StarSling vs Depot for GitHub Actions

Choose StarSling when you want to improve the whole GitHub Actions pipeline, from compute and workflow optimization to code review and GPU testing.

• 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.

Explore how to configure Docker CI on GitHub Actions, GitHub Actions alternatives.

Docker builds are often slow in CI because an ephemeral runner starts without the layers your last build produced. Depot changes that part of the system by running builds on remote BuildKit builders in the same private network as the runner, with a persistent NVMe layer cache on the builder. Depot Cache is a separate remote cache product for GitHub Actions, Bazel, Go, Gradle, Turborepo, and other build tools.

StarSling does not move your Docker build to a separate builder service. The build stays inside your GitHub Actions workflow on faster StarSling runners, and standard BuildKit cache backends still work. The agent layer is the difference: it can open PRs that fix Dockerfile ordering, cache scopes, install steps, and workflow structure instead of only making the existing build machine faster.

Depot is strongest when the Docker build itself is the bottleneck. StarSling's customer proof is broader: the agents have shipped PRs against caches, dependency installs, service readiness, test sharding, queue pressure, and runner right-sizing. These are published case-study windows, not head-to-head Depot benchmarks; follow each case study for source method and caveats.

• Better Auth: after migration and three agent PRs, E2E went from 2m 22s to 1m 04s and CI went from 1m 40s to 1m 02s.
• Mastra: agents replaced fixed sleeps, added healthchecks, sharded E2E, and migrated workflows; Combined store Tests measured 29m 56s to 5m 06s in the post-migration window.
• Partcl: on a synthesis-bound workload, the agents cut heaviest-job compute cost 13x per run and reduced p95 queue time from 9.5 minutes to 35 seconds.