies and pushing multi-gigabyte images added almost 30% to the compute line.
On your point about **active build vs overhead** - it's huge. We instrumented our Jenkins controllers and found nearly 40% of our billed "runner" time was just the agent waiting for the next job or provisioning. Switching to a lighter container-based agent image and implementing a more aggressive scale-down policy chopped that down to about 15%. The sweet spot for us was using spot for all dev branch builds and a small pool of on-demand for main branch commits. The key was making our pipeline stages idempotent so a spot termination just meant a restart, not a full failure.
Have you looked at your scheduler overhead? Sometimes the cheapest per-minute instance loses if your jobs spend 90 seconds just getting scheduled onto it.
it worked on my machine