For many DevOps and platform teams, CI/CD costs have started to feel a lot like cloud bills: vital to operations but often opaque and frustrating when they come in higher than expected. When finance leadership asks “why is our CI spend increasing?”, engineers need to be ready with answers grounded in data, strategy, and efficiency.
This guide is your actionable checklist for minimizing CI/CD spend while maintaining (and hopefully accelerating) delivery velocity. It’s built from lessons learned auditing pipelines, right-sizing compute, and scaling developer productivity without runaway costs.
Speed up your builds and tests
Use smaller base images
Most DevOps shops use containerized environments, and those base images set the tone for speed and cost. Large images (like full Ubuntu or Node distributions) significantly increase startup time, bandwidth usage, and local caching requirements. A good best practice is to opt for minimal base images like alpine or stripped-down language-specific containers (e.g., python:3-slim). Only add what you need. Over time, this reduces pull times, shortens job spin-up, and cuts network costs, especially if you’re running hundreds or thousands of jobs per week.
Standardize environments
Custom environments create variance, which in turn introduces friction and flakiness. Each unique config adds time to debug and demands more caching and memory. Instead, try to use shared base Docker images with predictable tooling and define consistent runner types in order to limit environmental drift across teams. This enables better caching, more efficient provisioning, and faster onboarding.
Cache dependencies
Downloading dependencies for every job run is one of the most unnecessary drains on both time and money. Whether you're using npm, Maven, or pip, these libraries don’t change often. To avoid this, set up proper dependency caching in your CI system (e.g., using restore_cache and save_cache in CircleCI, or GitHub Actions' cache action). This can reduce build times by 50–90% depending on the project and eliminate redundant spend.
Cache intermediate build results
For languages like Go, Rust, and Java, intermediate build files (object files, compiled classes) can be reused between job runs. Recompiling the same code every time wastes compute and developer time, so set up intermediate artifact caching to allow for faster rebuilds, especially in monorepos or large projects with multiple build stages.
Optimize test execution
A full test suite is great, until it's run on every commit. That’s overkill for most workflows. Take a smaller, modular approach instead: run smoke tests on every commit; run full suites on merge or deploy events; split tests to parallelize across runners; and skip unchanged modules by leveraging intelligent test orchestration. This avoids overloading your infrastructure and reduces cost per test run dramatically.
Use the right infrastructure for the job
Bigger isn’t always better
It’s common to default to the biggest runners available “just to be safe,” but this leads to chronic underutilization. Fix this with a quick analysis by logging CPU and memory usage for all job types, then identify low-usage jobs consuming large runners, and then assign appropriately sized instances per job profile. A simple reallocation can save thousands of dollars per month, especially in a busy org running hundreds of jobs daily.
Automate scaling
If your team works mostly 9-5, you likely don’t really need 24/7 high-capacity runner fleets. Idle runners = wasted money. Focus instead on:
- autoscaling runners (e.g., GitHub Actions + self-hosted autoscaling runners)
- scheduling scale-downs during off-peak hours
- leveraging burstable instances in cloud CI providers
Audit job runtime and failure patterns
Frequent retries, long-running builds, and flaky tests cost real money by wasting compute and engineer attention. Generate a report showing median job durations by workflow, failure rates by job, and retry frequency. This creates a case for eliminating low-value jobs, re-architecting slow steps, or investing in more stable testing libraries.
Keep storage and transfer under control
Enforce retention policies
Artifacts, cache files, and logs tend to accumulate and are rarely cleaned up unless there’s a crisis. Your policy should include automatic deletion of artifacts after 7-30 days (depending on build patterns), limited cache persistence based on branch or PR activity, tiered storage if available (ex. cold storage for historic logs). Don’t store what you’re not reading. It adds up.
Fix the way your pipelines are built
Modular pipelines via microservices
Monolithic pipelines can be slow, brittle, and expensive. With microservices, you can isolate changes to only affected components, parallelize builds and tests, and deploy smaller units faster. This minimizes wasted pipeline time and increases developer throughput.
Shift left on quality and security
CICD is often your best line of defense for catching regressions, bugs, and vulnerabilities. The earlier you catch them, the cheaper the fix. Embed tools like static code analyzers, dependency vulnerability scans, and linting and style enforcement. These checks are low-cost but high-impact when compared to hotfixes or security breaches in production.
Know when your CI is slowing you down
Run a routine ‘dial tone’ job
This is a simple "canary" CI job that runs a rudimentary test job. It's a fast, cheap way to detect baseline issues in your CI infrastructure. If this job is slow, failing, or queuing too long, you know the issue isn't your code: it's the CI provider or runner fleet. You can alert on high latency or lack of execution to detect outages early.
Identify performance regressions
Sometimes builds get slower and no one notices until it’s too late. Prevent this by tracking average build duration by commit, alerting on significant variance, and correlating performance dips with code changes. This makes it easy to revert problematic commits and maintain pipeline velocity.
Monitor infrastructure-level metrics
According to Datadog’s 2024 DevOps Report, 63% of pipeline failures stem from resource exhaustion. Key metrics to monitor include:
- CPU and memory usage per runner (alert if >80% for 5+ minutes)
- Network latency between services (<50ms is ideal)
- Disk I/O throughput on build servers
Without infrastructure monitoring, CI/CD failures become harder to diagnose and fix, making them more likely to cause extensive downtime.
Review your usage before someone else does
Perform regular CI cost audits
Once a month, set a standing meeting for you and your team to review usage and cost data from your CI provider (e.g., GitHub Actions usage minutes, CircleCI credits, etc.). Look for spikes in job volume, high-cost workflows, and inefficient caching or artifact use. This equips you to get ahead of finance when problem areas arise and proactively target any areas for optimization.
Set automated alerts
Most CI platforms let you alert on jobs that exceed a resource/time threshold, failed workflows above a % rate, and unusually high credit usage. Don’t wait for finance to flag an overage. Be proactive and build alerts into your observability stack.
Visualize and share the data
Create dashboards that show cost per workflow or per team, job durations and trends, and top failing or retry-heavy jobs. These visualizations make it easier to explain CI investments and trade-offs to stakeholders who aren’t engineers.
Investigate 3rd party build accelerators
Send the jobs to a specialist
Using your CI’s default runners (if you’ve exhausted any free credits) can be surprisingly slow and expensive compared to new vendors that can do it faster and cheaper than your CI. Investigate sending those jobs through a 3rd party runner provider (we have a somewhat biased view that Depot is a great choice) that can run those jobs faster and typically at half the cost of your CI.
Maintenance is sneaky expensive
There's no doubt that self-hosted runners are, at face value, one of the cheapest ways to run your jobs. But don't discount the person-hour cost of keeping a self-hosted runner in good shape, adding failback/availability/scalability, debugging during downtime etc. Trusting those runners to a 3rd party eliminates the bulk of that work.
Lost productivity is a cost too
Speaking of downtime, consider the cost of 20 devs waiting a few hours for your team to get, say, Jenkins working again after an outage. Those devs might get some work or documentation done, but some might just mentally check out after the first hour. And even a slow build isn’t without its drawbacks: if a pipeline takes more than 5 or 6 minutes, it’s not uncommon for an engineer to context switch a bunch and lose 20 minutes to socializing, snacking, walking the dog etc. Working with a 3rd party runner provider who obsesses over runner uptime and performance can eliminate much of that downtime risk.
Last words
CI/CD can be viewed as a cost center, but it's also an incredibly important strategic enabler. By auditing your pipelines, right-sizing infrastructure, enforcing data discipline, and building observability into your workflows, you can dramatically reduce cost while increasing developer velocity.
The best optimizations deliver both lower costs and faster builds. Most teams can cut their CI spend in half just by switching to more efficient infrastructure, and that's before factoring in productivity gains from developers who aren't waiting for slow builds.
When the CFO asks about CI spend, your response shouldn't just be "we're working on it." Using this checklist, you should be able to confidently explain "here's what we've done, here's what we've saved, and here's how we're scaling efficiently."
Related posts
- DevOps to finance: Explaining CI costs to your CFO
- How slow builds are costing you money
- Build Docker images faster using build cache
- Now available: Depot ephemeral registries
- Building Docker Images in CircleCI with Depot
