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Use CircleCI Machine Runner Orchestrator when each CI job needs a complete virtual machine and your team already operates Kubernetes with KubeVirt. The controller watches CircleCI demand, increases or decreases a KubeVirt VirtualMachinePool, and gives queued work an isolated VM that is removed after the job. It is not a replacement executor: jobs still use CircleCI’s machine executor and a self-hosted resource class.
The decision comes down to two questions: does the workload require a full operating-system environment, privileged access, or machine-level isolation that a container cannot provide; and can your platform team supply KVM-capable Kubernetes nodes, VM capacity, tokens, networking, and security controls? If either answer is no, static machine runner or container runner is usually a better starting point.
What Machine Runner Orchestrator does
CircleCI describes Machine Runner Orchestrator as “a Kubernetes controller that automatically scales CircleCI runner VMs using KubeVirt.” In practical terms, it is an autoscaling layer between CircleCI’s task queue and a pool of virtual machines.
The control loop
- The controller polls CircleCI for pending and running work associated with the configured self-hosted resource classes.
- It compares demand with the current number of runner VMs.
- It changes the replica count of a KubeVirt
VirtualMachinePool. - KubeVirt schedules and starts the required VMs on eligible Kubernetes nodes.
- A VM claims a CircleCI task, runs the job in a full operating-system environment, and is shut down when the work finishes.
The result is an on-demand fleet rather than a permanently maintained set of individually registered machines. Capacity follows the queue, subject to the limits and VM resources you configure.
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Warm capacity versus scale-from-zero
The minReplicas setting establishes the floor for the pool. A value above zero leaves that many VMs available to claim work, reducing startup delay at the cost of idle compute and capacity consumption. With minReplicas: 0, no VM may exist while the queue is empty; the first task must wait for Kubernetes, KubeVirt, and the guest operating system to become ready. The appropriate value depends on how much startup latency your pipelines tolerate and how expensive idle capacity is in your cluster.
It does not create a new CircleCI job type
CircleCI jobs continue to use the machine executor and a self-hosted resource class. A job should set machine: true and target a class in the form <namespace>/<name>. Do not select CircleCI-hosted machine.image aliases for these VMs; those aliases are not the provisioning mechanism used by the orchestrator.
A CircleCI task is the smallest unit of work that the runner can claim. A job is the larger workflow step defined in your configuration. The orchestrator reacts to the queued tasks, while your CircleCI configuration still defines jobs, steps, dependencies, and workflows.
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Full operating-system behavior
Choose this model when tests or build tools need kernel-level behavior, system services, custom drivers, system packages, or other machine configuration that is awkward or unsafe to reproduce in a container. Each task receives a VM boundary instead of sharing a container host’s user space.
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Privileged or hardware-sensitive work
Workloads that need privileged operations, nested virtualization, device access, or strict separation from other jobs can justify a VM. You still need to define the guest permissions carefully; a VM does not make an over-privileged job harmless.
Private or controlled infrastructure
Self-hosted execution is useful when builds must reach private networks, internal services, controlled static IP ranges, on-premises systems, or cloud permissions such as AWS IAM roles. Those are general self-hosted-runner reasons; validate the network paths and identity model for your particular KubeVirt deployment before committing to the orchestrator.
Variable demand
If a fixed machine fleet sits idle between bursts, demand-based VM provisioning can simplify capacity management. You can keep a small warm floor for interactive work and let the pool expand for larger queues, within the Kubernetes and resource-class limits you establish.
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- No Kubernetes or KubeVirt capability: the chart expects an existing KubeVirt installation and does not install it for you.
- No KVM-capable hosts: every node that runs these guests must expose
/dev/kvm, using hardware virtualization on bare metal or nested virtualization on a cloud VM. - Container-first pipelines: if the job’s main requirement is a custom Docker image or a CircleCI Convenience Image, container runner is the more natural self-hosted model.
- A deliberately fixed host: use conventional machine runner when a job must remain on a particular VM or physical machine with a known local environment.
- Very short jobs with no warm pool: VM boot time can dominate execution when
minReplicasis zero. Measure your queue and startup tolerance before choosing scale-from-zero.
How the execution models differ
| Option | Where work runs | Best fit | Main trade-off |
|---|---|---|---|
| Machine Runner Orchestrator | A KubeVirt VM provisioned through Kubernetes | Full VM semantics, operating-system customization, privileged work, or stronger isolation with an existing virtualization platform | Requires Kubernetes, KubeVirt, KVM-capable nodes, VM capacity planning, and guest lifecycle operations |
| Conventional machine runner | The VM or physical host where the runner binary is installed | A stable, known machine environment; available for Linux, Windows, and macOS | Each runner remains tied to its host, so dependencies, cleanup, drift, and idle capacity are your responsibility |
| Container runner | An ephemeral Kubernetes pod | Containerized jobs that depend on custom Docker images or Convenience Images | It is not a full-VM execution boundary and still requires Kubernetes operations |
Compare candidates on isolation boundary, operating-system requirements, image support, existing Kubernetes and KubeVirt expertise, acceptable startup latency, host capacity, and security policy. A team may use more than one model: container runner for ordinary builds and machine orchestration for jobs that genuinely need a VM.
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Prerequisites and capacity requirements
Cluster and virtualization
- A Kubernetes cluster with KubeVirt installed separately.
kubectlconfigured for the target cluster.- Helm version 3 or newer.
- KubeVirt-compatible scheduling labels and sufficient CPU and memory for the guest pool.
/dev/kvmexposed on every eligible VM host. This requires hardware-accelerated virtualization on bare metal or nested virtualization when the Kubernetes node itself is a cloud VM.
CircleCI’s current guide lists orchestrator version 1.0.0 and says the project was tested with KubeVirt v1.8. Confirm compatibility before upgrading either component.
CircleCI organization setup
An organization administrator creates a CircleCI namespace and one resource class for each class the pool will serve. CircleCI issues a token for each class. The token is displayed only once, so store it in a secret manager immediately and do not commit it to a values file or repository.
The chart maps each resource-class name to its token and scaling limits. The key used in that mapping must exactly match the class requested by the job, including namespace and name. A typo can leave a healthy Kubernetes deployment with no claimable work.
Guest image and sizing
The documented default VM specification is 2 GiB of memory and 1 CPU, using a CircleCI-maintained Ubuntu 24.04 containerDisk with Docker and common CI tooling. Treat that as a starting point, not a capacity recommendation. Increase CPU, memory, disk, or guest customization for compilers, browsers, Android tooling, large dependency graphs, or parallel test processes. The Kubernetes nodes must have enough allocatable capacity for the maximum number of replicas you permit.
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Network and server endpoints
Runner VMs need outbound HTTPS access to https://runner.circleci.com and https://circleci-binary-releases.s3.amazonaws.com. Checkout and persistence features can also require access to your VCS provider and CircleCI task-storage endpoints. Self-hosted runners do not require inbound connectivity from CircleCI.
For CircleCI Server, set provisioner.circleciAPIAddr to the Server hostname instead of leaving the Cloud endpoint. Without that address, the VM attempts to register with the wrong instance. Token behavior differs between Cloud and Server and can change with the guide version; verify the current Server instructions before deployment.
Deployment sequence
The following sequence follows CircleCI’s documented architecture. Names, secrets, and resource limits are environment-specific, so use the chart’s current values reference for exact field names beyond those shown here.
- Create the CircleCI namespace and resource class. In the organization administration area, create the namespace, create the class, and copy its one-time token into a secure secret store.
- Prepare Kubernetes. Select or create a namespace for the orchestrator, verify that KubeVirt is ready, and confirm that intended nodes expose
/dev/kvmand have enough allocatable resources. - Define chart values. Map each resource class to its token, set replica bounds including
minReplicas, and specify the VM CPU, memory, disk, and scheduling requirements appropriate to the jobs. - Install chart version 1.0.0. Use Helm 3 or newer and the installation command from CircleCI’s current Machine Runner Orchestrator guide. Keep secrets out of shell history and source control.
- Inspect the deployment. Check the controller deployment, its logs, the KubeVirt
VirtualMachinePool, and created VM objects. Confirm that replicas can schedule on KVM-capable nodes before sending production work. - Configure the CircleCI job. Set
machine: trueand use the exact self-hosted class key, such asnamespace/name. Remove any CircleCI-hostedmachine.imagealias from that job. - Run a controlled task. Start with a small diagnostic job, watch the controller poll and change the pool, verify that the VM registers and claims the task, then confirm that the VM is removed or returned according to the pool behavior after completion.
Operational and security controls
Identity and privilege
Choose the operating-system user under which the machine runner executes and grant only the permissions that jobs require. CircleCI notes that access to a Docker daemon is equivalent to root access on the machine. Treat the runner user, Docker socket, cloud credentials, and mounted volumes as a single security boundary.
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State and reproducibility
Minimize mutable state inside the guest so that successive tasks are reproducible. Persistent caches can shorten builds, but they consume disk, can become stale or fill the guest, reduce reproducibility, and may increase billable usage. Define cache eviction and cleanup rather than allowing every VM to accumulate artifacts.
Capacity and scheduling
Set an upper replica limit that your nodes can actually run. Watch pending VMs, CPU and memory pressure, KubeVirt events, and node affinity when demand rises. A controller can request more replicas, but it cannot create capacity that the cluster does not have.
Optional SSH reruns
SSH rerun support is disabled by default. Enabling it requires a Gateway API implementation that supports TCPRoute; follow the version-specific instructions and review the exposure this creates before turning it on.
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No VM appears for a queued job
- Confirm that the job uses
machine: trueand the exact namespace/class key. - Check that the configured token belongs to the intended organization and class.
- Review controller logs for polling or authentication errors.
- Verify that the queue is visible to the endpoint configured in
provisioner.circleciAPIAddr.
VMs remain pending
- Inspect KubeVirt events and Kubernetes scheduler messages.
- Check node labels, taints, allocatable CPU and memory, and the presence of
/dev/kvm. - Compare the requested guest size with actual free capacity.
The VM starts but never registers
- Test outbound HTTPS to https://runner.circleci.com or the configured CircleCI Server address.
- Check access to https://circleci-binary-releases.s3.amazonaws.com if runner binaries cannot be retrieved.
- For Server, verify that
provisioner.circleciAPIAddrnames the Server instance rather than the Cloud default. - Recheck the one-time resource-class token and secret injection.
Jobs are slow or inconsistent
Determine whether delay occurs while waiting for a replica, booting the guest, downloading dependencies, or executing the steps. Raise minReplicas when queue latency is the issue; increase guest resources when CPU or memory is saturated; and review cache policy when disk growth or stale state is involved.
A practical decision checklist
Machine Runner Orchestrator is a sensible choice when all of the following are true:
- The job requires a complete VM, privileged or OS-level behavior, or a stronger isolation boundary than a container provides.
- Your team can operate Kubernetes, KubeVirt, Helm, KVM-capable nodes, and VM capacity.
- You can store and rotate resource-class tokens securely.
- Your network policy permits the required outbound connections.
- You have a plan for guest sizing, warm replicas, cleanup, caches, logging, and permissions.
Choose conventional machine runner when the environment is a specific existing host, and choose container runner when custom Docker images and ephemeral Kubernetes pods meet the requirements. The orchestrator earns its operational complexity only when VM-level behavior and demand-based provisioning are both important.
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