StarSling vs Blacksmith runners for GitHub Actions

StarSling helps you diagnose failures, improve workflows, run GPU tests, and define how agents review your code. These are concrete capabilities beyond running existing jobs on faster hardware.

• 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.

Blacksmith is a GitHub Actions runner provider, not a separate CI engine. Teams evaluating Blacksmith CI can keep their existing workflows and choose a runner with a runs-on label. StarSling uses the same GitHub Actions model, so a Linux evaluation can start without rewriting the pipeline.

Compare runner-minute rates and any cache-storage charges against your own job durations. Blacksmith offers macOS and ARM runner families; StarSling currently targets Ubuntu Linux, so this is not a replacement for every OS-specific job. StarSling adds failure diagnosis and runner-lifecycle timing commands, GPU Runners, configurable Review Runners, and CI optimization skills that work with your current runner provider.

For a migration, pick one repeatable Linux workflow first, preserve secrets and permissions, change its runs-on label, and compare several successful runs before moving additional jobs. Keep specialist operating-system jobs on compatible machines.

Blacksmith is a drop-in GitHub Actions runner replacement: you keep GitHub Actions and change the runs-on label to a Blacksmith runner label. Its instance families span Linux x64, Linux ARM, macOS, and Windows Server 2025 beta, with Linux instance types up to 32 vCPU.

For Docker builds, Blacksmith keeps Docker layer caches on per-Dockerfile sticky disks that persist between runs, billed per GB-month as an add-on. That changes the cold-cache problem: repeat builds can reuse those layers and skip rebuilding from zero.

That is runner and cache acceleration. It can make existing work start and run faster, but it does not remove work the workflow should not be doing. StarSling's axis is the agent layer: find cache misses, redundant installs, unsafe serial work, unsharded suites, and oversized runners, then open reviewable PRs across the whole workflow.

The difference between fast runners and agent-native CI is visible in the customer PRs. StarSling agents do not just run jobs on different hardware; they edit the workflow and hand the team a PR to review. These are published case-study windows, not head-to-head Blacksmith benchmarks; follow each case study for source method and caveats.

• Better Auth: agents fixed Turborepo cache configuration and cached Playwright browser installs. The published case study measures E2E at 2m 22s to 1m 04s and CI at 1m 40s to 1m 02s.
• Mastra: agents sharded E2E kitchen-sink across parallel jobs (PR #15888) as part of the workflow speedup; E2E measured 14m 16s to 10m 50s.
• Partcl: on synthesis-heavy CI, the agents right-sized runner classes and cut heaviest-job compute cost 13x per run while queue p95 fell from 9.5 minutes to 35 seconds.