StarSling vs Namespace runners for GitHub Actions

StarSling brings managed Ubuntu runners and continuous workflow improvement together, so your team can improve both execution and the work each job performs.

• 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.

Namespace provides namespacelabs/nscloud-cache-action for Cache Volumes and namespacelabs/nscloud-checkout-action for checkout caching. Enrolling in Cache Volumes also means disabling redundant built-in action caching.

When evaluating a switch, inventory those actions and their inputs alongside the runner label. Check which cache paths, checkout options and credentials the workflow relies on before preparing the trial.

Namespace lets you pause between steps, open an interactive terminal and inspect job metrics. Its breakpoint action also works outside Namespace runners.

Namespace uses remote builders by default, plus an in-runner local-cache option. Blacksmith provides persistent Docker layer caching through useblacksmith/setup-docker-builder and useblacksmith/build-push-action.

For a Docker-heavy trial, record where the build executes, which setup actions it needs, and how the cache is warmed. Include builder usage in your cost comparison. When moving back to an in-runner build, restore the Buildx and cache configuration your workflow needs.

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 job you are comparing.
• Prepare a small workflow PR. Review vendor-specific checkout and cache actions and restore appropriate GitHub Actions caching where the trial needs it. Preserve required check names, permissions and test commands.
• Run repeated cold-cache and warm-cache trials. Compare completed work, failures, queue time, execution time and billed usage. Use the GitHub Actions pricing guide to compare the cost of your actual workload.
• Keep the original workflow revision available. Roll back the trial by reverting both the runner label and any cache or checkout 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.

For new accounts, AI-powered optimization PRs are only available to customers on paid plans and are not enabled by default.