If your engineering team ships fast, you're probably running hundreds — maybe thousands — of CI/CD jobs every month. Every pull request triggers a build. Every merge triggers tests. Every deploy triggers a pipeline. It all runs quietly in the background, and then one day someone in Finance asks why the "GitHub" or "GitLab" line item on the bill has tripled.
This is one of the most common — and most fixable — sources of hidden infrastructure cost we see when we assess a growing SaaS company's cloud setup. The good news: there is no single "correct" answer. There's a decision framework, and it depends almost entirely on your build volume, your team size, and how much engineering time you're willing to trade for savings.
This guide breaks down, in plain business terms, when it makes sense to keep using GitHub Actions or GitLab CI's own compute, and when it's worth shifting that workload onto AWS — plus the patterns companies use to make that shift without slowing engineers down.
Why this decision matters more as you scale
CI/CD compute is billed by the minute, and minutes add up fast as teams grow. GitHub and GitLab both price their hosted runners on a simple metered model — you pay per minute of build time, with multipliers for more powerful machines or non-Linux operating systems. That's convenient when your usage is small. It becomes expensive fast once your team is merging dozens of times a day across multiple repositories.
At the same time, running that same compute yourself on AWS can cost a fraction of the per-minute SaaS rate — but only if it's managed properly. Left unmanaged, a self-hosted setup can just as easily become its own cost and reliability headache. That's the tension every growing engineering org eventually runs into.
Option 1: Stick with GitHub Actions or GitLab CI's own compute
For a large share of companies, this is genuinely the right answer — not just the easy one.
Why it works well:
- Zero infrastructure to maintain. Runners are provisioned, patched, and scaled automatically by GitHub or GitLab.
- Predictable, simple billing for low-to-moderate usage, with free monthly minutes included in every plan.
- Fast to get started and low-risk for smaller teams without a dedicated platform engineer.
- Safer default for open-source or public repositories, where untrusted code from external contributors shouldn't run on infrastructure your company controls.
Where it starts to hurt:
As usage grows, the per-minute pricing model becomes expensive at scale — particularly for teams running frequent builds on non-Linux operating systems (Windows and macOS runners carry a significant cost multiplier), or teams running large test suites, matrix builds, or long integration pipelines many times a day.
Rule of thumb: if your organization is running a modest number of pipelines per day and doesn't have spare DevOps capacity, hosted runners are almost always the cheaper choice once you account for the engineering time it would take to run anything yourself.
Option 2: Move compute to AWS with self-hosted runners
This is where the real savings potential lives for teams with meaningful build volume. Instead of paying GitHub or GitLab per minute, you run the same jobs on your own AWS compute — and only pay AWS's underlying infrastructure cost, which is typically far lower per minute, especially when combined with spot instances (spare AWS capacity sold at a steep discount).
How much you actually save depends heavily on build volume and how well the system is architected. This isn't a "set it and forget it" switch; it's an engineering project with a real payback period. Measure first, then decide.
There are a few common ways this gets implemented.
Self-hosted runners on EC2, autoscaled with spot instances
The most common and cost-effective pattern. Instead of always-on servers, an autoscaling system spins up short-lived AWS servers only when a build is queued, runs the job, and shuts the server down immediately after — often using discounted spot capacity. This is the pattern behind most of the largest reported savings, because you're only ever paying for compute while it's actually doing work.
Kubernetes-based runner fleets
For larger engineering organizations already running Kubernetes, CI jobs can run as short-lived pods inside the existing cluster, with the cluster automatically adding and removing AWS compute capacity based on demand. This integrates CI/CD into infrastructure the team already operates and monitors, which can simplify long-term ownership.
Fully managed AWS build services
Rather than managing servers at all, AWS offers managed, pay-per-use build services (such as CodeBuild) that plug directly into GitHub or GitLab as a self-hosted runner target. This removes the operational burden of running your own fleet while still moving the cost basis onto AWS's lower infrastructure pricing — a strong middle ground for teams that want savings without owning a new piece of infrastructure.
Managed third-party runners inside your own AWS account
A newer category of specialized providers offers drop-in runners that deploy directly inside your company's own AWS account — giving you the cost benefits of self-hosting, the performance of dedicated hardware, and someone else operating the autoscaling logic. This is often the fastest path to savings for teams that want the benefits of self-hosting without building and maintaining the platform in-house.
So which one should you choose?
Here's the high-level framework we use with clients:
| Your situation | Likely best fit |
|---|---|
| Small team, low or bursty build volume, no spare DevOps capacity | Stay on GitHub Actions / GitLab CI hosted runners |
| Consistently high build volume across the org | Self-hosted compute on AWS (EC2 spot autoscaling or managed provider) |
| Heavy use of Windows or macOS runners | Strong case for moving those specific workloads to AWS or a managed alternative |
| Data residency, compliance, or "code never leaves our environment" requirements | Self-hosted on AWS, regardless of raw cost |
| Need for custom hardware (GPU workloads, high-memory jobs, ARM-native builds) | Self-hosted on AWS |
| Already running Kubernetes/EKS in-house | Kubernetes-based runner autoscaling fits naturally |
| Want savings fast, without building new infrastructure | Managed AWS build service or a third-party runner inside your own AWS account |
The single most important input is simple: how many build-minutes is your organization actually burning per month, and on what? Most teams have never measured this. It's usually the very first thing worth doing before any migration conversation even starts — and it often surfaces quick wins (smarter caching, cutting unnecessary matrix builds, right-sizing runners) that pay for themselves before you touch AWS at all.
The real cost isn't just the AWS bill
This is the part that gets missed in most "just switch to self-hosted" advice online: moving CI/CD compute to AWS is not free labor. Someone has to design the autoscaling, secure the runners, handle spot-instance interruptions gracefully, and keep the whole system patched and monitored. For a team running a large volume of builds, that investment pays for itself many times over. For a smaller team, the engineering time required can quietly erase the savings on paper.
This is exactly why most companies don't do this migration alone — and why it's worth treating as a proper infrastructure project with a clear cost model, not a weekend experiment.
How we help companies get this right
CI/CD cost optimization is one of the clearest, fastest wins inside our broader Cloud and DevOps work:
- Cloud infrastructure assessment — we start by measuring exactly where your CI/CD and broader cloud spend is actually going, before recommending any changes.
- CI/CD pipeline design and automation — whether that means optimizing your existing GitHub Actions or GitLab CI setup, or migrating compute to AWS with proper autoscaling.
- AWS cost optimization — identifying and eliminating unused or oversized resources across your entire cloud footprint, not just CI/CD.
- Scale-to-zero design — workloads that scale down automatically during idle periods, so you stop paying for compute you're not using.
- Security review — if self-hosting is the right call for your business, making sure it's implemented securely from day one.
For a sense of what that looks like in practice: we cut AIESEC Brasil's monthly infrastructure costs by ~95%, and shortened Conecta Gabinete's release cycles by 80%.
If your CI/CD bill has been creeping up and you're not sure whether the fix is a config change, a smarter pipeline, or a move to AWS, our DevOps consulting engagements start exactly there — measuring your actual usage and telling you honestly whether self-hosting makes sense at your scale.
