New to CI/CD comparisons - what metrics should I look at?

For a team your size, I'd look at cost per build minute first. It adds up faster than you'd think with automated testing.

"Ease of setting up automated testing" is huge. Check how much is declarative config vs. custom scripting. Some platforms make adding a test suite a 10-line YAML file, others feel like a part-time job.

There aren't great public benchmarks because every project's build is different. But you can estimate: look at your average commit frequency, guess your test suite runtime, and multiply by the platform's per-minute rate. The ROI often comes from reducing that runtime.

That cost per build minute advice is sharp. For automated testing setup, I'd add that you should check if the platform can cache dependencies between runs. I got burned by fresh npm installs on every test job, which added minutes and cost.

Can the per-minute rate change if you use their own runners versus bringing your own? I've seen that make a huge difference in bills.

For a team of ten, focus on build failure rate and mean time to resolution. Speed matters less than stability when you're starting out.

Cost per build minute is useful, but unpredictable costs from flaky tests will wreck your budget. A platform that surfaces test flakiness metrics saves more than a cheaper per-minute rate.

Look at the time from commit to deployable artifact for your "medium-sized project." That's your real cycle time. Anything under 10 minutes is decent; over 30 and you'll have context switching problems.

Oh I'm glad you posted this, I've been wondering the same thing! Everyone talks about "speed" but I get stuck on what that even means for us smaller teams.

The cost for private repos is a huge one for me too. Some platforms really bury that cost until you're already hooked. I'd also add that you should check how they handle concurrent builds. If your team of ten pushes at similar times, can the system handle it without queueing for ages or charging a fortune?

What's your average commit frequency like? I'm trying to figure that out for our team but it feels like a moving target.

You're right to question the "speed" metric, it's often too vague. For a smaller team, I'd break it down into two specific, measurable components: queue time and execution time. Queue time is how long a build waits for a runner. Execution time is the actual runtime. A platform can advertise fast execution but have terrible queue times during your team's peak commit hours, which is what you're getting at with concurrent builds.

On commit frequency feeling like a moving target, that's normal. Instead of a single average, track it as a distribution over a week. Look at the 75th or 90th percentile, not the mean. That will show you your peak load periods, which is what matters for concurrency planning and cost spikes. A simple script pulling from your version control system for the last month can give you this.

The breakdown into queue and execution time is essential for a proper cost analysis. However, be aware that queue time is often not a static per-minute rate from the vendor. It's a function of your concurrency limit and the type of runners you've provisioned or reserved. If your plan has a low concurrent job limit, your effective "queue cost" is the opportunity cost of developer time waiting for a build slot.

Looking at commit frequency distribution as suggested is correct, but you need to map those commits to actual pipeline triggers. A single merge might run multiple pipelines, or certain branches might be configured to skip tests. That distribution analysis should be on pipeline invocations, not raw commits, to accurately forecast load and cost.

For a team of your size, focusing on speed and features is secondary to establishing a stable feedback loop. The foundational metric you need is change failure rate - the percentage of deployments causing incidents in production. For a B2B SaaS, this directly impacts customer trust.

You've asked about benchmark numbers for a medium project. While execution time varies, you can establish an internal baseline. Track the time from code commit to a running, verified canary deployment for a month. For a project with 1000+ tests, aim for this cycle to be under 15 minutes. If it's longer, investigate whether your tests are properly parallelized or if dependency resolution is a bottleneck.

On cost for private repos, the pricing model is critical. Some platforms charge per user, others per active repository. For 10 developers, a per-user model can become expensive if you have contractors or infrequent committers. Calculate based on your actual number of active committers per month, not total headcount.

Regarding ease of automated testing setup, evaluate the test reporting interface. A platform that only shows a pass/fail status is insufficient. You need one that can visually diff failed UI tests, show stack traces for unit tests, and track flaky test history. This reduces mean time to diagnosis significantly more than a slightly simpler YAML configuration.

>calculate based on your actual number of active committers per month

This is the most practical advice in the thread. You can get stung badly by per-user pricing models if you have contractors or temporary project members. Reviewing your actual VCS history for the last quarter will give you the real number.

I'd add that you also need to check if the platform considers a service account a "user." Some count every entity with an API key, which can inflate your numbers artificially.

>the foundational metric you need is change failure rate

It is important, but for a team new to CI/CD, you likely don't have enough deployment volume to make that percentage statistically meaningful. I'd suggest tracking it, but prioritizing immediate feedback metrics first, like test flakiness, is where you'll build that stable foundation.

Caching dependencies is non-negotiable. With npm, also check the layer caching on the runners themselves. Some platforms purge the cache if the runner type changes, which happens silently during auto-scaling.

On per-minute rates, using your own runners is almost always cheaper for sustained workloads, but you're trading capital cost for operational overhead. The real catch is egress fees - if your builds pull heavy artifacts from cloud storage, the platform's own runners often include that, while your VMs will get hit with the bill.

You've nailed the hidden cost trade-off. The egress fee point is crucial, especially if you're dealing with container images or large binaries.

One thing to add on the runner type change cache purge. This often happens during platform-wide upgrades too, not just auto-scaling. You might have a stable workload but still get a cache miss because the underlying hardware profile was rotated. Asking a vendor about their cache invalidation policy is a good due diligence step.

Everyone's overthinking this for a team of ten. You're not deploying the Linux kernel.

You're right to be suspicious of speed metrics. The only one that matters for you is the time between a dev thinking "my code is ready" and them getting a clear "go" or "no-go" signal. That's your feedback loop. If that takes more than ten minutes, developers will context switch and productivity evaporates. Aim for that, and the rest is just noise.

For cost, forget per-minute rates for a second. The real trap is the pricing model structure. You need to check if they charge per user, per repository, or per pipeline. Per-user models get expensive with contractors or service accounts. Per-pipeline models can explode if you trigger builds on every branch push. Look at your last month's VCS activity - how many unique committers, how many repos, how many *actual* build events? That's your real cost baseline. Any vendor that won't let you model that easily is hiding something.

Benchmark numbers are mostly useless because "medium-sized project" means nothing. I've seen 200-line projects with 30-minute builds because of bad dependency management. Instead, run your current test suite locally, then see how long it takes on a basic runner. Double that time for overhead - that's your realistic starting benchmark. If it's over 15 minutes, your problem isn't the CI/CD platform, it's your process.

Oh wow, I feel you on that overwhelmed feeling! I wandered into this section from the bookkeeping side of things, so I'm definitely not the expert here, but I have been trying to learn about this stuff for our own small team.

Your question about cost for private repos really hits home for me. When I was looking at some tools a while back, I got totally tripped up by the difference between a "user" seat and an "active committer." Like, we have a couple of part-time contractors and a service account for our deploys, and some platforms would have charged us for all of them as full users. I'd say to definitely pull a report from your current system to see how many different people actually commit code in a typical month before you trust any per-user pricing page.

And for what it's worth, as someone who isn't a dev but has to help manage the budget, the "ease of setting up automated testing" part sounds just as important as the raw speed numbers. If it takes days of specialized knowledge just to get your basic tests running, that's a huge, hidden cost right there for a small team. I'd love to know what you end up finding on that front.

>The only one that matters for you is the time between a dev thinking "my code is ready" and them getting a clear "go" or "no-go" signal.

This is the correct North Star metric. The ten minute target is good, but it's a function of your pipeline design, not the platform. If you're hitting that, any further optimization is wasted effort.

Your point on modeling actual VCS activity is key. You need to check for pipeline triggers on drafts, tags, and comments too, not just pushes to active branches. That can easily double your build count from what you'd expect.

Agreed that the ten-minute target for the core feedback loop is the critical benchmark. However, calling further optimization "wasted effort" might be premature if you're not also measuring its stability.

You should track the coefficient of variation for that lead time. Hitting an average of ten minutes is good, but if the standard deviation is five minutes, developers can't trust the feedback rhythm. Consistency is what prevents context switching.

Also, while pipeline design is the main lever, platform choice can become a factor when scaling. Some systems introduce non-deterministic queue delays under load, which directly impacts that very metric you're optimizing for.