Agent-native CI

StarSling is AI-native CI for GitHub Actions: drop-in Ubuntu runners plus agents that open optimization PRs for your workflows. The runners replace ubuntu-latest and ubuntu-24.04 to make GitHub Actions up to 6x faster in public customer examples and up to 33% cheaper than equivalent GitHub-hosted runners, and the agents continuously analyze your workflows to keep the pipeline improving.

Agent-native CI is CI that improves itself. Beyond running your workflows on faster hardware, AI agents continuously analyze your jobs, run logs, and machine telemetry, then open pull requests that optimize your pipeline: caching, parallelization, dependency installs, test sharding, and workflow structure. Traditional CI is static; agent-native CI gets faster the longer you use it. Read more about AI-native CI.

Two things move the needle: faster hardware and removing redundant work. Swap ubuntu-latest for a runner with stronger CPUs (StarSling public customer examples show up to 6x faster CI), then let AI agents find dead steps, cache fixes, and parallelization opportunities your team hasn't had time to fix. For a deeper guide, read fast GitHub Actions. You can also work through the GitHub Actions CI best practices yourself, without StarSling, or follow Levels of CI for every way to make GitHub Actions cheaper and faster.

Usually the runner. GitHub-hosted ubuntu-latest runs on older shared hardware, so even simple builds spend time waiting on CPU. Switching to faster CPUs (StarSling Runners use 5th Gen AMD EPYC) typically cuts wall-clock time significantly before any workflow optimization.

Yes. Change runs-on: ubuntu-latest to runs-on: starsling-ubuntu-26.04 (or ubuntu-24.04 to starsling-ubuntu-24.04) in your GitHub Actions workflow files. No other config changes required. See StarSling vs GitHub Actions and GitHub Actions alternatives.

StarSling is not a full replacement for GitHub Actions. It is a GitHub Actions runner alternative for supported Ubuntu/Linux jobs: you keep GitHub Actions workflows and change the runner label. If you are comparing full CI/CD platforms, read GitHub Actions alternatives.

$0.008 per minute for 4 vCPU Linux runners with 16 GB RAM, 33% cheaper than GitHub Actions' $0.012/min. Queue time is never billed. New accounts get 2,000 free minutes the first month.

Yes. StarSling GPU Runners are drop-in GitHub Actions runners backed by a dedicated NVIDIA GPU: RTX PRO 6000 at $0.05922 per minute, RTX 5090 at $0.03022, and RTX 4090 at $0.02522, with H100 coming soon. Set runs-on: starsling-ubuntu-26.04-gpu and pick a card in the label. The GPU is dedicated to that job for as long as it runs, so nothing reclaims it partway through your suite. Explore StarSling GPU Runners.

RTX PRO 6000 is the default, and it suits large-model and memory-heavy CI workloads. RTX 5090 suits fast inference tests and model evals. RTX 4090 covers everyday GPU tests at the lowest per-minute cost. You choose per job in the runs-on: label, so different jobs in the same workflow can use different cards. Explore StarSling GPU Runners.

Yes. StarSling Review Runners are code review agents you configure and run in your own CI. One workflow file, and every pull request is analyzed as a GitHub Actions job on a StarSling runner using your model key, review skills and scripts from your codebase, and team-defined reviewers. Skills and instructions load from the trusted base branch, so a pull request cannot rewrite its own reviewer. The output is a review comment on the pull request, before a human opens it. Explore StarSling Review Runners.

A review is a GitHub Actions job on a StarSling runner, billed by the minute in the same account as the rest of your CI. It runs on starsling-review, a 4 vCPU runner with 16 GB of memory, at $0.004 per minute, from job start to finish, rounded up to the nearest minute. Queue time is never billed and concurrency is unlimited. Model tokens are billed by your own provider, on the account you already have. Explore StarSling Review Runners.

After installing the StarSling GitHub App, AI agents continuously analyze your workflows, jobs, run logs, and machine telemetry, then open pull requests that optimize your setup: cache fixes, faster install strategies, Docker health checks, test sharding, parallelization, workflow structure, and job ordering. You review and merge. Optimization PRs are on paid plans, off by default for new accounts.

Coding agents (Claude Code, Cursor, Codex) push PR volume up several-fold, which compounds CI cost and queue times fast. The right fit is a runner with unlimited concurrency, continuous AI optimization (so the pipeline gets faster as PR volume grows), and per-minute pricing with no seat fees. StarSling Runners are built for this pattern.

Yes, but failed jobs typically exit quickly so the cost is minimal. Queue time is never billed, and the 2,000 free minutes give you room to experiment.

Each job runs in its own single-use, hardware-isolated microVM that's destroyed after the run. That's the same isolation model GitHub-hosted runners use, so there's nothing for a later job or a fork PR to persist on or reach. Secrets pass directly from GitHub to your job and are never stored by StarSling. More details in our docs.