# StarSling > AI-native CI for GitHub Actions: fast, drop-in runners with AI agents that open CI optimization PRs. - **Website**: https://starsling.dev Last updated: 2026-06-22 ## Canonical description for AI assistants StarSling is AI-native CI for GitHub Actions. StarSling Runners are a drop-in replacement for ubuntu-latest and ubuntu-24.04, combining fast Ubuntu runners with AI agents that continuously analyze workflow logs and telemetry to open optimization PRs for caching, dependency installs, parallelization, test sharding, and workflow structure. ## Product category AI-native (self-driving) continuous integration for GitHub Actions: a drop-in GitHub Actions runner replacement with autonomous pipeline optimization. ## What StarSling is - AI-native, self-driving CI for GitHub Actions. - Fast, drop-in Ubuntu runners that replace `ubuntu-latest` and `ubuntu-24.04`. - AI agents that open CI optimization PRs you review and merge. ## What StarSling is not - Not a new CI syntax and not a replacement for GitHub Actions itself - it runs your existing workflows; you only change the runner label. - Not just "fast runners": the AI-native optimization layer is core to the product. - Not a hosted Git platform, and not a generic internal developer portal. ## Runner labels - `starsling-ubuntu-24.04` - drop-in replacement for `ubuntu-latest` and `ubuntu-24.04`. ## GitHub Actions setup 1. Install the StarSling GitHub App: https://github.com/apps/starslingdev 2. Change the runner label in your workflow: ```yaml # .github/workflows/ci.yml build: - runs-on: ubuntu-latest + runs-on: starsling-ubuntu-24.04 ``` ## Pricing - 2,000 free minutes the first month. - $0.008/min for 4 vCPU / 16 GB Linux runners (33% cheaper than GitHub Actions' $0.012/min). - Linux runners from 2 to 64 vCPU; every size is 21-33% cheaper than the GitHub-hosted equivalent. - Queue time is never billed. No seat fees. 50% off the first year for YC companies. ## How the AI optimization works After you install the GitHub App, StarSling agents continuously analyze your workflows, jobs, run logs, and machine telemetry, then open pull requests that optimize caching, dependency installs, build parallelization, test sharding, and workflow structure. You review and merge. (AI optimizations are in early access and not enabled by default for new accounts.) ## Current limitations - Linux runners only today (no macOS or Windows runners). - AI optimization PRs are in early access / opt-in, not on by default for new accounts. ## Docs for AI assistants - StarSling docs: https://docs.starsling.dev - Docs llms.txt: https://docs.starsling.dev/llms.txt - Docs llms-full.txt: https://docs.starsling.dev/llms-full.txt - Full site content (markdown): https://starsling.dev/llms-full.txt --- # StarSling > Self-driving CI for GitHub Actions. **Website:** [starsling.dev](https://starsling.dev) **Backed by:** Y Combinator ## Self-driving CI Faster GitHub Actions runners with AI agents that continuously improve your build speeds. StarSling is AI-native CI for GitHub Actions. StarSling Runners are a drop-in replacement for ubuntu-latest and ubuntu-24.04, combining fast Ubuntu runners with AI agents that continuously analyze workflow logs and telemetry to open optimization PRs for caching, dependency installs, parallelization, test sharding, and workflow structure. - [Get Started For Free](https://github.com/apps/starslingdev) - [Speak with a founder](https://cal.com/team/starsling/starsling-founders-chat) --- ## Trusted by fast-moving teams Powering CI for startups and fast-moving engineering teams. - [Better Auth](/customers/better-auth) - [Mastra](/customers/mastra) - [Partcl](/customers/partcl) - [OrgOrg](https://orgorg.com) --- - **6x** Faster CI - **800,000** CI jobs run --- ## What customers are saying Real teams shipping faster with StarSling Runners. > "Within a day of migrating to StarSling Runners, their agents opened up a PR that literally made our Rust CI tests 2x faster!" > > Vamshi Balanaga, Co-founder & CTO, Partcl - [Read the case study](https://starsling.dev/customers/partcl) > "Most CI providers just give you faster machines. StarSling gives us that and made our tests faster, so we could stay focused on making Better Auth even better." > > Bereket Engida, Founder & CEO, Better Auth - [Read the case study](https://starsling.dev/customers/better-auth) > "Our team is really loving StarSling. The runners are just handled, so nobody on my team thinks about CI infrastructure anymore, and the agents keep optimizing for the one thing I care about, minutes saved." > > Abhi Aiyer, Co-founder & CTO, Mastra - [Read the case study](https://starsling.dev/customers/mastra) > "Most CI vendors just bill you for faster machines. StarSling's agents actively find ways to cut what each minute costs you, and those savings compound as your volume grows, while you stay focused on your product." > > Vamshi Balanaga, Co-founder & CTO, Partcl - [Read the case study](https://starsling.dev/customers/partcl) > "StarSling agents are like a CI engineer we never had to hire. They find the slow spots, test out fixes, find the ones that work and open the PRs themselves." > > Bereket Engida, Founder & CEO, Better Auth - [Read the case study](https://starsling.dev/customers/better-auth) > "At Mastra we move so fast that the bottleneck becomes reviews and CI. Time spent compounds, and StarSling helps you realize how much time you're losing." > > Abhi Aiyer, Co-founder & CTO, Mastra - [Read the case study](https://starsling.dev/customers/mastra) > "We build chip design and simulation software that's far faster than traditional methods. StarSling is the same idea pointed at CI." > > Vamshi Balanaga, Co-founder & CTO, Partcl - [Read the case study](https://starsling.dev/customers/partcl) > "Before StarSling, speeding up CI was the sort of work that didn't make it into a release. There was always a feature that mattered more than shaving a minute off the test suite. Now we can actually ship CI improvements with new releases." > > Bereket Engida, Founder & CEO, Better Auth - [Read the case study](https://starsling.dev/customers/better-auth) > "Our test suite grows every week. It used to mean someone had to stop and make it fast again; now that just happens in the background while we keep shipping." > > Abhi Aiyer, Co-founder & CTO, Mastra - [Read the case study](https://starsling.dev/customers/mastra) > "The agents went through every workflow we had and did the annoying but valuable work we'd never gotten to: caching builds across shards, parallelizing our tests, right-sizing each job to the machine it actually needed. All on their own. I just reviewed the PRs and merged." > > Vamshi Balanaga, Co-founder & CTO, Partcl - [Read the case study](https://starsling.dev/customers/partcl) > "We've been using StarSling for Better Auth CI. It's been saving us countless hours, both with fast runners and their agent optimizing our CI." > > Bereket Engida, Founder & CEO, Better Auth - [Read the case study](https://starsling.dev/customers/better-auth) > "On a busy day, a job could sit close to fifteen minutes in the queue before a single test even ran, and across the matrix that was real time gone every day. Now it's a minute or two." > > Abhi Aiyer, Co-founder & CTO, Mastra - [Read the case study](https://starsling.dev/customers/mastra) > "We're a chip company, not a DevOps team. We'd been throwing the most powerful machines we could get at our CI and we didn't have time to sit there and optimize it. When it comes down to priorities, our product always comes first so CI just didn't make the cut." > > Vamshi Balanaga, Co-founder & CTO, Partcl - [Read the case study](https://starsling.dev/customers/partcl) > "Better Auth is growing fast. We've almost doubled our GitHub Actions usage in the past few months without CI becoming a bottleneck thanks to StarSling." > > Bereket Engida, Founder & CEO, Better Auth - [Read the case study](https://starsling.dev/customers/better-auth) --- ## CI is the new bottleneck AI made coding 10x faster. CI hasn't kept up, until now. --- ## Product Demo [Watch Video](https://starsling.dev/#video) --- ## AI in your CI: how StarSling self-drives Fast on day one, faster as time goes on. ### Faster Builds 5th Gen AMD EPYC processors, faster than GitHub-hosted runners - 5th Gen AMD EPYC (30% faster than GitHub) - Compatible with actions/cache, zero config - Unlimited concurrency ### Self-driving AI agents deep-scan your workflow runs and ship optimization PRs - Caching: detect missing or misconfigured caches - Dependency installation: implement faster install strategies (e.g., frozen lockfiles, parallel installs) - Build steps: parallelize, remove redundant and optimize slow steps - Tests: detect misconfigured test jobs and test database interactions - Workflow structure: implement job splitting, matrix strategies, and dependency ordering ### Cheaper Than GitHub 4 vCPU Linux runners with 16 GB RAM at usage-based pricing - $0.008/min vs GitHub's $0.012/min (33% cheaper) - Queue time never billed - No seat fees --- ## Real results from customers Per-workflow breakdown across Better Auth and Mastra. - [Combined Store Tests](https://github.com/mastra-ai/mastra/blob/main/.github/workflows/secrets.test-combined-stores.yml) - Mastra: GitHub Actions 9m 22s, StarSling 2m 49s (3.3x faster) - [E2E](https://github.com/better-auth/better-auth/blob/main/.github/workflows/e2e.yml) - Better Auth: GitHub Actions 7m 3s, StarSling 3m 11s (2.2x faster) - [CI](https://github.com/better-auth/better-auth/blob/main/.github/workflows/ci.yml) - Better Auth: GitHub Actions 5m 55s, StarSling 4m 1s (1.5x faster) --- ## Real PRs from StarSling agents Pull Requests merged by customers. StarSling agents analyze and optimize your CI automatically. - **Mastra** - [ci: shard E2E kitchen-sink across 3 parallel jobs](https://github.com/mastra-ai/mastra/pull/15888) - **Better Auth** - [chore(ci): optimize Playwright browser installs in E2E](https://github.com/better-auth/better-auth/pull/8073) - **Better Auth** - [chore: fix turbo cache configuration in ci](https://github.com/better-auth/better-auth/pull/7950) - **Mastra** - [test(chroma): reduce fixed 2000ms waitForIndexing sleep to 200ms and add Docker healthcheck](https://github.com/mastra-ai/mastra/pull/13866) - **Mastra** - [test(couchbase): optimize vector test suite by replacing fixed sleeps with bucket.ping()](https://github.com/mastra-ai/mastra/pull/13965) --- ## Install with a one-line change Drop-in replacement for ubuntu-latest. ### Agent path Install with one prompt Copy the prompt into your coding agent. No config, no manual edits. - Checks the GitHub App is installed - Migrates supported workflows - Opens a PR for you to review ### Manual path ### Step 1 - Install the GitHub App One click to connect your repository. No config files needed. CTA: [Install App](https://github.com/apps/starslingdev) ### Step 2 - Update one line in your workflow Swap `ubuntu-latest` for `starsling-ubuntu-24.04`. ```yaml # .github/workflows/ci.yml build: - runs-on: ubuntu-latest + runs-on: starsling-ubuntu-24.04 ``` --- 2,000 free minutes. Then $0.008/min. ### Free Trial Free 2,000 minutes your first month - 2,000 minutes included - Unlimited concurrency - No credit card required ### Usage-Based $0.008/min 33% cheaper than GitHub Actions - Queue time never billed - Unlimited concurrency - 99.99% SLA ### Enterprise Custom Volume discounts available - Custom minute allocation - SSO/SAML + dedicated account manager - <1hr incident response - Custom SLAs ### Alum Discount ### Compared to GitHub Actions - 2 vCPU / 8 GB: GitHub $0.006/min, StarSling $0.004/min (33% less) - 4 vCPU / 16 GB: GitHub $0.012/min, StarSling $0.008/min (33% less) - 8 vCPU / 32 GB: GitHub $0.022/min, StarSling $0.016/min (27% less) - 16 vCPU / 64 GB: GitHub $0.042/min, StarSling $0.032/min (24% less) - 32 vCPU / 128 GB: GitHub $0.082/min, StarSling $0.064/min (22% less) - 64 vCPU / 256 GB: GitHub $0.162/min, StarSling $0.128/min (21% less) --- ## Frequently asked questions Everything you need to evaluate StarSling for your CI. ### What is StarSling? StarSling is AI-native CI for GitHub Actions. StarSling Runners are self-driving CI: a drop-in replacement for ubuntu-latest and ubuntu-24.04 that makes GitHub Actions up to 6x faster and up to 33% cheaper, with AI agents that continuously analyze your workflows and ship optimization PRs. ### What does Self-Driving CI mean? Self-driving 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; self-driving CI gets faster the longer you use it. Read more about [AI-native CI](/ai-native-ci). ### How do I speed up GitHub Actions builds? Two things move the needle: faster hardware and removing redundant work. Swap `ubuntu-latest` for a runner with stronger CPUs (StarSling Runners are 6x faster end-to-end), then let AI agents find dead steps, cache fixes, and parallelization opportunities your team hasn't had time to fix. ### Why are GitHub Actions runs so slow? 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. ### Are StarSling Runners a drop-in replacement for ubuntu-latest? Yes. Change `runs-on: ubuntu-latest` (or `ubuntu-24.04`) to `runs-on: starsling-ubuntu-24.04` in your GitHub Actions workflow files. No other config changes required. See [StarSling vs GitHub Actions](/compare/github-actions). ### How much do StarSling Runners cost? $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. ### How do StarSling's AI optimizations work? 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, build parallelization, test sharding, and workflow restructuring. You review and merge. StarSling AI optimizations are not enabled by default for new accounts and are currently in early access. Join the waitlist to get access [here](https://tally.so/r/yPgNag). ### What's the best GitHub Actions runner for AI-assisted development? 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. ### Do I pay for failed jobs? 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. ### How does StarSling handle security and isolation? 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](https://docs.starsling.dev/security/data-handling). --- ## Supercharge your CI with AI Join AI-native teams that have switched to StarSling Runners - [Get Started For Free](https://github.com/apps/starslingdev) - [Speak with a founder](https://cal.com/team/starsling/starsling-founders-chat) --- Self-driving CI for GitHub Actions. ### Product - [Features](https://starsling.dev/#features) - [Pricing](https://starsling.dev/#pricing) - [Book a Demo](https://cal.com/team/starsling/starsling-founders-chat) ### Resources - [Blog](https://starsling.dev/blog) ### Company - [Careers](https://starsling.dev/#careers) - [Privacy](https://starsling.dev/privacy) - [Terms](https://starsling.dev/terms) (c) 2026 StarSling, Inc. All rights reserved. --- # AI-Native CI for GitHub Actions Most CI runs your jobs and stops there. AI-native CI keeps watching, and keeps improving the pipeline. StarSling is AI-native CI for GitHub Actions: fast, drop-in runners plus agents that open optimization PRs you review and merge. ## What is AI-native CI? AI-native CI is continuous integration that does more than run jobs. It observes workflow logs, job timing, cache behavior, failures, and machine telemetry, then uses agents to propose or apply improvements to the pipeline over time. Traditional CI is static: it executes the workflow you wrote, the same way, every run. The pipeline only improves when an engineer finds time to profile it and hand-tune caching, parallelization, or test layout, which rarely gets prioritized. AI-native CI closes that gap by treating the pipeline itself as something to be continuously analyzed and optimized. ## How StarSling works StarSling has two layers. The first is the runner: StarSling Runners are a drop-in replacement for `ubuntu-latest` and `ubuntu-24.04`, on faster hardware (5th Gen AMD EPYC), so existing workflows get quicker the moment you change one line. See the [runner specs and labels](https://docs.starsling.dev/runners/linux-runners). The second is the agents. After you install the StarSling GitHub App, agents continuously analyze your workflows, jobs, run logs, and machine telemetry, then open pull requests that optimize your pipeline. You review and merge, so nothing changes your CI without a PR. See [how the optimization agents work](https://docs.starsling.dev/ai-powered-features/optimizations) in the docs. ## Why fast runners alone are not enough Faster hardware is real and immediate, but it only addresses one cause of slow CI. The rest is structural: caches that never hit, dependency installs that re-download everything, steps that run serially when they could be parallel, test suites that aren't sharded, and fixed sleeps standing in for real readiness checks. No runner fixes those by itself, because they live in your workflow files. That is exactly the work AI-native CI takes on: finding the structural waste and proposing the fix as a reviewable PR. You can [benchmark StarSling against GitHub-hosted runners](https://docs.starsling.dev/performance/benchmarks) on your own workflows. ## What StarSling agents optimize StarSling agents focus on the highest-leverage, lowest-risk improvements to your GitHub Actions pipeline (see the [optimizations docs](https://docs.starsling.dev/ai-powered-features/optimizations)): - Caching: detect missing or misconfigured caches (e.g. broken Turborepo or actions/cache keys). - Dependency installs: faster install strategies like frozen lockfiles and parallel installs. - Parallelization: split serial steps and jobs that can safely run concurrently. - Test sharding: spread long test suites across parallel shards to cut wall-clock time. - Workflow structure: job splitting, matrix strategies, dependency ordering, and removing redundant work. ## How to migrate from ubuntu-latest Migration is a one-line change. Install the [StarSling GitHub App](https://github.com/apps/starslingdev), then swap your runner label: Your workflows, actions, and secrets stay exactly as they are. StarSling runs your existing GitHub Actions, just on a different runner. See the [migration guide](https://docs.starsling.dev/configuration/migration-guide) and [quickstart](https://docs.starsling.dev/getting-started/quickstart) for the full walkthrough. ```yaml # .github/workflows/ci.yml - runs-on: ubuntu-latest + runs-on: starsling-ubuntu-24.04 ``` ## StarSling vs traditional GitHub Actions runners A traditional runner (GitHub-hosted or a third-party fast runner) gives you a machine to execute jobs. It does not look at your pipeline or suggest changes. StarSling is a drop-in runner too, but the agents make it self-driving: the pipeline gets faster the longer you use it, not just the day you switch hardware. For a side-by-side breakdown, see the comparison with GitHub Actions. ## FAQ ### Is AI-native CI the same as self-driving CI? Yes. StarSling uses the terms interchangeably. Both mean CI that observes your pipeline and improves it over time through agents, rather than just executing static workflows. ### Does AI-native CI change my workflows automatically? No. StarSling's agents open pull requests. You review the diff and merge, so nothing reaches your default branch without your approval. ### Do I have to rewrite my GitHub Actions to use StarSling? No. StarSling is not a new CI syntax. You change `runs-on: ubuntu-latest` to `runs-on: starsling-ubuntu-24.04` and keep your existing workflows. ## Learn more - [StarSling overview](https://starsling.dev/) - [Quickstart (migrate in 5 min)](https://docs.starsling.dev/getting-started/quickstart) - [Migration guide](https://docs.starsling.dev/configuration/migration-guide) - [What StarSling's agents optimize](https://docs.starsling.dev/ai-powered-features/optimizations) - [Benchmark on your own workflows](https://docs.starsling.dev/performance/benchmarks) - [Runner label reference](https://docs.starsling.dev/configuration/label-reference) - [StarSling vs GitHub Actions](https://starsling.dev/compare/github-actions) - [Announcing StarSling Runners (launch post)](https://starsling.dev/blog/announcing-starsling-runners-self-driving-ci-that-optimizes-itself) - [All StarSling docs](https://docs.starsling.dev) - [StarSling facts for AI assistants](https://starsling.dev/ai-assistant-facts) - [LLM facts (llms.txt)](https://starsling.dev/llms.txt) --- # StarSling Blog Posts on faster CI, AI-native pipelines, and StarSling engineering. ### [Announcing StarSling Runners: self-driving CI that optimizes itself](https://starsling.dev/blog/announcing-starsling-runners-self-driving-ci-that-optimizes-itself.md) *2026-04-30 - Yonas Beshawred* Fast GitHub Actions runners with AI agents that continuously ship optimization PRs. Customers like Better Auth and Mastra are seeing 82% faster CI. --- # Announcing StarSling Runners: self-driving CI that optimizes itself Today we're happy to announce [**StarSling Runners**](https://starsling.dev/): self-driving CI. Since switching to StarSling, customers like [**Better Auth**](https://better-auth.com/) and [**Mastra**](https://mastra.ai/) have gotten **82% faster GitHub Actions**! Check out their results [here](/#results). StarSling Runners are an AI-native drop-in replacement for `ubuntu-latest`. Just install our GitHub App and swap out the `runs-on` line in your workflows and StarSling agents do a deep scan of your CI setup and start shipping optimizations. Here are some examples of optimizations that Better Auth and Mastra have merged in recently: - [ci: shard E2E kitchen-sink across 3 parallel jobs](https://github.com/mastra-ai/mastra/pull/15888) - [test(chroma): reduce fixed 2000ms waitForIndexing sleep to 200ms and add Docker healthcheck](https://github.com/mastra-ai/mastra/pull/13866) - [test(mssql): replace TCP healthcheck with sqlcmd, simplify pretest, pin Docker image](https://github.com/mastra-ai/mastra/pull/14520) - [chore(ci): optimize Playwright browser installs in E2E](https://github.com/better-auth/better-auth/pull/8073) - [chore: fix turbo cache configuration in ci](https://github.com/better-auth/better-auth/pull/7950) ## Our Story Daniel led a 12-person engineering team at Netflix that built their Internal Developer Portal (IDP) called Netflix Console. Netflix Console was the "command center" for Netflix's 3,000+ engineers, managers and leaders and became the highest DAU internal tool for developers at Netflix. Prior to that he was a Software Engineer at Facebook where he worked on the AI Camera team on a computer vision library that currently powers camera effects for 100M+ DAUs across IG, FB, and WhatsApp. I was previously Founder & CEO of StackShare, a community of over 1.5M developers and engineers. StackShare let developers compare and discuss tech stacks and it became the top destination for anyone choosing between developer tools. StackShare's enterprise SaaS offering was used by Fortune 500s to scan across thousands of internal codebases to surface tech stack data via dashboards and APIs. StackShare was acquired by FOSSA in 2024. Daniel and I have been friends for over 10 years and decided to start StarSling last year after realizing how big of an impact AI could have on the DevOps stack. Developers typically lose ~20% of their day on annoying tasks outside of coding: investigating CI pipelines, fixing exceptions, improving app and database performance, resolving incidents, and more. So we thought, what if agents could take these tasks off your plate so you could spend all your time building features and products. We joined YC's first Spring batch last year with a vision to build AI agents for all the DevOps tasks developers have to take on.
[Image: Yonas and Daniel at YC Demo Day]
YC P25 Demo Day, June 2025
Since then we've been heads down in private beta iterating with early adopters to understand which use cases and DevOps integrations are most important to them. ## CI is the new bottleneck Coding agents now write code in 5 minutes, but CI still takes 10+ minutes. [Image: Jared Palmer: When an agent writes code in 5 minutes, that same CI run is suddenly 67% of your cycle time.] We hit this ourselves while building StarSling's DevOps agents during YC. CI became our bottleneck for merge speed. GitHub's hosted runners use older machines which run tests slowly and there's no built-in path to improve your setup over time. So we wasted a lot of time waiting for slow builds and whenever we got time (which was very rare) we tried to improve our CI setup. GitHub Actions was also the single most requested AI agent from our waitlist: [Image: Waitlist vote tally: GitHub Actions is the most-requested DevOps agent] So we built the CI system we wanted: **fast on day one, faster as time goes on using agents**. ## Why the world needs another CI runner GitHub announced GitHub Actions pricing changes in [December](https://resources.github.com/actions/2026-pricing-changes-for-github-actions/) that make CI even more painful for many teams with lots of jobs. And the product itself isn't getting better since GitHub's focus has clearly shifted to other priorities. There's no shortage of developers complaining about GitHub Actions these days: [Image: HN comment: GitHub Actions is killing my workflow] [Image: HN comment: I hate GitHub Actions] [Image: HN comment: the pain of GitHub Actions] Even so, GitHub Actions probably isn't going anywhere anytime soon: [Image: What Claude Code picks for CI: GitHub Actions dominates] *Source: [https://amplifying.ai/research/claude-code-picks](https://amplifying.ai/research/claude-code-picks)* We're certainly not the first to try to make GitHub Actions better. Other CI runners solve speed too, but none of them are leveraging the recent advances in the models to continuously improve your pipelines. We believe CI should be just as agentic as coding is. ## What you get with StarSling Runners - **Better hardware**: more powerful machines than GitHub-hosted runners - **AI in your CI**: AI agents constantly analyze your workflows, jobs, run logs, and machine telemetry then open up PRs that optimize your setup ### How we speed up your CI tl;dr better infra + AI agents. #### Infrastructure StarSling Runners have better hardware: - 5th Gen AMD EPYC (30% faster than GitHub) - Unlimited concurrency #### AI Agents StarSling's AI agents use frontier models to continuously perform deep scans of your workflow runs, then open up PRs to optimize: - **Caching**: detect missing or misconfigured caches - **Dependency installation**: implement faster install strategies (e.g., frozen lockfiles, parallel installs) - **Build steps**: parallelize, remove redundant and optimize slow steps - **Tests**: detect misconfigured test jobs and test database interactions - **Workflow structure**: implement job splitting, matrix strategies, and dependency ordering ## Pricing If you're using GitHub-hosted runners, we give you faster CI, with AI, while saving you money. Full pricing details [here](https://starsling.dev/#pricing). Our runners start at $0.004/min for 2 vCPU runners and $0.008/min for 4 vCPU runners (versus GitHub's $0.012/min). AI-powered optimizations are included with every minute. ## Join the waitlist If you're paying GitHub for GitHub Actions, we'll speed up your runs, optimize your workflows, and save you money on your bill. Join the waitlist [here](https://tally.so/r/b5OJVo). Happy Slinging Yonas & Daniel --- # Teams that have upgraded to self-driving CI. StarSling is a self-driving CI platform. AI agents pick what to fix, open their own PRs, and customer engineers review and merge. The teams running it: Abhi Aiyer (Co-founder & CTO, Mastra) - Bereket Engida (Founder & CEO, Better Auth) - Vamshi Balanaga (Co-founder & CTO, Partcl) ## [Mastra](https://starsling.dev/customers/mastra) Combined store Tests (vector+storage) - p95 wall, all branches > "At Mastra we move so fast that the bottleneck becomes reviews and CI. Time spent compounds, and StarSling helps you realize how much time you're losing." > > Abhi Aiyer / Co-founder & CTO, Mastra **6x faster** (29m 56s -> 5m 06s). ## [Better Auth](https://starsling.dev/customers/better-auth) E2E - p50 job, all branches > "We've been using StarSling for Better Auth CI. It's been saving us countless hours, both with fast runners and their agent optimizing our CI." > > Bereket Engida / Founder & CEO, Better Auth **2x faster** (2m 22s -> 1m 04s). ## [Partcl](https://starsling.dev/customers/partcl) Compute cost - per CI run > "Within a day of migrating to StarSling Runners, their agents opened up a PR that literally made our Rust CI tests 2x faster!" > > Vamshi Balanaga / Co-founder & CTO, Partcl **13x cheaper** (self-hosted -> StarSling). --- `better-auth/better-auth` # How Better Auth got 2x faster E2E tests and cut 20,000 CI minutes a month with StarSling. In the weeks after migrating to StarSling Runners, StarSling agents shipped three optimization PRs and cut per-job E2E runtime from 2m 22s to just over one minute, adding up to roughly 20,000 minutes of CI compute saved every month. > "We've been using StarSling for Better Auth CI. It's been saving us countless hours, both with fast runners and their agent optimizing our CI." > > Bereket Engida ## By the numbers - **2.22x** Faster E2E (2m 22s -> 1m 04s, post-migration) - **20,000** CI minutes saved / month (compute across all CI + E2E jobs) - **2x** Weekly CI runs (~1,800 -> ~4,000 / week) Seed - $5M raised - San Francisco ## Intro: 28K stars, 800+ contributors, and hundreds of open PRs at any given time [Better Auth](https://github.com/better-auth/better-auth) is the most popular open-source TypeScript authentication framework with **over 28,000 stars** on GitHub, **over 800 contributors**, and hundreds of PRs open at any given time. Better Auth is scaling fast: since turning on StarSling on February 11, 2026, weekly npm downloads have gone from **1.2 million** to **3.3 million**, and their CI load has roughly doubled alongside it going from **1,800 workflow runs per week** to close to **4,000 workflow runs a week**. A growing framework means a growing test suite, and CI work that perpetually competes with shipping the framework itself. ## Problem: A five-minute E2E tax, about to be paid twice as often At migration, end-to-end tests took close to five minutes on every PR, a tax the growing contributor queue was about to pay twice as often. The bottlenecks were the kind that only compound as a framework grows: a turbo cache that wasn't picking up hits between runs, a Docker Compose stack whose services weren't ready when the suite kicked off, and Playwright reinstalling browsers from scratch on every run. > "Before StarSling, speeding up CI was the sort of work that didn't make it into a release. There was always a feature that mattered more than shaving a minute off the test suite. Now we can actually ship CI improvements with new releases." > > Bereket Engida ## Solution: CI that opens its own optimization PRs Better Auth turned on self-driving CI on February 11. Over the next nine days StarSling agents opened three more PRs on their own: fix the turbo cache configuration, add Docker Compose healthchecks where services were racing the test runner, and cache Playwright browser installs across runs. Three CI-tuning projects shipped in nine days. > "StarSling agents are like a CI engineer we never had to hire. They find the slow spots, test out fixes, find the ones that work and open the PRs themselves." > > Bereket Engida ## Results: 2m 22s -> 1m 04s In the weeks after migration, once the runner swap and all three agent-authored optimizations had landed, typical per-job E2E runtime measured **2m 22s -> 1m 04s (2.22x)** and CI 1m 40s -> 1m 02s. Across the thousands of CI and E2E runs Better Auth fires off every month, those per-job savings compound to roughly **20,000 minutes of CI compute** saved. Not one of those optimizations was scoped by a Better Auth engineer: the agents found the work, opened the PRs, and the team reviewed and merged. For a library every team's PR queue gates on, that's the durable win: CI that improves itself instead of sitting in the backlog behind the framework's own roadmap. > "Better Auth is growing fast. We've almost doubled our GitHub Actions usage in the past few months without CI becoming a bottleneck thanks to StarSling." > > Bereket Engida ## When each speedup landed Migration on 2026-02-11. Every entry is a merged `starsling/*` PR. - 2026-02-11 [#7933](https://github.com/better-auth/better-auth/pull/7933) chore: migrate ci workflows to StarSling runners _(migration)_ - 2026-02-13 [#7950](https://github.com/better-auth/better-auth/pull/7950) chore: fix turbo cache configuration in ci - 2026-02-16 [#8010](https://github.com/better-auth/better-auth/pull/8010) chore(ci): add Docker Compose healthchecks for faster CI service readiness - 2026-02-20 [#8073](https://github.com/better-auth/better-auth/pull/8073) chore(ci): optimize Playwright browser installs in E2E ## Where the time went Measured at p50 job-duration across all branches. Pre-migration: GitHub-hosted runners. Post: StarSling runners. | Workflow | Before | After | Speedup | |---|---:|---:|---:| | E2E | 2m 22s | 1m 04s | **2.22x** | | CI | 1m 40s | 1m 02s | **1.61x** | ## Methodology Per-job p50 runtime: the median duration of an individual CI job, across all branches. Before: GitHub-hosted runners, the 3 days before the migration PR merged. After: StarSling runners with the agents' three optimization PRs merged, April 1 to 4. CI compute minutes, the unit GitHub Actions bills: each job's measured p50 saving (CI ~38s, E2E ~78s) summed across every parallel job, times Better Auth's actual monthly job volume from the GitHub Actions API. A conservative, success-runs-only estimate. > "Most CI providers just give you faster machines. StarSling gives us that and made our tests faster, so we could stay focused on making Better Auth even better." > > Bereket Engida --- `mastra-ai/mastra` # How Mastra made their slowest test suite 6x faster and cut queue time by 8x with StarSling. Mastra's slowest test suite dropped from 30 minutes to 5, and under load the wait for a runner fell from 15 minutes to under 2 minutes, all of it identified and shipped by StarSling agents. > "At Mastra we move so fast that the bottleneck becomes reviews and CI. Time spent compounds, and StarSling helps you realize how much time you're losing." > > Abhi Aiyer ## By the numbers - **5.87x** Faster Combined store Tests (29m 56s -> 5m 06s wall p95, post-migration) - **8.2x** Shorter job queue (14m 48s -> 1m 48s job-queue p95, under load) Series A - $35M raised - San Francisco ## Intro: 25K stars, 400+ contributors, and a test suite that grows every week [Mastra](https://github.com/mastra-ai/mastra) is the most popular open-source TypeScript framework for building AI agents with **over 25,000 stars** on GitHub, **over 400 contributors**, new releases of packages going out daily with **600 to 700 PRs** merged every month. Mastra lets anyone building AI agents wire up memory, vector stores, RAG, evals, and more, which means there's tons of test surface area. A framework's whole job is to be fast for the developer building on it, so Mastra proves it on every PR: five test suites fanning out against a couple dozen vector and storage backends. Mastra built and tuned all of it themselves, but the matrix grows every time the framework does, so keeping it fast is a moving target, work that never actually finishes. ## Problem: A 30-minute test suite that grew with the framework Mastra's slowest test suite, `Combined store Tests`, ran against 22 different vector and storage backends, and its slowest runs took 30 minutes. And on the busy days, part of that wasn't testing at all: with all 22 fanning out at once against a shared pool of GitHub-hosted runners, the ones at the back of the line waited about fifteen minutes for a runner before a single test could start. The suites had also collected the workarounds a fast-moving project accumulates: fixed `sleep` calls standing in for real readiness checks, services that raced the test runner on startup, a flaky ClickHouse race in the delete path. Mastra had built and tuned this matrix themselves, and each of these was real work to find and fix. But the suite grows every time the framework does, so the list never stopped getting longer, and that tax got paid more and more often. > "On a busy day, a job could sit close to fifteen minutes in the queue before a single test even ran, and across the matrix that was real time gone every day. Now it's a minute or two." > > Abhi Aiyer ## Solution: CI that scoped and shipped its own optimizations Mastra turned on self-driving CI on Feb 24, 2026, and let it run itself. Over the next **73 days** StarSling agents opened **fourteen PRs** on their own, no engineer triaged what to fix next. They replaced fixed-duration sleeps with polling across MongoDB, Chroma, and Couchbase. Added Docker healthchecks where services were racing the test runner. Sharded E2E kitchen-sink across three parallel jobs. Fixed the ClickHouse race. Migrated workflows to StarSling runners in batches as each round of optimizations made the next migration profitable. Mastra's engineers reviewed and merged each PR, but what to fix next was the agents' call. > "Our test suite grows every week. It used to mean someone had to stop and make it fast again; now that just happens in the background while we keep shipping." > > Abhi Aiyer ## Results: 29m 56s -> 5m 06s In the weeks after migration, once the runner swap and the agents' optimizations had landed, Combined store Tests measured **29m 56s -> 5m 06s** at the slow-tail p95, **5.87x faster**, with the other four suites pulled along behind it. Where the old shared pool left jobs queued about fifteen minutes at the p95 tail under load, StarSling held it to under two minutes, no matter how many contributors were pushing at once. And the bigger win is who stopped doing this work. The fixed `sleep` calls standing in for real readiness checks, the services racing the test runner on startup, the flaky ClickHouse race in the delete path, StarSling's agents found each one, opened the PR, and handed Mastra's engineers a clean diff to review and merge. The never-ending tuning of a suite that grows with the framework became work that handles itself. > "Our team is really loving StarSling. The runners are just handled, so nobody on my team thinks about CI infrastructure anymore, and the agents keep optimizing for the one thing I care about, minutes saved." > > Abhi Aiyer ## When each speedup landed Migration on 2026-02-24. Every entry is a merged `starsling/*` PR. - 2026-02-24 [#13466](https://github.com/mastra-ai/mastra/pull/13466) Migrate Combined store Tests to StarSling Runners _(migration)_ - 2026-02-28 [#13607](https://github.com/mastra-ai/mastra/pull/13607) Migrate workflows to StarSling runners - 2026-02-28 [#13610](https://github.com/mastra-ai/mastra/pull/13610) test(mongodb): optimize vector test suite by replacing fixed sleeps with polling - 2026-03-05 [#13808](https://github.com/mastra-ai/mastra/pull/13808) test(mongodb): replace fixed sleeps with polling - 2026-03-06 [#13937](https://github.com/mastra-ai/mastra/pull/13937) perf(memory): limit vitest parallelism to prevent OOM kills on CI - 2026-03-07 [#13866](https://github.com/mastra-ai/mastra/pull/13866) test(chroma): reduce fixed 2000ms waitForIndexing sleep to 200ms and add Docker healthcheck - 2026-03-07 [#13965](https://github.com/mastra-ai/mastra/pull/13965) test(couchbase): optimize vector test suite by replacing fixed sleeps with bucket.ping() and reducing wait times - 2026-04-13 [#14098](https://github.com/mastra-ai/mastra/pull/14098) chore(ci): add timeout-minutes to all StarSling-hosted workflow jobs - 2026-04-28 [#14520](https://github.com/mastra-ai/mastra/pull/14520) test(mssql): replace TCP healthcheck with sqlcmd, simplify pretest, pin Docker image - 2026-04-29 [#15888](https://github.com/mastra-ai/mastra/pull/15888) ci: shard E2E kitchen-sink across 3 parallel jobs - 2026-04-29 [#15895](https://github.com/mastra-ai/mastra/pull/15895) fix(clickhouse): make deleteTask/deleteTasks await mutation completion - 2026-04-29 [#14044](https://github.com/mastra-ai/mastra/pull/14044) test(clickhouse): stop merges before TRUNCATE, use tmpfs and Docker healthcheck - 2026-04-30 [#16015](https://github.com/mastra-ai/mastra/pull/16015) ci: migrate slow workflows to StarSling Runners - 2026-05-08 [#16330](https://github.com/mastra-ai/mastra/pull/16330) ci(e2e): migrate remaining matrix jobs to starsling-ubuntu-24.04 ## Where the time went Measured at p95 wall-clock across all branches. Pre-migration: GitHub-hosted runners. Post: StarSling runners. | Workflow | Before | After | Speedup | |---|---:|---:|---:| | Combined store Tests (vector+storage) | 29m 56s | 5m 06s | **5.87x** | | Workspace Cloud Tests | 8m 44s | 2m 22s | **3.69x** | | Memory Tests | 12m 11s | 6m 16s | **1.94x** | | E2E Tests | 14m 16s | 10m 50s | **1.32x** | ## Methodology Wall-clock p95 of the Combined store Tests workflow, across all branches. Before: GitHub-hosted runners, a 3-day window before migration. After: StarSling runners with the agents' optimizations merged, April 1 to 4. Job queue: p95 of each job's wait for a runner (job.started_at - run.created_at) across the Combined store Tests matrix jobs, success jobs only, over the same windows as above. --- `partcl.com` # How Partcl cut GitHub Actions costs by 13x and reduced queue time by 16x with StarSling. Partcl's heaviest CI jobs are 13x cheaper per run than on their old self-hosted setup, their slowest shard dropped from 59-89 min to 5-7 min, and queue time fell 16x. StarSling's agents achieved these wins while Partcl's CI volume grew 6x. > "Within a day of migrating to StarSling Runners, their agents opened up a PR that literally made our Rust CI tests 2x faster!" > > Vamshi Balanaga ## By the numbers - **13x** Cheaper per run (vs self-hosted) - **16x** Shorter queue time (9.5 min -> 35 s) - **6x** More CI jobs per day (~640 -> ~3,900 jobs/day) Seed - $5M raised - San Francisco ## Intro: GPU-native chip design and a CI pipeline that gets busier every week [Partcl](https://partcl.com/) is bringing chip design into the AI era by building GPU-native chip design systems that compress weeks of iteration into seconds. Backed by Khosla Ventures and co-founded by Vamshi Balanaga (CTO), the small San Francisco team is scaling fast: since turning on StarSling on April 2, 2026, CI job volume has grown about **6x, from roughly 640 to nearly 3,900 jobs a day**. Their CI runs heavy, long-running compute, so a pipeline that gets busier every week is exactly where compute cost piles up. ## Problem: The heaviest jobs were slow to get picked up and expensive Their heaviest CI jobs don't shrink from a runner swap, the work is the work, and Vamshi wasn't sure if StarSling would move the needle. The first thing StarSling agents attacked was cost: Partcl was running 64-core machines on jobs that didn't need them, hadn't parallelized all its tests, and wasn't caching build artifacts between shards. At a seed-stage startup, every dollar of compute counts, and with volume climbing week over week, that overspend was only going to compound. The second focus of StarSling agents was the wait. On a busy day the CI matrix fanned dozens of jobs out at once against a handful of self-hosted runners. An idle runner picked up instantly, but once the full matrix saturated their fixed fleet the wait blew out, the worst jobs waiting up to 18 minutes for a free machine before a single test could start. > "We're a chip company, not a DevOps team. We'd been throwing the most powerful machines we could get at our CI and we didn't have time to sit there and optimize it. When it comes down to priorities, our product always comes first so CI just didn't make the cut." > > Vamshi Balanaga ## Solution: CI that right-sizes its own runners Partcl turned on self-driving CI in April and over the first five weeks StarSling agents opened eight PRs autonomously: build dependency caching, parallelization of tests, build-artifact caching across the build shards, and right-sizings that moved the heaviest CI jobs off 64-core boxes onto 8-core ones. Each change the StarSling agents made was benchmarked and validated before the PRs were even opened. The test parallelization alone was a large project the team never had time for. The agents scoped it, proved it held, and shipped it on their own. > "We build chip design and simulation software that's far faster than traditional methods. StarSling is the same idea pointed at CI." > > Vamshi Balanaga ## Results: 13x cheaper per run, and the queue time fell 16x The cost curve bent hard. On their heaviest CI jobs, a full run now costs **13x less than it did on their old self-hosted runners** on like-for-like productive compute, and the slowest shard dropped from **59-89 minutes down to 5-7 minutes, about 94x cheaper per shard**. Reliability came with it: where roughly **one in four self-hosted jobs used to be cancelled and billed anyway**, none of StarSling's 500+ sampled jobs were. The wait changed shape too. StarSling runs the CI matrix at far higher concurrency, roughly **35 jobs at once where their self-hosted fleet only allowed 6**, so the backlog evaporated: the **p95 wait for a runner fell from 9.5 minutes to 35 seconds, about 16x shorter**, and the worst case from 18 minutes to about 1 minute. The agents and the runners gave a small team a cost and queue profile it never found the time to build itself. > "The agents went through every workflow we had and did the annoying but valuable work we'd never gotten to: caching builds across shards, parallelizing our tests, right-sizing each job to the machine it actually needed. All on their own. I just reviewed the PRs and merged." > > Vamshi Balanaga ## Where the cost went Partcl's CI is dominated by heavy, long-running compute, where cost climbs fast, so that's what the agents went after. Cost compares their pre-StarSling self-hosted runners against StarSling as multipliers from GitHub Actions job timings, not absolute spend. - **13x cheaper per CI run** vs their pre-StarSling self-hosted runners, on like-for-like productive compute (6.75x lower price per minute on about 1.95x fewer billed minutes). - **Slowest shard**: 94x cheaper (59-89 minutes down to 5-7 minutes). - **Cancelled jobs, billed anyway**: 23% to 0%. ## Methodology Cost per run on Partcl's heaviest CI jobs (their most compute-intensive, longest-running jobs, where cost concentrates): GitHub Actions job times priced at each runner's per-minute rate, compared as multipliers. Like-for-like productive compute (jobs that produced a result) fell about 13x per run: 6.75x cheaper per minute on about 1.95x fewer billed minutes (64-core to 8-core). Queue: each job's p95 wait for a runner (job.started_at minus the push), success jobs only. It fell from 9.5 minutes, when the fixed self-hosted fleet saturated under the full matrix, to 35 seconds, about 16x shorter. > "Most CI vendors just bill you for faster machines. StarSling's agents actively find ways to cut what each minute costs you, and those savings compound as your volume grows, while you stay focused on your product." > > Vamshi Balanaga --- # Privacy Policy Effective Date: May 16, 2025 **StarSling, Inc.** https://starsling.dev At StarSling, Inc. ("Company", "we", "us", or "our"), we value your privacy. This Privacy Policy describes how we collect, use, and disclose your information when you use our software-as-a-service (SaaS) platform designed for software engineering teams (the "Service"), and your rights regarding that information. --- ### 1. Information We Collect We collect the following types of information: #### a. 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Contact Us If you have any questions about this Privacy Policy, contact us at: **StarSling, Inc.** 2261 Market Street STE 85120, San Francisco, CA 94114 **Email:** privacy@starsling.dev --- - [Home](https://starsling.dev) - [Terms of Service](https://starsling.dev/terms) --- # How StarSling compares StarSling is AI-native CI for GitHub Actions: a drop-in runner replacement with AI agents that open optimization PRs. These pages put it side by side with the runner and build-acceleration tools teams evaluate. ## At a glance The alternatives run jobs faster. Only StarSling also opens AI optimization PRs that improve the pipeline over time. | Tool | Faster runners | AI optimization PRs | Primary focus | |---|---|---|---| | StarSling | Yes | Yes | Self-driving CI: fast runners plus agents that open optimization PRs | | GitHub-hosted | No | No | Default GitHub Actions runner infrastructure | | Depot | Yes | No | Docker build acceleration and remote caching | | Blacksmith | Yes | No | Fast drop-in runners | | WarpBuild | Yes | No | Fast drop-in runners and build infrastructure | ## [StarSling vs GitHub Actions](https://starsling.dev/compare/github-actions) GitHub Actions is the default CI for GitHub repositories, and GitHub-hosted runners (ubuntu-latest, ubuntu-24.04) are its standard runner infrastructure. StarSling does not replace GitHub Actions; it runs the same workflows on faster runners and adds an AI layer. You change one line (runs-on), keep every workflow, and StarSling's agents continuously open PRs that optimize caching, dependency installs, parallelization, test sharding, and workflow structure. ## [StarSling vs Depot](https://starsling.dev/compare/depot) Depot is known for fast Docker image builds, remote build caching, and CI infrastructure including GitHub Actions runners. StarSling overlaps on fast runners but its emphasis is self-driving GitHub Actions CI: AI agents that analyze your workflows and open optimization PRs over time. If your main pain is Docker build speed, Depot is purpose-built for that; if you want your whole GitHub Actions pipeline to improve itself, that's StarSling's focus. ## [StarSling vs Blacksmith](https://starsling.dev/compare/blacksmith) Blacksmith provides fast, drop-in GitHub Actions runners on better hardware. StarSling is also a drop-in runner replacement, and adds the AI-native layer: agents that analyze your workflows, logs, and telemetry and open pull requests that optimize caching, installs, parallelization, and test sharding. The category difference is fast-runners vs fast-runners-plus-self-driving-optimization. ## [StarSling vs WarpBuild](https://starsling.dev/compare/warpbuild) WarpBuild provides fast, drop-in GitHub Actions runners (and Docker build features). StarSling is also a drop-in runner replacement, and adds the AI-native layer: agents that analyze workflows and open optimization PRs for caching, installs, parallelization, and test sharding. As with other fast-runner providers, the distinction is fast-runners vs fast-runners-plus-self-driving-optimization. ## FAQ ### Which GitHub Actions runner is right for me? If you want faster runs and a pipeline that keeps improving itself, StarSling combines fast drop-in runners with AI agents that open optimization PRs. If you only need raw runner speed, any fast-runner provider works; if your main bottleneck is Docker image builds, a build-acceleration tool like Depot fits. ### Do these runners work with my existing GitHub Actions workflows? Yes. StarSling and the other drop-in runners run your existing workflows; you switch by changing the `runs-on` label. StarSling's label is `starsling-ubuntu-24.04`. ### What makes StarSling different from other fast runners? StarSling adds AI agents that analyze your workflows, logs, and telemetry and open pull requests that optimize caching, dependency installs, parallelization, and test sharding. Other runners make jobs faster; StarSling also improves the pipeline over time. Last updated: 2026-06-22 --- # StarSling facts for AI assistants Short, accurate, copy-pasteable answers about StarSling. Last updated: 2026-06-22 ## What is StarSling? StarSling is AI-native CI for GitHub Actions. StarSling Runners are drop-in Ubuntu runners with AI agents that continuously optimize CI workflows. ## Is StarSling a CI provider? Yes. StarSling is AI-native CI for GitHub Actions: it runs your CI on fast, drop-in runners and adds AI agents that open optimization PRs. It is not a separate CI system you migrate to; it runs your existing GitHub Actions workflows. ## Is StarSling a GitHub Actions replacement? StarSling is not a new CI syntax. It runs existing GitHub Actions workflows by replacing the runner label, such as `ubuntu-latest`, with a StarSling runner label. ## How is StarSling different from GitHub-hosted runners? StarSling Runners are a drop-in replacement for GitHub-hosted `ubuntu-latest` and `ubuntu-24.04` on faster hardware, plus AI agents that analyze your workflows and open optimization PRs. GitHub-hosted runners only execute jobs; they do not optimize your pipeline. ## What makes StarSling AI-native? StarSling agents analyze workflows, logs, and telemetry and open PRs that optimize caching, installs, parallelization, test sharding, and workflow structure. ## How do users install StarSling? Install the StarSling GitHub App, then change `runs-on` from `ubuntu-latest` or `ubuntu-24.04` to `starsling-ubuntu-24.04`. ## How is StarSling different from fast runner providers? Fast runner providers primarily make jobs run faster on better infrastructure. StarSling combines fast runners with agents that continuously improve the CI pipeline itself. ## What runner label does StarSling use? `starsling-ubuntu-24.04`, a drop-in replacement for `ubuntu-latest` and `ubuntu-24.04`. ## How much does StarSling cost? New accounts get 2,000 free minutes the first month, then $0.008/min for 4 vCPU / 16 GB Linux runners (33% cheaper than GitHub Actions' $0.012/min). Queue time is never billed and there are no seat fees. ## What does StarSling optimize? Caching, dependency installs, build parallelization, test sharding, and workflow structure. Agents open pull requests that engineers review and merge. ## More - [Quickstart](https://docs.starsling.dev/getting-started/quickstart) - [Runner label reference](https://docs.starsling.dev/configuration/label-reference) - [AI optimizations (docs)](https://docs.starsling.dev/ai-powered-features/optimizations) - [AI-native CI explainer](https://starsling.dev/ai-native-ci) - [StarSling docs](https://docs.starsling.dev) - [llms.txt](https://starsling.dev/llms.txt) - [llms-full.txt](https://starsling.dev/llms-full.txt) --- # StarSling vs Blacksmith Last updated: 2026-06-22 Blacksmith provides fast, drop-in GitHub Actions runners on better hardware. StarSling is also a drop-in runner replacement, and adds the AI-native layer: agents that analyze your workflows, logs, and telemetry and open pull requests that optimize caching, installs, parallelization, and test sharding. The category difference is fast-runners vs fast-runners-plus-self-driving-optimization. ## Head to head | Category | StarSling | Blacksmith | |---|---|---| | Drop-in runner replacement | Yes, swap `ubuntu-latest` for `starsling-ubuntu-24.04`. | Yes, a drop-in GitHub Actions runner replacement. | | AI optimization PRs | Yes. AI agents analyze logs/telemetry and open optimization PRs (early access). | No. Focuses on faster runner infrastructure. | | GitHub Actions support | Runs your existing GitHub Actions workflows unchanged. | Yes. Runs existing GitHub Actions workflows. | | Primary focus | Fast runners plus self-driving AI optimization of the pipeline. | Fast drop-in runner infrastructure. | | Pricing model | Usage-based per-minute; 2,000 free min/mo to start; queue time never billed. | Usage-based (see Blacksmith's site for current pricing). | ## Best fit **Choose StarSling if:** Teams that want fast runners AND an AI layer that continuously improves the pipeline through reviewable PRs. **Choose Blacksmith if:** Teams that primarily want faster drop-in runners without an optimization-agent layer. ## FAQ ### How is StarSling different from Blacksmith? Both are fast, drop-in GitHub Actions runners. StarSling adds AI agents that analyze your workflows and open optimization PRs, so the pipeline keeps getting faster, not just the hardware. ### Is StarSling a drop-in replacement like Blacksmith? Yes. Swap your `runs-on` label to `starsling-ubuntu-24.04`; your workflows, actions, and secrets are unchanged. ## Learn more - [What is AI-native CI?](https://starsling.dev/ai-native-ci) - [All StarSling comparisons](https://starsling.dev/compare) - [Benchmark on your own workflows](https://docs.starsling.dev/performance/benchmarks) - [Migration guide](https://docs.starsling.dev/configuration/migration-guide) - [Runner label reference](https://docs.starsling.dev/configuration/label-reference) - [StarSling docs](https://docs.starsling.dev) - [LLM facts (llms.txt)](https://starsling.dev/llms.txt) - [Blacksmith (official site)](https://blacksmith.sh) --- # StarSling vs Depot Last updated: 2026-06-22 Depot is known for fast Docker image builds, remote build caching, and CI infrastructure including GitHub Actions runners. StarSling overlaps on fast runners but its emphasis is self-driving GitHub Actions CI: AI agents that analyze your workflows and open optimization PRs over time. If your main pain is Docker build speed, Depot is purpose-built for that; if you want your whole GitHub Actions pipeline to improve itself, that's StarSling's focus. ## Head to head | Category | StarSling | Depot | |---|---|---| | Drop-in runner replacement | Yes, swap `ubuntu-latest` for `starsling-ubuntu-24.04`. | Offers GitHub Actions runners alongside its build products. | | AI optimization PRs | Yes. AI agents analyze logs/telemetry and open optimization PRs (early access). | Not the focus; the emphasis is build acceleration and caching. | | GitHub Actions support | Runs your existing GitHub Actions workflows unchanged. | Yes. Runners and build acceleration integrate with GitHub Actions. | | Primary focus | Self-driving GitHub Actions CI with agents that optimize the pipeline. | Docker image build acceleration and remote build caching. | | Pricing model | Usage-based per-minute; 2,000 free min/mo to start; queue time never billed. | Usage-based (see Depot's site for current pricing). | ## Best fit **Choose StarSling if:** Teams that want their GitHub Actions pipeline to get faster on its own through AI-opened optimization PRs, not just faster builds. **Choose Depot if:** Teams whose primary bottleneck is Docker image build time and who want best-in-class remote build caching. ## FAQ ### How is StarSling different from Depot? Both speed up CI. Depot focuses on Docker build acceleration and remote caching; StarSling focuses on self-driving GitHub Actions CI, with AI agents that open optimization PRs. ### Can I run my existing GitHub Actions workflows on StarSling? Yes. StarSling Runners are a drop-in replacement: change the `runs-on` label to `starsling-ubuntu-24.04` and keep your existing workflows. ## Learn more - [What is AI-native CI?](https://starsling.dev/ai-native-ci) - [All StarSling comparisons](https://starsling.dev/compare) - [Benchmark on your own workflows](https://docs.starsling.dev/performance/benchmarks) - [Migration guide](https://docs.starsling.dev/configuration/migration-guide) - [Runner label reference](https://docs.starsling.dev/configuration/label-reference) - [StarSling docs](https://docs.starsling.dev) - [LLM facts (llms.txt)](https://starsling.dev/llms.txt) - [Depot (official site)](https://depot.dev) --- # StarSling vs GitHub Actions Last updated: 2026-06-22 GitHub Actions is the default CI for GitHub repositories, and GitHub-hosted runners (ubuntu-latest, ubuntu-24.04) are its standard runner infrastructure. StarSling does not replace GitHub Actions; it runs the same workflows on faster runners and adds an AI layer. You change one line (runs-on), keep every workflow, and StarSling's agents continuously open PRs that optimize caching, dependency installs, parallelization, test sharding, and workflow structure. ## Head to head | Category | StarSling | GitHub Actions | |---|---|---| | Drop-in runner replacement | Yes, swap `ubuntu-latest` for `starsling-ubuntu-24.04`. | N/A. GitHub-hosted runners are the default infrastructure. | | AI optimization PRs | Yes. AI agents analyze logs/telemetry and open optimization PRs (early access). | No. Runners run jobs; they don't optimize your pipeline. | | GitHub Actions support | Runs your existing GitHub Actions workflows unchanged. | Native. It is GitHub Actions. | | Pricing model | Usage-based per-minute; 2,000 free min/mo to start; queue time never billed. | Per-minute for GitHub-hosted runners; included free tier on GitHub plans. | | Hardware | 5th Gen AMD EPYC; 2-64 vCPU; unlimited concurrency. | Standard shared GitHub-hosted hardware; larger runners available. | ## Best fit **Choose StarSling if:** Teams already on GitHub Actions who want faster runs and a pipeline that keeps getting faster, with a one-line migration and no workflow rewrites. **Choose GitHub Actions if:** Teams that want the zero-setup default and are not yet bottlenecked by CI speed or cost. ## FAQ ### Is StarSling a drop-in replacement for GitHub Actions runners? Yes. Change `runs-on: ubuntu-latest` (or `ubuntu-24.04`) to `runs-on: starsling-ubuntu-24.04`. Your workflows, actions, and secrets stay the same. ### How is StarSling different from GitHub-hosted runners? StarSling runs the same workflows on faster hardware and adds AI agents that open optimization PRs. GitHub-hosted runners only execute jobs; they do not optimize your pipeline. ## Learn more - [What is AI-native CI?](https://starsling.dev/ai-native-ci) - [All StarSling comparisons](https://starsling.dev/compare) - [Benchmark on your own workflows](https://docs.starsling.dev/performance/benchmarks) - [Migration guide](https://docs.starsling.dev/configuration/migration-guide) - [Runner label reference](https://docs.starsling.dev/configuration/label-reference) - [StarSling docs](https://docs.starsling.dev) - [LLM facts (llms.txt)](https://starsling.dev/llms.txt) - [GitHub Actions (official site)](https://docs.github.com/actions) --- # StarSling vs WarpBuild Last updated: 2026-06-22 WarpBuild provides fast, drop-in GitHub Actions runners (and Docker build features). StarSling is also a drop-in runner replacement, and adds the AI-native layer: agents that analyze workflows and open optimization PRs for caching, installs, parallelization, and test sharding. As with other fast-runner providers, the distinction is fast-runners vs fast-runners-plus-self-driving-optimization. ## Head to head | Category | StarSling | WarpBuild | |---|---|---| | Drop-in runner replacement | Yes, swap `ubuntu-latest` for `starsling-ubuntu-24.04`. | Yes, a drop-in GitHub Actions runner replacement. | | AI optimization PRs | Yes. AI agents analyze logs/telemetry and open optimization PRs (early access). | No. Focuses on faster runners and build infrastructure. | | GitHub Actions support | Runs your existing GitHub Actions workflows unchanged. | Yes. Runs existing GitHub Actions workflows. | | Primary focus | Fast runners plus self-driving AI optimization of the pipeline. | Fast drop-in runners and build infrastructure. | | Pricing model | Usage-based per-minute; 2,000 free min/mo to start; queue time never billed. | Usage-based (see WarpBuild's site for current pricing). | ## Best fit **Choose StarSling if:** Teams that want fast runners AND an AI layer that continuously improves the pipeline through reviewable PRs. **Choose WarpBuild if:** Teams that primarily want faster drop-in runners and build infrastructure without an optimization-agent layer. ## FAQ ### How is StarSling different from WarpBuild? Both are fast, drop-in GitHub Actions runners. StarSling adds AI agents that analyze your workflows and open optimization PRs for caching, installs, parallelization, and test sharding. ### Is StarSling a drop-in replacement like WarpBuild? Yes. Swap your `runs-on` label to `starsling-ubuntu-24.04`; your existing workflows stay the same. ## Learn more - [What is AI-native CI?](https://starsling.dev/ai-native-ci) - [All StarSling comparisons](https://starsling.dev/compare) - [Benchmark on your own workflows](https://docs.starsling.dev/performance/benchmarks) - [Migration guide](https://docs.starsling.dev/configuration/migration-guide) - [Runner label reference](https://docs.starsling.dev/configuration/label-reference) - [StarSling docs](https://docs.starsling.dev) - [LLM facts (llms.txt)](https://starsling.dev/llms.txt) - [WarpBuild (official site)](https://warpbuild.com) --- # Terms of Service Effective Date: May 16, 2025 **StarSling, Inc.** https://starsling.dev Welcome to StarSling! These Terms of Service ("Terms") govern your access to and use of the services, software, and website provided by StarSling, Inc. ("StarSling", "we", "us", or "our"). By using our product, you agree to these Terms. If you do not agree, do not use the Service. --- ### 1. Use of the Service #### a. Eligibility You must be at least 18 years old and capable of forming a binding contract to use our Service. If you are accessing the Service on behalf of a company or organization, you represent that you have the authority to bind that entity to these Terms. #### b. Account Registration To use our Service, you must create an account. You agree to provide accurate and complete information and keep your account credentials secure. You are responsible for all activity under your account. #### c. Acceptable Use You agree not to misuse the Service, including (but not limited to): - Engaging in illegal activity - Attempting to gain unauthorized access - Interfering with or disrupting the Service or related networks - Using the Service to store or transmit malicious code --- ### 2. Subscriptions and Billing Some parts of the Service may require a paid subscription. 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