CI/CD at scale: A performance analysis of CircleCI vs GitHub Actions

When evaluating CI/CD platforms, it can be easy to view them as commodities — interchangeable tools that accomplish the same basic tasks. But as development teams scale, small differences in platform performance can be compounded, significantly impacting development velocity and resource utilization.

To better understand these differences, we conducted a head-to-head comparison between CircleCI and GitHub Actions, focusing specifically on performance at enterprise scale. The results revealed meaningful distinctions in both job execution speed and queueing efficiency that can materially impact teams’ ability to ship code quickly and reliably.

Methodology

To ensure a rigorous and fair comparison, we designed our experiment around React’s codebase; an ideal candidate due to its multiple testing pipelines, steady contribution rate, and matrix of jobs that reflect real-world enterprise CI/CD complexity.

Key experimental parameters:

• Enterprise GitHub organization with 500 concurrent job capacity
• CircleCI configured with matching 500 concurrent job limit
• Private repository to eliminate public queue variance
• Script to replay 24 hours of commits in rapid succession
• Each commit triggered 6 workflows containing 184 total jobs
• Simultaneous pipeline execution on both platforms
• Comparable compute resources:
  • GitHub Actions: ubuntu-latest (2 CPU/7GB RAM) and linux-amd64-large (4 CPU/16GB RAM)
  • CircleCI: linux-large (4 CPU/15GB RAM)
• Daily test runs with metrics collected via API

This testing methodology was specifically designed to mirror the demands of modern enterprise development environments. Today’s leading technology companies typically operate with hundreds of developers across multiple teams, all continuously integrating code changes throughout the day. These organizations often run complex test suites and sophisticated CI/CD pipelines that can include hundreds of jobs per commit.

We chose React’s codebase as our test subject because it exemplifies these enterprise characteristics: multiple robust testing pipelines, matrix-based testing across different configurations, and complex interdependencies between jobs. By replaying 24 hours of commits in rapid succession, we simulated the high-throughput environment typical of enterprises running microservices architectures or maintaining multiple products.

Results

Our analysis revealed substantial performance differences between the platforms across two critical dimensions that directly impact engineering productivity and infrastructure costs.

Pipeline execution speed: Direct impact on developer productivity

When comparing CircleCI against GitHub Actions’ default runners (ubuntu-latest), we observed CircleCI executing pipelines 40.29% faster at the median. This speed advantage means developers spend less time waiting for builds and tests to complete, enabling them to ship features faster and maintain their flow state. For a team of 50 developers each making multiple commits per day, this difference could reclaim hundreds of engineering hours per month.

Even when matched against GitHub’s more powerful linux-amd64-large runners, CircleCI maintained a 2.09% speed advantage despite having slightly less RAM (15GB vs 16GB). This consistent performance edge with nearly identical resources suggests fundamental architectural advantages in how CircleCI executes jobs, translating to more efficient use of computing resources and lower infrastructure costs.

Queue management: The hidden multiplier of CI/CD costs

The most dramatic differences emerged in how each platform handled high-volume concurrent workloads.

CircleCI demonstrated superior resource utilization by immediately leveraging its full concurrent capacity, successfully initiating up to 500 jobs simultaneously across multiple workflows.

In contrast, GitHub Actions showed significant limitations that could severely impact development speed at scale. Despite having a 500-job concurrent capacity, it never exceeded approximately 124 concurrent jobs and limited processing to roughly 6 workflows simultaneously.

The performance bottlenecks on GitHub Actions created significant delays in workflow speeds, as queue times ballooned under increasing workloads. GitHub’s default runners saw median queue times exceeding 153 seconds, with maximum waits of over 22 minutes. The situation worsened with larger Actions runners, where median queue times exceeded 22 minutes and maximum waits stretched beyond 63 minutes.

In contrast, queue times on CircleCI remained consistently under 30 seconds (maximum 30.85 seconds), regardless of workflow size or overall system load.

To put these differences in business terms: CircleCI demonstrated 90.13% less queuing compared to GitHub Actions default runners, and 99.12% less queuing compared to larger runners. For a growing engineering organization, this queue management efficiency can mean the difference between shipping multiple times per day and being constrained to less frequent releases. It also has direct cost implications – when developers are waiting for builds, they’re not delivering value, and your CI/CD platform is consuming resources without producing output.

Performance under load: Scaling without surprises

Perhaps most importantly for growing organizations, our experiment revealed critical differences in how each platform handles increased load. CircleCI maintained consistent performance as demand grew, demonstrating effective load balancing and predictable queue times even under heavy use. This stability means engineering leaders can plan capacity with confidence and maintain consistent delivery velocities as their teams grow.

GitHub Actions, however, showed degrading performance under increased load, with queue times multiplying as workflow counts increased. This behavior makes capacity planning more challenging and can lead to unexpected delays in critical deployment pipelines. For organizations practicing continuous deployment or managing multiple teams, these delays can cascade into missed deadlines and frustrated developers.

Conclusion

As engineering organizations scale, the choice of CI/CD platform becomes increasingly critical. Our analysis demonstrates that CircleCI’s architecture provides material advantages in both execution speed and queue management - factors that directly impact development velocity and team productivity.

For teams looking to optimize their CI/CD performance at scale, we encourage you to explore these differences firsthand. CircleCI offers a free trial that allows you to benchmark performance with your own workflows and experience these benefits directly.