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Choose based on the environment your build actually needs. CircleCI Machine Runner executes a job on the VM or physical host where the runner is installed. CircleCI Container Runner schedules each containerized job in an ephemeral Kubernetes pod. Machine Runner Orchestrator is a third model: a Kubernetes controller uses KubeVirt to create full virtual machines for runner jobs. Container Runner is the natural fit for container-ready builds; a conventional Machine Runner fits jobs that require a complete host operating system; Orchestrator fits teams that need full VMs but want Kubernetes to manage VM capacity.
The important distinction is operational as well as technical. Container Runner can create and remove pods, and Orchestrator can adjust a KubeVirt VM pool, but neither option supplies the underlying Kubernetes nodes, virtualization hardware, cluster policy, or network. Your team still owns that capacity and its security boundaries.
Comparison at a glance
| Decision axis | Machine Runner | Container Runner on Kubernetes | Machine Runner Orchestrator |
|---|---|---|---|
| Job environment | Existing VM or physical machine | Ephemeral container pod | Full VM provisioned through KubeVirt |
| Kubernetes required | No | Yes | Yes, plus KubeVirt |
| Best fit | Non-containerized jobs or jobs needing a complete machine environment | Containerized workloads and teams already operating Kubernetes | Workloads needing a full VM with Kubernetes-managed VM scaling |
| Isolation boundary | The runner host | Pod and container boundary | Virtual-machine boundary |
| Automatic unit of capacity | None built into the runner; hosts are supplied by your scaling arrangement | Job pods | VM replicas in a KubeVirt VirtualMachinePool |
| Underlying capacity owner | Your team or infrastructure provider | Your Kubernetes operator | Your Kubernetes and virtualization operator |
| Special requirement | Maintain runner images, dependencies, and cleanup | Kubernetes 1.25 or later and Helm 3.x | KubeVirt, Helm 3 or later, a CircleCI resource-class token, and /dev/kvm on VM-hosting nodes |
CircleCI’s runner overview describes the execution models and supported machine-runner platforms. Its feature comparison matrix adds environment, resource controls, architecture, isolation, scaling, service-container, Docker, and cleanup dimensions that should be part of an evaluation.
How the three architectures execute a job
Machine Runner: the job owns an installed host
You install the runner agent on a VM or directly on physical hardware. When CircleCI assigns a job to that runner, the commands execute in that same environment. The host’s operating system, mounted volumes, installed SDKs, local caches, attached devices, and network routes are therefore part of the job’s practical environment.
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This model is useful when a build cannot be packaged cleanly as a container or needs capabilities that are awkward inside a pod. Examples include tests coupled to a particular operating-system image, hardware or device access, legacy services installed on the host, or workflows that expect a conventional machine rather than a container filesystem. CircleCI lists Linux, Windows, and macOS support for machine runners; the exact image and hardware remain your responsibility.
A fleet of machine runners is a host-capacity problem. You must create, register, patch, monitor, drain, and replace hosts, and arrange whatever automation adjusts the number of available machines as demand changes. CircleCI’s runner-scaling guidance discusses scaling responsibilities but does not turn a machine fleet into an automatically supplied compute service.
Container Runner: one job, one ephemeral pod
Container Runner runs its agent in a Kubernetes cluster. The agent claims CircleCI work and creates a pod for each job. The job’s steps run in the pod’s containerized environment; after completion, the pod is removed. This gives every job a defined container image and a short-lived execution object instead of a long-lived host with accumulated state.
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The model works best when your build, test, and deployment tooling can run in containers and when Kubernetes policies are already part of your platform. Pod-level resource requests, scheduling constraints, namespaces, secrets, network policies, storage classes, and admission controls become part of the execution design.
Container Runner can scale pods to match queued work, but pod creation is not the same as adding compute. If the cluster has no schedulable CPU, memory, accelerator, or compatible node, new pods remain pending. CircleCI does not scale the underlying Kubernetes cluster for you; the operator must provide node and cluster capacity. The Container Runner installation guide states the current prerequisites as Kubernetes 1.25 or later and Helm 3.x.
Machine Runner Orchestrator: Kubernetes-managed full VMs
Machine Runner Orchestrator combines a Kubernetes control plane with KubeVirt virtualization. Its controller observes CircleCI task demand and adjusts a KubeVirt VirtualMachinePool. The resulting VMs become machine-runner environments, so the job receives a full guest operating system rather than merely a container.
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This is useful when VM semantics matter but a team wants Kubernetes APIs, scheduling, declarative configuration, and pool-based capacity management. A configurable minimum replica count can keep a number of VMs pre-warmed, reducing the need to create every guest from zero when work arrives. The current CircleCI documentation identifies the Orchestrator release as version 1.0.0; treat that version and compatibility details as changeable and verify them in the Machine Runner Orchestrator guide before deployment.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsOrchestrator does not install KubeVirt for you. The cluster must support KubeVirt, and nodes that host the VMs need accessible /dev/kvm hardware virtualization. You also need Helm 3 or later and a CircleCI resource-class token. KubeVirt version compatibility should be checked against the current compatibility matrix rather than inferred from the Orchestrator version alone.
Where scaling happens—and who pays the operational cost
Machine Runner capacity
With a conventional machine runner, scaling means changing the number or size of registered hosts. You may use an autoscaling group, an internal provisioning system, or another arrangement, but the runner itself does not provide the machines. Capacity planning must include the operating-system image, boot time, runner registration, dependency installation, disk cleanup, and retirement of unhealthy hosts.
Container Runner capacity
Container Runner’s control loop can create ephemeral pods in response to jobs. The cluster scheduler still needs suitable nodes, and those nodes need enough allocatable resources after system and DaemonSet overhead. Decide in advance how you will handle pending pods, node autoscaling, quotas, taints and tolerations, availability zones, and specialized hardware. A pod-scaling policy that is faster than node provisioning simply moves the queue from the CircleCI agent to the Kubernetes scheduler.
Orchestrator capacity
Orchestrator adjusts the desired VM pool size, but the VM pool depends on Kubernetes scheduling and KubeVirt’s ability to place and start guests. Capacity therefore exists at three layers: the CircleCI queue, the Kubernetes nodes, and the virtualization resources available on those nodes. Include guest boot time, image storage, CPU and memory overhead, and the availability of hardware virtualization when setting a minimum replica count or testing burst behavior.
For all three models, automatic execution-unit provisioning should not be described as unlimited or free capacity. The operator still supplies compute, storage, network bandwidth, operating-system images, and the controls that keep jobs from exhausting shared resources.
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Prerequisites and deployment planning
Machine Runner checklist
- Select supported Linux, Windows, or macOS host images for the jobs you intend to run.
- Install the runner on each VM or physical machine and register it with the appropriate CircleCI resource class.
- Define how hosts receive security updates, toolchain versions, certificates, and credential rotation.
- Choose a host-scaling arrangement and document how a new host becomes ready, how it is drained, and how it is removed.
- Design cleanup for workspaces, caches, temporary credentials, Docker data, and any files left by a failed job.
Container Runner checklist
- Provide a Kubernetes cluster running version 1.25 or later and Helm 3.x, as required by the current installation documentation.
- Install the Container Runner components with the permissions, namespace, service accounts, and secrets required by your cluster policy.
- Define the job pod’s images, resource requests and limits, storage, service containers, and network access.
- Set quotas and scheduling rules so CI cannot consume all capacity needed by production workloads.
- Implement or verify node-capacity automation; CircleCI creates job pods but does not scale your underlying cluster.
Machine Runner Orchestrator checklist
- Operate Kubernetes with KubeVirt installed and supported for your chosen Kubernetes version.
- Confirm that VM-hosting nodes expose
/dev/kvmand that the infrastructure permits hardware-accelerated virtualization. - Install Helm 3 or later and obtain the CircleCI resource-class token required by the controller.
- Prepare guest VM images with the machine runner and all required build dependencies.
- Configure the VM pool, including its minimum replica count, resource requests, storage, networking, and cleanup behavior.
- Validate the current KubeVirt compatibility matrix and the Orchestrator documentation before pinning versions.
Do not treat the Orchestrator chart as a complete virtualization stack: the CircleCI controller chart does not install KubeVirt. The virtualization layer, its upgrades, and its failure recovery remain part of your platform responsibility.
Isolation, privileges, and trust boundaries
Runner privileges affect the impact of a compromised job
A self-hosted job can use the privileges available to its task agent or configured runner user. The effective boundary is therefore determined by the host or pod identity, mounted credentials, network routes, attached storage, and any privileged capabilities you grant—not simply by the product name.
CircleCI warns that giving a job access to a Docker daemon is equivalent to giving it root access to the machine. Avoid mounting a host Docker socket unless the trust model explicitly allows that level of control. Prefer a narrowly scoped build service or an isolated builder when the workflow does not require host-level Docker access.
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Forked pull requests require an explicit trust decision
Untrusted forked pull-request jobs can contain malicious code. CircleCI’s runner concepts and FAQ documentation describe risks including access to the machine or network and unwanted persistent data. Decide whether such jobs may run on self-hosted infrastructure at all. If they must run, separate them from sensitive runners, minimize credentials, restrict egress, prevent access to production networks, and ensure the execution environment is discarded or reliably cleaned after every job.
What the models do—and do not—guarantee
Container Runner removes a job pod after completion, which limits persistence in that pod, but it does not make the Kubernetes cluster ephemeral. Cluster administrators still protect the control plane, node operating systems, image registries, secrets, and shared storage.
Orchestrator gives each job a VM environment, but a VM is not a substitute for access control, patching, or network segmentation. KubeVirt, the Kubernetes nodes, guest images, and the runner user all remain in the security chain. None of the cited documentation constitutes a comparative security audit or a quantified guarantee that one architecture is safer than another.
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Operational comparison by workload
| Workload characteristic | Most natural starting point | Reason | Questions to verify |
|---|---|---|---|
| Build and test steps already packaged as containers | Container Runner | Each job maps directly to an ephemeral pod and Kubernetes scheduling policy. | Are images reproducible? Do pods have enough CPU, memory, storage, and network access? |
| Toolchain depends on a complete operating system or host-level services | Machine Runner | The job runs on the installed VM or physical host without translating every dependency into a container. | How will hosts be patched, cleaned, scaled, and isolated from untrusted code? |
| Full VM required, but Kubernetes is already a platform standard | Machine Runner Orchestrator | KubeVirt supplies VM semantics while Kubernetes manages a declarative pool. | Can nodes provide /dev/kvm? Who operates KubeVirt, guest images, and cluster capacity? |
| Strict separation between jobs is needed | Evaluate Orchestrator or isolated machine hosts | VM or host boundaries may better match the required trust model than a shared pod design. | What credentials, devices, volumes, and network paths remain reachable from the job? |
| Highly variable queue volume | Container Runner or Orchestrator, subject to cluster capacity | Pods or VM replicas can be adjusted in response to demand. | How quickly can nodes, images, and guests become ready, and what is the burst limit? |
This table is a starting point, not a performance ranking. Actual queue latency, utilization, and infrastructure cost depend on image size, dependency installation, guest boot time, node provisioning, workload parallelism, and your existing platform.
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- Classify the execution environment. List every job that needs a full OS, privileged kernel behavior, hardware or device access, a persistent local service, or a specific host image. Any such requirement is a reason to evaluate a machine runner. If the job can be expressed entirely through container images and Kubernetes services, Container Runner is the simpler candidate.
- Decide whether Kubernetes is already an operating competency. Container Runner and Orchestrator add Kubernetes ownership: upgrades, RBAC, admission policy, quotas, node health, storage, networking, and incident response. If your team does not already operate those layers, include that staffing and reliability work in the decision.
- Choose the isolation boundary deliberately. Compare a host, a pod, and a VM against the code trust level, credentials, network reach, device access, and persistence risk of each pipeline. Do not use “container” or “VM” as a shorthand for a complete security posture.
- Map scaling ownership. For Machine Runner, document who creates and retires hosts. For Container Runner, document both pod scaling and node scaling. For Orchestrator, document VM-pool scaling, Kubernetes capacity, KubeVirt operations, and guest-image lifecycle.
- Calculate capacity from the slowest layer. Measure or estimate concurrent jobs, CPU and memory per job, storage and network throughput, image-pull time, host or guest startup time, and the number of jobs that can run before quotas or hardware limits are reached.
- Run a representative pilot. Use the same workflows, dependency caches, service containers, artifacts, security policies, and concurrency that matter in production. Compare queue time, startup time, job duration, failure recovery, cleanup reliability, and operator effort rather than relying on a generic benchmark.
How to design a useful pilot
Use workload groups, not one “hello world” build
- A container-native compile and unit-test workflow.
- A workflow that needs service containers and controlled network access.
- A full-OS or legacy-toolchain workflow that is difficult to containerize.
- A parallel matrix job that stresses scheduling and storage.
- A deliberately untrusted or low-trust workflow used only in a safe test account to verify boundaries and cleanup.
Record comparable measurements
- Time from job queued to an execution environment becoming ready.
- Time spent pulling images or booting hosts and VMs.
- Job duration with cold and warm caches.
- Maximum sustainable concurrency before pods remain pending or VM replicas cannot start.
- CPU, memory, storage, and network utilization at the execution and cluster layers.
- Rate of failed provisioning, stuck resources, incomplete cleanup, and manual interventions.
- Operator hours required for upgrades, image changes, incident recovery, and security review.
Keep the test conditions visible in the result. A one-time warm-cache run, a pre-warmed VM pool, or a cluster with spare nodes can make two architectures appear more similar than they will be during a real burst.
Common failure modes and recovery paths
Container jobs stay pending
First inspect Kubernetes scheduling events and resource requests. Check node allocatable capacity, quotas, taints, tolerations, affinity rules, and namespace limits. If the cluster has no suitable node, increasing the desired number of job pods will not resolve the queue; add or free compatible cluster capacity through your own node-management process.
Orchestrated VMs do not start
Check whether KubeVirt is installed and compatible, whether the VM-hosting nodes expose /dev/kvm, and whether CPU, memory, storage, and networking resources are schedulable. Then inspect the VirtualMachinePool status and guest events. A healthy controller cannot start a guest when hardware virtualization or node capacity is unavailable.
Machine runners accept jobs but behave inconsistently
Compare host images, runner-user permissions, installed tool versions, disk state, environment variables, and leftover files between machines. Drain and rebuild hosts that cannot be returned to a known state. If demand is bursty, verify that the separate host-scaling mechanism registers new runners before assigning work and removes unhealthy ones instead of repeatedly retrying them.
Jobs fail after a security-policy change
Review the exact capabilities, service accounts, mounted volumes, network policies, secrets, and Docker access required by the job. Grant only the minimum set and split workflows that genuinely need privileged operations from ordinary tests. For forked code, use a runner class and network zone with no access to sensitive systems.
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For machine hosts, remove workspaces, temporary credentials, caches, Docker layers, and service processes before reusing a machine; rebuilding the host can be safer when reliable cleanup is difficult. For pods and VMs, verify that attached volumes, snapshots, cloud credentials, and external services are also removed or expired. Deleting the primary execution object does not automatically erase every external side effect.
Costs and ownership to include in an architecture review
The documentation for these runner types does not establish a universal price or performance figure, so a responsible comparison should use your own infrastructure and workload data. Count more than the nominal compute instance:
- Machine Runner: host compute, operating-system licensing where applicable, disks, standby capacity, image maintenance, patching, cleanup automation, and the system that scales and replaces hosts.
- Container Runner: Kubernetes control-plane and node capacity, storage, registry traffic, observability, cluster upgrades, policy management, and spare nodes for CI bursts.
- Machine Runner Orchestrator: all Kubernetes costs plus KubeVirt operations, VM CPU and memory overhead, guest-image storage, hardware-virtualization-capable nodes, and the time needed to maintain both host and guest layers.
Use the same accounting period and concurrency target for each design. A lower per-job runtime can still cost more if it requires a large warm pool; a high-density pod design can still be unsuitable if the workload needs a VM boundary or privileged host behavior.
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Start with Container Runner when your jobs are genuinely containerized and your team already runs Kubernetes with enough node capacity and security controls. Start with conventional Machine Runner when the complete machine environment is the requirement and Kubernetes would add an unnecessary control plane. Evaluate Machine Runner Orchestrator when both conditions are true: jobs need full VMs, and your organization is prepared to operate Kubernetes, KubeVirt, hardware virtualization, and the VM image lifecycle.
Whichever path you choose, document the execution boundary, trust model, scaling owner, cleanup process, and failure recovery before moving production workloads. The strongest architecture is the one whose operational responsibilities match your team’s existing platform capabilities and whose isolation model matches the code you permit to run.
Read the current CircleCI documentation for the runner overview, Container Runner installation, Machine Runner Orchestrator, runner scaling, self-hosted runner FAQs, and runner concepts before implementation; versioned prerequisites and compatibility can change.
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