StarSling
StarSling is agent-native CI: GitHub Actions runners on 5th Gen AMD EPYC plus AI agents that open optimization PRs.
StarSling earns a recommendation for GitHub Actions shops because the first win is a line of YAML, not a migration project: $0.008/min on the default 4 vCPU / 16 GB runner against GitHub's $0.012/min, and queue time is never billed. The second win is the agent layer — Partcl, Mastra and Better Auth all publish measured improvements on the StarSling site, and GPU Runners plus Review Runners give it scope beyond a runner swap. Compare it against GitHub-hosted larger runners on rate and against BuildJet or Blacksmith on runner speed alone; StarSling's differentiator is the agents, not the hardware. Budget review time, because agent PRs still need a human merge.
Verified 3d ago · liveness 69/100 · cite: rightaichoice.com/tools/starsling
- Startups on GitHub Actions that want faster CI without hiring DevOps
- Teams whose queue times and test suites are the daily bottleneck
- Multi-language or Docker workflows needing automatic cache and test optimization
- Cost-conscious teams comparing per-minute rates against GitHub-hosted runners
- Teams not running GitHub Actions (the runners replace Ubuntu labels)
- Organizations requiring self-hosted or on-premise runners
- Projects with custom hardware needs beyond the listed CPU and GPU runner types
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Skip StarSling if your pipelines do not run on GitHub Actions, if you need self-hosted or air-gapped runners, or if no one on your team will review and merge agent-opened pull requests.
GPU runners are opened per org on request, so you have to tell StarSling which card you want and what you plan to run before you can test GPU pricing against your own workloads.
Usage-based pricing fits seed-to-Series-B teams whose GitHub Actions bill is a visible line item: $0.008/min on the default 4 vCPU / 16 GB runner is 33% below GitHub's own $0.012/min larger runner, and the 2 vCPU size at $0.004/min undercuts GitHub's $0.006/min. Small teams that just want cheap minutes without agents can look at GitHub's standard runners, while teams needing self-hosted or multi-CI-provider coverage should look at a general CI platform instead. Enterprise adds SSO/SAML, a
In short
StarSling — StarSling is agent-native CI: GitHub Actions runners on 5th Gen AMD EPYC plus AI agents that open optimization PRs. Best for Startups on GitHub Actions that want faster CI without hiring DevOps, Teams whose queue times and test suites are the daily bottleneck, Multi-language or Docker workflows needing automatic cache and test optimization. Free to start; paid plans from $0.004.
What's new in StarSling
Checked 2 days agoAcross the latest 10 updates: 4 feature updates, 5 launches and 1 news mention.
StarSling raises $3M pre-seed and launches Review Runners in private beta
$3M pre-seed from Bessemer Venture Partners, Y Combinator and five more funds. Review Runners enter private beta.
StarSling GPU Runners launch with NVIDIA RTX and H100 coming
GitHub Actions runners backed by NVIDIA RTX PRO 6000, RTX 5090 and RTX 4090, configured via one line in runs-on. H100 listed as coming.
CI Harness v2 finds 11-month-old bottleneck, speeds job 5.4x
StarSling says CI Harness v2 caught a bottleneck missed by green builds and clean logs, making that job 5.4x faster.
sling CLI released for StarSling Runners
Agent-first CLI diagnoses failed jobs, ranks slowest workflows, shows CI costs and surfaces only relevant log lines.
StarSling publishes 16 GitHub Actions optimization recipes for agents
Sixteen optimization recipes with runnable YAML, tradeoffs and verify steps, plus a JSON index coding agents can fetch.
StarSling posts 18 GitHub Actions best-practices guides for coding agents
Eighteen free guides with copyable YAML, verify steps and markdown mirrors that agents can read and apply to a repo.
ci-secure agent skill scans GitHub Actions for critical attack vectors
Free open-source agent skill scans CI configs for ten critical CI/CD attack vectors, explains findings and applies fixes.
Sandbox benchmark results explorer and refreshed 11-environment run
Interactive explorer added to StarSling's sandbox benchmarks; run refreshed to cover 11 environments with shareable metric views.
ci-score agent skill grades GitHub Actions setups
Free open-source agent skill checks GitHub Actions against best practices, grades the config and ranks fixes for each gap.
StarSling benchmarks cloud sandboxes on real CI workloads
Comparison of Blaxel, Daytona, E2B, Modal and Novita on real CI workloads, open-sourced; startup speed not the headline metric.
Viability Score
How well maintained and how widely used is StarSling? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Up to 6x faster GitHub Actions runners on 5th Gen AMD EPYC
- Six CPU sizes from 2 vCPU / 8 GB to 64 vCPU / 256 GB, $0.004 to $0.128 per minute
- One-line YAML switch via starsling-ubuntu-24.04 runs-on labels
- Queue time never billed and unlimited concurrency
- Dedicated NVIDIA RTX PRO 6000, RTX 5090 and RTX 4090 single-GPU runners billed by the minute
- Review Runners run your own review models, scripts and repo-native skills on every PR
- AI agents open pull requests that shard test suites, fix caching and cut critical path
- Agentic dependency install parallelization and cache repair
- Right-sizing each job to the machine it actually needs
- sling: agent-first CLI that diagnoses failed jobs, ranks slow workflows and shows CI costs
- Open-source ci-speedup skill finds the check gating your pull requests
- Open-source ci-score skill grades GitHub Actions configs against a published rubric
- Open-source ci-secure skill scans for ten critical CI/CD attack vectors
- 99.99% SLA and real-time build log streaming with CI cost visibility
- Works with any GitHub Actions workflow across JS, Python, Rust, Go and Docker
About StarSling
StarSling is CI for teams already running GitHub Actions. You change the runs-on label in your workflow YAML — for example from ubuntu-latest to starsling-ubuntu-24.04 — and your jobs run on 5th Gen AMD EPYC machines across six CPU sizes from 2 vCPU / 8 GB to 64 vCPU / 256 GB, billed per minute from $0.004 to $0.128, with queue time never billed and unlimited concurrency. Against GitHub's Linux x64 larger-runner list prices, the published rates save 33% on the 2 and 4 vCPU sizes down to 21% on 64 vCPU. The agent layer is what separates it from a plain runner swap. Agents read your build history, test fixes, and open pull requests that shard test suites, repair caching, parallelize dependency installs, and cut critical path. StarSling reports up to 91% runtime reduction and a combined 94% total savings once agents have worked a suite, and customer numbers are named: Partcl's queue time fell from 9.5 minutes to 35 seconds, Mastra's test suite from 29m 56s to 5m 06s, and Better Auth's E2E from 2m 22s to 1m 04s. The product now spans three runner types. CPU Runners are the base. GPU Runners give test workloads dedicated NVIDIA RTX PRO 6000 ($0.05922/min), RTX 5090 ($0.03022/min) or RTX 4090 ($0.02522/min) on a 4 vCPU / 16 GB / 100 GB host, single-GPU and billed by the minute, so tests stop borrowing production capacity. Review Runners run your own review models, scripts, and repo-native skills on every pull request at the same CPU rates. sling, an agent-first CLI published in August 2026, diagnoses failed jobs, ranks slow workflows and surfaces CI cost from the terminal. Four open-source skills ship free for any coding agent — ci-speedup, ci-score, ci-secure and sling — and work whether or not you move runners. Runners reached general availability on 2026-06-29 with 2,000 free minutes. StarSling announced a $3M raise in 2026. It is for engineering teams where CI time is real money and where nobody has spare hours for YAML tuning. It is not for teams outside GitHub Actions, teams needing self-hosted or air-gapped runners, or teams unwilling to review and merge agent-generated PRs.
Behind the Verdict
StarSling sits at an unusual intersection: it competes purely on CI price-per-minute, then tries to make price less relevant by shrinking the minutes you buy. Both halves are documented on its own pricing page, which is worth reading line by line because the arithmetic is transparent. The CPU table is the clearest part. At 4 vCPU / 16 GB, StarSling is $0.008/min versus GitHub's $0.012/min — 33% less. At 64 vCPU / 256 GB it is $0.128/min versus $0.162/min — 21% less. The savings narrow as the machine gets bigger, which is honest of them to publish. Queue time is never billed on any tier, and concurrency is unlimited, so you are not paying a surcharge to fan out a matrix build. The pricing page also runs a worked example: 10,000 billed StarSling minutes equal 17,000 GitHub minutes on the same job, $80 versus $204, a $124/month difference. Whether 1.7x is your ratio depends on your workload, but the model is stated rather than implied. The agent layer is the reason to look past the price table. Agents read build history and open pull requests that shard suites, repair caching, parallelize dependency installs and cut critical path. The customer quotes name the same pattern repeatedly: Partcl's CTO describes agents going through every workflow and doing "caching builds across shards, parallelizing our tests, right-sizing each job to the machine it actually needed," with the human reviewing and merging. Mastra's CTO frames it as test-suite upkeep that "just happens in the background." That is the honest framing of what you buy — a contributor that ships CI improvements as reviewable diffs, not a magic switch. GPU Runners matter more than they look. GitHub Actions offers one GPU runner, a 16 GB NVIDIA T4 at 8.1 TFLOPS and $0.052/min. StarSling's default GPU runner is an RTX PRO 6000 at $0.05922/min — a slightly higher rate but 120 FP32 TFLOPS against the T4's 8.1, which StarSling frames as 14.8x faster and a 92% saving on equal FP32 work. The RTX 5090 at $0.03022/min and RTX 4090 at $0.02522/min are cheaper per minute than the T4 and far faster. If your tests touch a GPU and you have been borrowing production cards, this is the cleanest part of the offer. GPU access is opened per org on request rather than self-serve signup, and the H100 is listed as coming soon. Review Runners bill at the same CPU rates and let you run your own review models and repo-native skills on every PR, versioned with your code. The free skills are the low-risk entry. ci-speedup, ci-score, ci-secure and sling run with your own coding agent and work whether or not you move runners, so the whole system is worth a weekend. Plan for the human side: nothing merges itself, agent PRs need reviewers, and teams with a strict change-control process will feel that. Teams outside GitHub Actions, or needing on-premise or air-gapped runners, should look elsewhere — StarSling is a cloud-managed service tied to GitHub Actions.
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Real-world workflow fit
Concrete scenarios for the personas StarSling actually fits — and what changes day-one when you adopt it.
You change runs-on from ubuntu-latest to starsling-ubuntu-24.04 in your workflow YAML, install the GitHub App, and use the 2,000 free first-month minutes to run your real suite.
Outcome: Jobs move to 5th Gen AMD EPYC at $0.008/min versus GitHub's $0.012/min on the same 4 vCPU / 16 GB shape, with queue time unbilled.
You install the free ci-speedup and ci-score skills with your own coding agent first, use them to find the check gating your pull requests, then migrate runners and let agents open PRs that shard the suite and repair caching.
Outcome: You review and merge validated PRs rather than hand-tuning YAML, following the pattern Partcl described when its queue time went from 9.5 minutes to 35 seconds.
You request access to GPU Runners, point the workflow at starsling-ubuntu-24.04-gpu for the RTX PRO 6000 or the /gpus=rtx-4090:1 variant for the 4090, and move GPU-dependent tests off borrowed production capacity.
Outcome: Tests run on a dedicated single-GPU runner billed by the minute, from $0.02522/min on the RTX 4090, while production GPUs stay on serving.
Use Cases
- Replace ubuntu-latest with StarSling runners to cut per-minute CI cost by up to 33%
- Let agents open PRs that shard test suites, fix caching and parallelize dependency installs
- Run GPU-dependent tests on dedicated RTX 4090, 5090 or PRO 6000 cards instead of production GPUs
- Run your own review models and repo-native skills on every pull request with Review Runners
- Diagnose failed jobs, rank slow workflows and see CI cost from the terminal with the sling CLI
- Audit existing GitHub Actions configs with the free ci-score and ci-secure skills before migrating
- Cut queue time on busy matrix builds without paying for concurrency
- Right-size each job so a 2 vCPU task stops occupying a 64 vCPU machine
Limitations
- StarSling is focused exclusively on GitHub Actions, so workflows from other CI providers are not supported.
- It is a cloud-managed service, which means it will not fit teams needing on-premise or air-gapped environments.
- GPU runner access is opened per org after you tell StarSling which GPU you want and what you plan to run on it, rather than being instantly available on every account, and the NVIDIA H100 is listed as coming soon rather than available today.
- Agent-opened pull requests still require a human to review and merge, so the time savings depend on your team having review capacity.
as of 2026-09-25
Verification history
We have re-verified StarSling 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published StarSling tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free Trial
$0/mo
Ideal for
A team that wants to run its real GitHub Actions suite on StarSling runners before committing any budget.
What this tier adds
Free entry point: 2,000 minutes included in your first month, no credit card required, with unlimited concurrency and queue time never billed.
Usage-Based
$0.004-$0.128/min CPU; GPU from $0.02522/min
Ideal for
Seed-to-Series-B engineering teams with a steady GitHub Actions workload and no interest in a contract.
What this tier adds
Adds metered per-minute billing across six CPU sizes ($0.004 to $0.128/min) and GPU runners ($0.02522 to $0.05922/min), a 99.99% SLA, and optimization agents included with paid usage.
Enterprise
Custom
Ideal for
Larger organizations that need security controls, procurement terms and a named contact.
What this tier adds
Adds volume discounts, custom minute allocation, SSO/SAML, a dedicated account manager, under one hour incident response and custom SLAs on top of usage-based rates.
Where the pricing makes sense
The company stage and team size where StarSling's pricing actually pencils out — and where peers do it cheaper.
Usage-based pricing fits seed-to-Series-B teams whose GitHub Actions bill is a visible line item: $0.008/min on the default 4 vCPU / 16 GB runner is 33% below GitHub's own $0.012/min larger runner, and the 2 vCPU size at $0.004/min undercuts GitHub's $0.006/min. Small teams that just want cheap minutes without agents can look at GitHub's standard runners, while teams needing self-hosted or multi-CI-provider coverage should look at a general CI platform instead. Enterprise adds SSO/SAML, a
Setup time & first value
How long it actually takes to get something useful out of StarSling — broken out by persona, not the marketing-page minute.
One line of YAML plus installing the GitHub App: most teams get their first job running on StarSling runners the same afternoon, and the 2,000 free first-month minutes cover the trial. The free open-source skills (ci-speedup, ci-score, ci-secure, sling) can be run against your existing workflows with your own coding agent before you change any runner label, so the audit costs you an agent session
Switching to or from StarSling
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From GitHub-hosted ubuntu-latest runners: swap the runs-on label for starsling-ubuntu-24.04 and install the GitHub App, no other YAML changes required.
- →From GitHub larger runners: pick the equivalent StarSling size (2 to 64 vCPU) by runs-on label and compare the published rate against GitHub's Linux x64 larger-runner list price.
- →From a manually tuned GitHub Actions setup: run the free ci-score skill against your checkout first to rank the gaps, then let StarSling agents open the optimization PRs.
- →From borrowed production GPUs for tests: request GPU runner access, then point GPU jobs at starsling-ubuntu-24.04-gpu or the /gpus=rtx-5090:1 and /gpus=rtx-4090:1 variants.
- ↗To GitHub-hosted runners: change the runs-on label back to ubuntu-latest or the matching GitHub larger-runner label and remove the GitHub App install.
- ↗To self-hosted GitHub runners: point runs-on at your own runner labels; nothing in the workflow depends on StarSling beyond the runner label.
- ↗To another third-party runner provider: swap the starsling runs-on label for the new provider's label, since StarSling is a drop-in replacement for the Ubuntu runner label.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “StarSling”, and we withheld 6: 6 could not be judged, because “StarSling” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about StarSling.
Official links
Tools that pair well with StarSling
Common stack mates teams adopt alongside StarSling, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Starsling vs Spider Cloud
Choose StarSling if your pain point is slow, expensive CI on GitHub Actions and you want hands-free optimization. Choose Spider Cloud if you need to feed real-time web data to AI agents or LLMs via a fast, reliable scraping API. They solve completely different problems; the choice depends on whether your bottleneck is CI speed or external data acquisition.
Starsling vs Presto Voice
StarSling offers transparent freemium pricing and immediate ROI for GitHub Actions users, while Presto Voice requires sales engagement and targets physical drive-thru operations. Choose StarSling if you're a dev team wanting faster CI without DevOps overhead; pick Presto Voice if you run QSR drive-thrus and need proven revenue lift.
Starsling vs Temporal Ai
Temporal and StarSling solve fundamentally different problems: Temporal is for building reliable, stateful workflows (AI agents, microservices) that survive failures, while StarSling is an AI-native CI runner that speeds up and optimizes GitHub Actions pipelines. Choose Temporal if you need durable execution; choose StarSling if your team spends too much time waiting on CI builds.
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