StarSling vs RunsOn runners for GitHub Actions

Choose StarSling for managed GitHub Actions compute and agents that improve the workflow, with runner operations handled for you.

• Give each job faster compute: Adaptive Compute combines Sandbox Router's placement on the fastest available sandbox with Afterburner's extra CPU and RAM during a running step, at no extra cost.
• Improve the work your pipeline performs: StarSling agents deliver reviewable optimization PRs for workflow improvements. Inspect the changes in real repositories and merge the ones that fit your team.
• Turn CI findings into fixes: ci-speedup, ci-score, and ci-secure give your coding agent workflow-specific findings and fixes for speed, configuration, and security, applying CI optimization best practices.
• Own your code review: Review Runners run your chosen model, repo-native skills, scripts, and team-defined reviewers. Version review logic alongside your code.
• Give GPU tests their own hardware: GPU Runners provide dedicated NVIDIA GPUs for GPU tests, CUDA builds, and model evaluations, keeping CI off your production GPUs.
• Diagnose failures and wasted time: the sling CLI explains failed jobs with evidence and next steps and breaks wall-clock time into runner-lifecycle phases.

In RunsOn's public GitHub Actions runner benchmark of Rust and TypeScript builds on x64 runners at 8 to 12 vCPU (p90 wait + job time, September 30 to October 4, 2026), StarSling ranked first on both builds with Rust 1m 54s and TypeScript 1m 07s. RunsOn recorded Rust 2m 02s and TypeScript 1m 14s.

Each figure is a provider's best runner in that size range, on a cold Rust build and a cold TypeScript build. RunsOn runs the benchmark and sells one of the runners it measures. Its harness and raw results carry all nine providers, and our write-up walks through them.

RunsOn deploys into your AWS account from a CloudFormation template or its Terraform module, which brings up the VPC, cache bucket, IAM roles and scheduler together. A GitHub App on your organization registers an ephemeral runner when a job asks for one, and the instance ends with the job.

A job selects its machine through the runs-on label, which RunsOn treats as a query: constraints such as CPU count, instance family and image are resolved when the instance launches. RunsOn tries spot capacity first and falls back to on-demand.

RunsOn charges a flat annual license per legal entity, covering the GitHub organizations and AWS accounts you own or control. The EC2 instances, S3 cache bucket and EBS volumes are billed by AWS to your account, with compute billed per second. A free license covers nonprofit, open-source, educational and personal non-commercial projects.

StarSling bills per runner minute, from $0.004/min (2 vCPU) to $0.128/min (64 vCPU), with queue time never billed and no seat fees. Use the GitHub Actions pricing guide to model your own workload, and Self-hosted GitHub Actions runners for the cost of running runners in your own cloud.

Choose a representative Ubuntu job with a clear success condition. Keep the same commit, tests and build outputs across the comparison, and record queue time separately from execution time.

• Install the StarSling GitHub App for the trial repository, then choose a label from StarSling runner sizes. The default starsling-ubuntu-26.04 selects 4 vCPU; match the size to the RunsOn instance you are comparing.
• Prepare a small workflow PR. Where the job uses RunsOn label options such as extras=s3-cache, restore GitHub Actions caching in the same PR. Preserve required check names, permissions and test commands.
• Run repeated cold-cache and warm-cache trials. Compare completed work, error rate, queue time, execution time and the full cost: the runner minutes on one side, the license plus the AWS bill on the other.
• Keep the original workflow revision available. Roll back the trial by reverting the runner label and any cache edits together.

Read the public Mastra optimization PRs and results and Better Auth case study to see changes to real workflows, the measured windows and the work behind each result. Use those examples to identify a testable improvement in your own pipeline.

Optimization PRs are on paid plans, off by default for new accounts.