Your agents ship faster than your CI.
AI made writing code cheap. It did nothing to make building and testing it cheap. WarpBuild runs GitHub Actions 2x faster at half the cost, with instant autoscaling so the queue never becomes the bottleneck.
One-line change · No credit card required
Developer velocity went up. CI capacity didn't.
The constraint on shipping used to be how fast people could write code. With assistants and agents in the loop, it is how fast your pipelines can validate it.
Code generation outran code validation
Assistants and autonomous agents produce review-ready branches in minutes. Nothing about that made compiling, testing, or packaging that code any faster.
Every change fans out into a matrix
One PR is rarely one job. Push, rebase, fix a lint error, retry a flake - each round trip re-runs the full matrix across every OS and architecture you support.
Agents don't wait for business hours
CI load used to follow a workday curve with quiet nights and weekends. Agent-driven pipelines run flat out around the clock, so peak capacity is now the baseline.
The bill scales with output, not headcount
When minutes are billed at GitHub-hosted rates, CI spend grows with how much code your team and its agents generate. Budgets planned around engineer count stop holding.
The bottleneck is compute-bound, not human-bound
Every assumption CI capacity planning was built on assumed a human typing on the other end. That assumption is gone.
Before agents
- -Throughput was capped by how fast engineers could write code
- -CI load followed a 9-to-6 curve with quiet nights and weekends
- -A 20-minute build cost one context switch per developer
- -Runner spend was a rounding error next to salaries
With agents in the loop
- +Throughput is capped by how fast runners return a verdict
- +Pipelines run flat out, 24/7, driven by agents and humans together
- +A 20-minute build stalls every agent loop waiting on that result
- +Runner spend is a line item leadership asks about by name
CI built for the pace of AI
Drop-in GitHub Actions runners that are faster per job, cheaper per minute, and elastic enough that a burst of agent-driven builds never forms a queue.
2x faster runners
Faster CPUs, NVMe-backed disks, and better network throughput than GitHub-hosted runners. The same workflow finishes in roughly half the wall-clock time.
Instant autoscaling, no queue
Runners boot on demand with unlimited concurrency. Matrix strategies actually parallelize instead of queuing behind your own jobs at peak.
Half the cost per minute
Per-minute billing at half the rate of GitHub-hosted runners. Combined with shorter runtimes, most teams cut CI spend by well over half.
Unlimited, faster caching
3-10x faster cache save and restore with no storage quotas, so dependency installs and build artifacts stop dominating every run.
Docker layer caching and remote builders
Remote Docker builders with persistent layer caches turn repeated image builds into near-instant rebuilds for both x86-64 and ARM64.
Every platform your agents target
Linux on x86-64 and ARM64, macOS on Apple silicon including M4 Pro, and Windows. Run in WarpBuild's cloud or your own AWS, GCP, or Azure account.
Build wall-clock time
Cost per minute
Cache throughput
Concurrency at peak
One line of YAML, then stop thinking about it
WarpBuild is a drop-in replacement for GitHub-hosted runners. Existing workflows, actions, and preinstalled tooling all keep working.
- runs-on: ubuntu-latest+ runs-on: warp-ubuntu-latest-x64-2xChange one line of YAML
Watch the queue disappear
Track the numbers
Running the pipelines behind teams that ship constantly
From solo open-source projects to thousand-developer organizations, on SOC2 Type 2 attested infrastructure with ephemeral, fully isolated VMs.
Run times went down from ~25 mins to ~9 mins. Even bazel cache load time was faster by 30%
Going from GitHub standard runners to your cheapest runners will save 30-40% time off the average workflow at half the price, and the workflow I care most about is staying the same price but 60% time savings
I get to save the company money and save us on build times. Making me look good to my management team.
Frequently asked questions
Do we have to change how our agents or assistants work?
No. WarpBuild sits underneath GitHub Actions. Whatever opens the PR - a developer, Claude Code, or an autonomous agent - hits the same workflows. The only change is the runs-on label, so tooling that generates or reviews code is unaffected.
Why does faster CI matter more with AI in the loop?
An agent's iteration speed is gated on how quickly CI returns a verdict. A 20-minute pipeline caps how many correction loops an agent can complete in an hour, and that cost is paid on every retry. Halving build time compounds across every loop, for humans and agents alike.
How much does this actually reduce our CI bill?
WarpBuild runners are priced at roughly half the per-minute rate of equivalent GitHub-hosted runners, and jobs finish in about half the time. Teams that also adopt faster caching and Docker layer caching typically see larger reductions. See https://www.warpbuild.com/pricing for current rates.
What happens when a burst of agent-driven builds hits at once?
Runners are provisioned on demand with unlimited job concurrency, so a burst of parallel workflows starts immediately rather than queuing behind your own jobs. There is no fixed pool to size or pre-warm.
Which platforms and architectures are supported?
Linux on x86-64 and ARM64, macOS on Apple silicon including M4 Pro, and Windows. GPU runners are coming soon. Runners can be hosted in WarpBuild's cloud or deployed into your own AWS, GCP, or Azure account with BYOC.
Is this safe for code our agents wrote but nobody has reviewed?
Yes. Every job runs on an ephemeral, fully isolated VM that is destroyed after the run, and WarpBuild never retains your source. WarpBuild is SOC2 Type 2 attested with SSO and access controls. See https://www.warpbuild.com/docs/ci/security for details.