Product / architecture preview

Keep the workflow. Rethink where it runs.

A proposed execution layer for GitHub Actions, customer-owned workers, and controlled cloud overflow. Designed to start with private-repository Linux CI.

One policy, multiple places to run.

The hosted coordinator would make placement decisions. A customer-side agent would request work through outbound connections and create an ephemeral job environment. GitHub would remain the workflow engine; the official runner would execute inside that environment.

ONE WORKFLOW. YOUR CAPACITY.Design preview
GitHub Actions workflowbuild → test → report
Tbench execution policyTrust + cache + capacity + cost
Your dedicated worker

Company hardware · warm build cache

Preferred
An opted-in workstation

Resource limits · eligible workloads only

Optional
A cloud runner

Overflow capacity · approved budget

Fallback
Disposable job environment. Persistent, trust-scoped caches.
This is the target architecture. Worker enrollment, job dispatch, cache services, and cloud provisioning are not enabled by this website.

Choose workers by eligibility, not availability alone.

The initial focus is company-owned, always-on Linux servers. Personal workstations are an optional later tier, never an automatic destination just because their owner opened a pull request.

  • Authorize by organization, repository, workflow, ref, and job trust level.
  • Reserve CPU, RAM, disk, and network headroom. Stop accepting new work when the host is busy.
  • Match operating system, architecture, virtualization support, and toolchain requirements.
  • Prefer the triggering developer’s approved machine only when policy and capacity permit it.
  • Keep deployment and signing credentials off personal machines.

Scheduling should minimize expected completion cost while respecting a time-to-result objective. Waiting indefinitely for “free” compute is not a saving.

Disposable jobs. Deliberately persistent caches.

Dependency stores, compiler caches, and BuildKit state can survive between jobs. Workspaces and credentials should not. The design separates these lifecycles so a clean job does not require a completely cold build.

  • Namespace caches by organization, repository, environment, and trust policy.
  • Allow trusted jobs to publish cache state; give low-trust jobs disposable writes.
  • Use content-addressed inputs where the build tool supports them.
  • Compare transfer time with rebuild time before moving a large cache between sites.
Read the trust boundaries

Cloud overflow needs a budget and a deadline.

A queued job can cause a matching cloud runner to be provisioned when eligible local capacity is insufficient. A job already executing on a failed machine generally has to restart; it cannot simply continue from the same instruction on another host.

Retries need leases and duplicate-execution protection. Deployment, publication, and other side-effecting jobs must have explicit retry policies. Cache failure should be distinguishable from worker failure, with a controlled cold-build path when appropriate.

What is available—and what comes next.

LayerStatus
Research, cost calculator, comparison registerAvailable on this site
Client login and account applicationsConnected to Tbench identity; approval required
Managed Linux worker executionPlanned; not self-service
Persistent managed caches and cloud overflowPlanned; validation required
Harbor evaluation adapterProposed extension
Windows, macOS, GPU, personal workstationsFurther compatibility and security work required

Start with your actual workload.

Explore the model, then apply for a scoped pilot. No card or compute commitment.

Request client access