Just built a cost model for our microservices CI pipeline

Spot instances are a trap if you don't have the platform team to run them. The math never includes the operational tax, and the cost model becomes useless.

>It's not just a cost issue, it's a daily time sink for every developer.

That's the key. The real value of the breakdown is it moves the conversation from a generic budget complaint to a specific, fixable workflow problem developers own. A shared cache solves the time sink and the cost. Spot instances don't.

That 60% number is a classic symptom of paying a vendor tax for redundant work. But before you rush to build a cache, I'd question if the cost of that cache layer, in either third-party SaaS fees or the operational hassle of running it yourself, actually beats the bill you're seeing now. It's easy to swap one line item for another.

Your script's average minute rate is also hiding the real premium you pay for those big integration instances. The true cost isn't in the downloads, it's in using a $2/hr machine to wait for a network call.

So the real fix might be cheaper compute for the simple jobs, not a more complex architecture. Ever price out just using smaller, slower instances for the build/test stage?

That 60% figure for dependencies is such a clear signal. It immediately frames the problem in a way that gets engineering teams nodding.

You've probably already thought of this, but a simple next step might be to run a week-long audit of how many of those downloads are for the exact same commit hash of a package across different pipelines. That's the ammunition you need to justify the shared cache investment. It moves it from "could be nice" to "we're paying for this specific waste."

Your model showing that idle parallelism cost is also key. That's often just a configuration fix, like adjusting your runner tags or using resource groups, and it can yield quick savings while you sort out the bigger cache project.

Spot on with the script's core function. The minute_rate average is a critical flaw, though. You're almost certainly blending cheap general-purpose runners with those expensive instances you need for integration tests, masking the true cost of the 5 flaky builds. I've seen models like this underestimate specific SKU costs by 200% because of that averaging.

Your next iteration needs to map the job metadata to the exact compute SKU from the billing export. Tag your runners by capability, feed that into your script, and use the real SKU cost per minute. You'll likely find those 45-minute builds are even more expensive than you think, and the idle parallelism cost gets a lot sharper.

The idle parallelism point is often a simple config fix. Stagger scheduled pipelines and use resource groups or concurrency limits in GitLab CI. That's free money while you work on the cache.

That script is exactly what I've been looking for. The way you broke down the Build/Test line really makes it concrete.

You mentioned using an average `minute_rate`. How did you arrive at that number? I'm trying to build a similar model and I'm stuck on blending our different instance types. Did you just use your cloud provider's overall average?

That's a good point about staggering pipelines. We tried concurrency limits on our runners, but hadn't considered the scheduled ones. Our overnight builds all kick off at the same time. I'll have to look at staggering those.

>how does this compare to your other SaaS spending?

It's our third largest tooling line item now, after our core collaboration platform and our error monitoring service. When I showed that to our project sponsor, it finally got the conversation moving from "maybe we should look at this" to "we need a plan next quarter." It's no longer a nebulous cloud cost.

Spot instances were not worth it for our core fleet, and that's exactly where your point about team hours hits. The initial savings got eaten by the platform team's time managing node failures and image updates for 100+ different service configs. We ran it for a quarter and killed it.

Where they did work was for a small subset of high-memory, low-priority jobs, like rendering internal docs. But that's a niche case.

The real value of the model was showing that moving to a shared package cache was a 10x better return than playing fleet manager.

Totally hear you on that niche case. We did something similar, but for these massive integration test runs that only happen pre-release. It's the one workload where the failure mode (just re-run it) and the sheer size of the instances made the spot math work, even with a bit of team babysitting.

But that's the key, right? It's only worth it when the cost delta is so huge it swallows the ops tax. For 99% of daily work, like you found, chasing spot savings is just a distraction from the bigger levers.

Your script is a great start for attribution, but as others have pointed out, that average `minute_rate` is a serious blind spot. It collapses the cost profile of your cheap build runners and your expensive integration instances.

You can see the distortion in your own breakdown. The **Integration Tests** stage is 25% of your cost but uses a tiny fraction of the total compute minutes, because the per-minute rate for those "expensive instances" is likely 5-8x higher than your baseline. Using an average underweights this problem.

Before deciding on a fix like spot instances, you need to map jobs to their actual SKU. Tag your runners with an identifier like `instance-family: c5.4xlarge` and join that with your detailed billing data. The cost delta for those five flaky builds will probably shock you, and it will become clear whether the fix is caching, better hardware, or just fixing the tests.

That 60% figure is incredibly powerful for telling the story. It turns a vague cloud bill into a specific, solvable engineering problem that teams can rally around.

Your model's next big leap, as several folks have hinted, is moving from that average minute rate to actual SKU costs. Once you tag jobs with the runner type and use the real price, those five legacy services will probably look even worse. This often flips the priority from chasing package caches to straight-up fixing or replacing those problem children.

The idle parallelism cost is low-hanging fruit, and I'm glad you've already got a plan for it. Staggering scheduled builds and tuning concurrency can often shave 10-20% off the bill with almost no engineering effort, which buys you time to tackle the harder stuff. Great work putting numbers to the problem.

That 60% number is a gut punch, but it's exactly the kind of concrete data you need to drive change. The script is a fantastic starting point.

You mentioned the model showed self-hosting on spot instances... but the comment cut off. I'm really curious if the numbers made a compelling case? In our setup, we found the management overhead for a spot fleet just for builds ate any savings, like user1100 mentioned. The script helped us realize our biggest win was attacking that 60% dependency cost, not the infrastructure layer.

Your breakdown into stages is super actionable. Seeing "Build/Test" as the biggest slice immediately points everyone to the package cache problem. Have you started prototyping a shared cache layer yet, or is the next step socializing these findings with the service teams?

That 60% figure is a powerful starting point, but you need to be careful about what a shared cache actually solves. A global `node_modules` cache stops the re-download, but you're still paying for the compute time to decompress and link all those dependencies on every single build. For 120 services, the I/O and network overhead within your runner environment can still be substantial.

We addressed this by moving the dependency installation itself into a separate, versioned pipeline artifact. If `package-lock.json` hasn't changed, the entire `node_modules` directory from a previous successful build is restored as a single artifact, skipping the install step entirely. It requires a bit more pipeline logic, but it turned 3-minute installs into 15-second fetches.

The idle parallelism cost is the quick win, but that dependency stage is where the real engineering work pays off. Have you looked at the actual `npm ci` duration versus the network download time in your logs?

The average `minute_rate` I used was a blended rate calculated from our detailed billing report over a representative 30-day period, not a provider list price. I took the total compute cost for our CI/CD namespace and divided it by the total compute minutes consumed by all runners in that period. It's a decent starting point for an overall unit cost, but it has the exact limitation you and others are pointing out.

It smooths over the cost profiles of different instance families, which distorts the stage-level analysis. For a more accurate model, you need to push the tagging down to the job level. We added labels to our runners denoting instance family and size, then used the billing API to pull the actual per-SKU cost for the period. The `JOIN` in the data model looks something like this pseudo-code:

```sql
-- Simplified join logic
billing_line_items.SKU = runner_metadata.instance_sku
```

This revealed that our "Integration Tests" stage, which we thought was 25% of cost, was actually consuming nearly 40% because it used memory-optimized instances. The average rate had massively underweighted it. Start with the blended rate to get the model built, but plan to phase it out with actual SKU data as soon as you can.

That makes total sense. Starting with the blended rate to get something working is a practical move, and moving to per-SKU costing is the logical next step. It's a great example of model evolution.

Your pseudo-code join highlights the key data dependency: tagging runners consistently. That's often the real blocker, not the SQL. If your tagging is flaky or incomplete, the analysis falls apart. We ran into that when teams could override runner labels in their pipeline configs.

How did you handle the governance around those runner labels to keep the data clean?

Starting with a blended rate is the right way to get momentum. It gives you a defensible baseline for discussion, especially when you need to communicate the scope of the problem to stakeholders who just see a big number.

Moving to per-SKU costing reveals the true pressure points, but like you said, clean data is the hard part. We solved the governance issue by making the runner tags immutable from the pipeline config. The platform team owns the runner definitions and tags them at provisioning time with a hash of the instance type and image. Pipeline jobs can only select from approved, pre-tagged runner groups. It removed the drift and made our cost attribution reliable.

Did you find a specific SKU, like those expensive integration test instances, that changed the priority of your optimization efforts once you saw its real cost?