I am embarking on a project to evaluate and select a CI/CD platform for my organization, and one of our primary selection criteria is raw pipeline execution speed. I understand that performance can be highly subjective to context, so I am attempting to design a rigorous benchmarking methodology. My background is primarily in data visualization and reporting, so I am accustomed to comparing tools like Tableau and Power BI using controlled datasets and specific metrics; I am seeking a similar structured approach for CI/CD.
My goal is to create a comparison that could be useful for teams of roughly 10-15 developers, managing a monorepo with a mix of microservices (approximately 8-10 services). The pipeline patterns we need to evaluate include standard build, test, and deploy stages, with a focus on parallel execution capabilities and dependency caching.
To move beyond anecdotal evidence, I am considering the following controlled experiment, but I would appreciate feedback on its design and any potential pitfalls:
* **Test Codebase:** A standardized, public repository containing a simple web application in two languages (e.g., a Node.js and a Go service) with defined unit and integration tests.
* **Pipeline Definition:** Identical pipeline logic (e.g., `install dependencies -> build -> run tests -> build container image`) translated into the native syntax of each tool (GitHub Actions, GitLab CI, CircleCI, etc.).
* **Key Performance Indicators (KPIs):**
* **Wall-clock Time:** Total time from commit push to pipeline completion.
* **Queue Time:** Time spent waiting for an available runner.
* **Cost-Per-Execution:** Estimated cost for a single pipeline run, based on public pricing and the compute minutes consumed.
* **Cache Efficiency:** Measured by the reduction in build time on a subsequent, identical run when caching is enabled.
My primary questions for the community are:
* Are there established, open-source benchmarking suites or standardized "dummy" projects for this purpose that I am unaware of?
* Beyond the KPIs listed, what other quantitative metrics would provide meaningful insight into pipeline speed and efficiency?
* How significant is the variability introduced by the geographic region of the runners, and should I attempt to control for this by mandating a specific region (e.g., us-east-1) across all platforms?
* In your experience, what are the most common confounding variables when performing such comparisons, and how can they be mitigated?
I plan to compile the results into a dashboard for clear comparison, focusing on the variance in performance across multiple runs. Any guidance on ensuring the benchmark is fair and representative would be greatly appreciated.
That's a solid start. Your background in data viz actually gives you an advantage here - you're thinking about controls and metrics, which is exactly right.
One major pitfall with the standardized test repo approach is that it can miss the real-world overhead of scaling to 8-10 services. The biggest time sinks often come from dependency resolution across a monorepo and pipeline orchestration logic, not the raw build of a single service. I'd suggest adding a matrix build test to your suite that triggers builds for multiple services simultaneously, mimicking a PR that touches shared libraries.
Also, don't just measure wall-clock time. Track queue time (how long a job waits for an available runner) and cost per pipeline run. Sometimes a slower platform that never queues is actually faster in practice. I can share a simple script we use to parse timestamps from pipeline logs if you're interested.
terraform and chill
That queue time point is the only one that really matters. The rest is noise.
All the major platforms can run a fast build with a dedicated runner. The real benchmark is what happens at 4 PM when everyone pushes. You're paying for the performance they advertise, but you get the performance of their shared runner pool during peak load.
Forget the script. Just pick a Tuesday, run the same trivial job on three platforms at the same time, and see which one finishes last. That's your answer.
your mileage will vary
Your methodology is heading in the right direction, but your test codebase is too simplistic for a monorepo with 8-10 services. A simple web app in two languages won't capture the orchestration and dependency graph complexities you actually face.
Instead, your benchmark repo must include shared libraries with semantic versioning and dependent services that consume them. The critical test isn't building one service, but triggering a build for a shared library and measuring the time for all downstream services that depend on it to complete their integration tests. This exposes how each platform handles complex dependency caching and parallel fan-out.
Also, standardize the runner specs across platforms to a known VM size. If you don't control the hardware, you're just benchmarking someone else's commodity cloud, not the pipeline orchestration engine itself. Your final metric should be "total wall-clock time from commit to all services deployed, divided by total compute cost for that run."
show me the SLA