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Use Kubernetes as the foundation in most cases. Add an AI workload control layer only when you have a demonstrated need for job admission queues, shared quotas, fair sharing, priority and preemption, GPU fractions, topology-aware or gang scheduling, multi-tenant policy, or workload-level observability that your current Kubernetes setup does not provide.
These are usually complementary layers, not competing replacements. Kubernetes schedules Pods onto nodes. A batch or admission layer decides which jobs may start and which quota they consume. A framework such as Ray schedules tasks and actors inside its own cluster. Choosing correctly means locating the bottleneck at the right layer, then validating the operational cost and behavior with representative workloads.
What “AI workload control plane” means
“AI workload control plane” is a useful description, but it is not a single standardized product category. In practice, the term can refer to several different functions that sit above, beside, or inside Kubernetes:
- Kubernetes cluster scheduling: the Kubernetes control plane reconciles desired state and places Pods on eligible nodes.
- Batch or admission control: a queueing system holds jobs, applies quotas and priorities, and admits or preempts work when resources become available.
- Framework scheduling: a distributed framework such as Ray places application tasks or actors on framework workers, which themselves run as Kubernetes Pods.
The distinction matters operationally. A Pod stuck in Pending is usually a Kubernetes placement, capacity, taint, quota, or admission problem. A Ray task waiting for a worker is a framework-level scheduling problem. A training job waiting in a queue before any Pods are created belongs to the admission layer.
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Kubernetes documentation describes the cluster architecture and its control-plane components at kubernetes.io/docs/concepts/architecture/. That architecture remains the substrate even when another system adds AI-specific scheduling or policy.
What Kubernetes provides by itself
Kubernetes is a general-purpose cluster control plane, not an AI-only scheduler. It gives you a broad set of primitives that may already be sufficient for a small or single-team GPU environment.
Resource requests and limits
Pods declare CPU, memory, and accelerator requests. The scheduler uses those requests when selecting nodes, while quotas can limit aggregate consumption by namespace or other administrative boundary. Accurate requests are essential: an understated GPU or memory request can cause unsafe packing, while an overstated request can leave capacity stranded.
Placement constraints
Node selectors, affinity and anti-affinity, taints, and tolerations let operators express hardware and topology requirements. You can keep a workload on GPU nodes, separate replicas across failure domains, or reserve specialized nodes for approved workloads.
Priority and controllers
Priority classes influence which Pods are considered first and can enable preemption when a higher-priority Pod needs space. Kubernetes controllers also handle the lifecycle of Deployments, Jobs, StatefulSets, and other workload objects. These mechanisms do not automatically provide all-or-nothing admission for a distributed training job, GPU sharing, or a policy that treats an entire job as one schedulable unit.
Where Kubernetes alone is often enough
- A single team owns the cluster and can coordinate GPU use without a shared queue.
- Workloads are conventional Deployments or Jobs with straightforward resource requests.
- Namespace quotas and Kubernetes priorities cover the required tenancy and urgency rules.
- Applications do not need fractional GPU allocation, gang admission, or framework-level placement.
- Existing metrics and logs provide enough visibility to explain pending Pods and job failures.
In this situation, another control plane can add installation, upgrades, credentials, monitoring, and on-call surface without solving a real constraint.
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When an additional workload layer is justified
Add a layer in response to a specific failure mode or governance requirement rather than the label “AI.” The strongest signals are:
- Queue contention: users submit more training or inference work than the cluster can run concurrently, and ad hoc retries or manual approval are becoming the scheduling policy.
- Shared quotas: teams need hierarchical quotas, resource flavors, or a predictable allocation across departments and projects.
- Fairness: one large job can otherwise monopolize GPUs, or you need fair sharing over time rather than simple first-come scheduling.
- Distributed admission: a job should start only when all of its workers can be placed together, avoiding partially launched training jobs.
- GPU sharing and topology: you need fractional GPUs, shared accelerators, or placement that understands interconnect and locality requirements.
- Central governance: several clusters or business units require common policy, RBAC, usage accounting, and workload-level dashboards.
- Framework complexity: a framework scheduler has decisions that Kubernetes cannot see, such as actor resources, task placement groups, or application-level retries.
Feature availability depends on the selected system, workload object, deployment model, and version. Treat a feature matrix as a compatibility checklist, not as proof that every workload receives the same behavior.
How the main layers fit together
| Layer | Primary decision | Typical object it controls | What to investigate when it waits |
|---|---|---|---|
| Kubernetes | Which eligible node should run a Pod? | Pod and its owning controller | Requests, node labels, taints, affinity, quota, capacity, and scheduler events |
| Batch or admission control | May this job start now, and which quota or queue does it consume? | Job, training resource, or framework workload before admission | Queue, cohort, quota, priority, preemption, and admission events |
| Ray or another framework | Which framework worker should execute this task or actor? | Task, actor, placement group, or application graph | Framework resources, worker availability, placement constraints, and application logs |
A single training run can pass through all three decisions: an admission layer releases it, Kubernetes places its worker Pods, and Ray schedules tasks on those workers. Installing one layer does not remove the responsibilities of the others.
Kueue: a Kubernetes-native queue and admission layer
Kueue is designed as an additive layer on a Kubernetes cluster. Its project overview states: Kueue is a kubernetes-native system that manages quotas and how jobs consume them.
It decides when jobs wait, are admitted, or are preempted, while established Kubernetes components continue to handle Pod-to-node scheduling, autoscaling, and job lifecycle. Read the overview at kueue.sigs.k8s.io/docs/overview/.
What Kueue changes
- Queues can express priority and fair sharing between workloads or teams.
- Resource flavors can represent different node or accelerator pools.
- Preemption can make room for higher-priority work under defined policy.
- Integrations cover Kubernetes Batch Jobs, Kubeflow training jobs, and Ray workloads.
- Prometheus metrics expose queue and workload state for operational monitoring.
Kueue can be installed on a vanilla Kubernetes cluster; it does not replace the Kubernetes scheduler or controllers. That separation is attractive when your organization wants open-source queueing and quota policy while retaining existing cluster operations.
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Kueue is not a universal replacement for accelerator-aware scheduling, a framework’s internal scheduler, or every form of GPU sharing. You still need Kubernetes to place admitted Pods and an appropriate operator or framework to manage the workload lifecycle. Validate gang behavior, accelerator topology, and the exact training integration against the Kueue and workload versions you operate.
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Run:ai: a broader productized platform
NVIDIA’s self-hosted documentation describes Run:ai as a Kubernetes-native orchestration and management platform for AI workloads. Its installation has two broad parts: a control plane for resource management, workload submission, monitoring, and analytics, plus cluster components that extend Kubernetes scheduling and workload management. Both parts require Kubernetes. A typical deployment places the control plane and the first cluster on the same Kubernetes cluster, while connected and air-gapped self-hosted options are also documented. Check the current installation requirements at run-ai-docs.nvidia.com/self-hosted/getting-started/installation before committing to an architecture.
Capabilities listed in the feature matrix
The Run:ai feature matrix lists combinations of fairness, priority and preemption, quotas, node pools, bin-packing or spread placement, GPU fractions, gang scheduling, monitoring, and RBAC. Some capabilities are limited to distributed workloads, and support varies by version and deployment. The matrix is available at run-ai-docs.nvidia.com/saas/workloads-in-nvidia-run-ai/workload-types/supported-features.
Externally submitted Kubernetes workloads may receive only a subset of the platform’s scheduling and management features. Do not assume that applying a Run:ai component to an arbitrary existing workload produces the same experience as submitting a natively integrated workload.
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The self-hosted workload documentation lists standard Kubernetes objects, Kubeflow jobs, RayService, RayCluster, RayJob, and other ecosystem integrations. It also clarifies an important Slurm boundary: Slurm NodeSet support refers to Slurm compute-node Pods running on Kubernetes. Run:ai does not submit or manage Slurm jobs, partitions, accounts, or the Slurm controller. That distinction matters in mixed HPC and AI environments; it is not a substitute for Slurm administration.
Run:ai may be appropriate when you need a packaged governance, scheduling, and observability layer across teams and clusters and are prepared to operate its additional components. It is a larger operational commitment than adding a focused queue controller.
Ray: why framework scheduling is a separate concern
The KubeRay scheduling guide explicitly separates Kubernetes and Ray: Kubernetes places Pods on nodes, while Ray places tasks or actors on Ray nodes. Kubernetes constraints describe machine-level requirements, quota, and workload admission; Ray constraints describe application resources and placement. See docs.ray.io/en/master/kuberay/user-guides/scheduling.html.
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Suppose a Ray cluster has worker Pods running but a task requests a resource label or placement group that no worker can satisfy. Kubernetes may report healthy Pods while the Ray task remains queued. Conversely, if a Ray worker Pod is Pending because no GPU node matches its request, changing Ray’s task configuration will not solve the Kubernetes placement problem. Configure and debug both schedulers deliberately.
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| Criterion | Kubernetes alone | Kueue added to Kubernetes | Run:ai on Kubernetes | Ray with KubeRay |
|---|---|---|---|---|
| Operating model | Use the native control plane, scheduler, and controllers. | Adds queue and admission policy while Kubernetes retains node scheduling and lifecycle. | Adds a separately installed control plane and cluster components. | Adds a framework scheduler for tasks and actors; worker Pods still use Kubernetes. |
| Queueing and admission | Jobs and Pods use native controller behavior, quotas, and priorities. | Queues, quotas, fair sharing, priority, and preemption are central functions. | Feature matrix lists queues, quotas, fairness, priority, and preemption, subject to workload and version. | Schedules framework work after Ray workers exist; it is not, by itself, a general cluster admission queue. |
| GPU fractions or sharing | Depends on device plugins and configured Kubernetes extensions; not guaranteed by core scheduling. | Resource flavors and integrations are documented; exact GPU-sharing behavior depends on the implementation and workload. | GPU fractions are listed for applicable workload categories; verify version and deployment limits. | Uses framework resource declarations inside the Ray cluster; Pod-level GPU placement remains Kubernetes’ job. |
| Gang or distributed placement | Core primitives do not establish that every distributed job receives all-or-nothing admission. | Integrates with distributed workload types; verify the specific training operator. | Gang scheduling is listed for supported categories, with some limits for distributed workloads. | Placement groups express application-level co-location or spreading after workers are available. |
| Multi-tenant policy | Namespaces, quotas, RBAC, priority, and admission controllers. | Queue hierarchy, quotas, flavors, and fair sharing complement Kubernetes policy. | Centralized resource management, RBAC, and workload analytics are documented platform functions. | Framework-level identities and resource rules; cluster tenancy remains Kubernetes’ responsibility. |
| Monitoring scope | Cluster, node, Pod, and controller metrics and events. | Prometheus metrics for queue and workload state, alongside Kubernetes telemetry. | Workload monitoring and analytics through its control plane, with feature variation by workload. | Ray task, actor, and application telemetry plus Kubernetes metrics. |
| Deployment constraints | Requires a supported Kubernetes cluster and its normal add-ons. | Installs on a vanilla Kubernetes cluster and preserves existing controllers. | Control plane and cluster components both require Kubernetes; connected and air-gapped self-hosted patterns are documented. | Requires Kubernetes for Pods and a compatible Ray/KubeRay deployment. |
| Licensing and total cost | Core project cost and commercial support depend on your distribution and operations. | The cited overview does not establish commercial terms; account for engineering and support costs. | Commercial terms are not established by the cited installation and feature pages; obtain current terms separately. | The cited scheduling guide does not establish commercial terms; include framework operations in your estimate. |
A practical decision procedure
1. Inventory the workloads
List interactive notebooks, conventional Deployments, batch training, distributed training, inference services, and framework-managed task graphs. Record the Kubernetes object, operator or framework, accelerator type, minimum and maximum replicas, startup time, retry behavior, and whether partial startup is useful or harmful.
2. Measure the current failure mode
For pending Pods, inspect scheduler events, resource requests, node labels, taints, affinity, quotas, and capacity. For jobs that never start, identify whether the owning controller created Pods at all. For Ray, inspect worker availability and task resource demands separately. This prevents buying an admission system to solve a node-label error or changing Kubernetes placement to solve a Ray resource mismatch.
3. Write the policy in concrete terms
Specify which team may consume which accelerator pool, whether quotas are hard or borrowing is allowed, how fairness is calculated, which jobs may preempt others, whether distributed jobs require gang admission, and which identities can view or cancel work. If you cannot state the rule, a new control plane will not make the policy predictable.
4. Establish a Kubernetes baseline
Correct resource requests, node labels, taints, tolerations, namespace quotas, priority classes, and autoscaling behavior first. Capture queue wait time, startup time, GPU occupancy, failed retries, and operator intervention. This baseline is necessary for evaluating any added layer without confusing cleanup work with product benefit.
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5. Match the smallest layer to the gap
- Choose native Kubernetes when the baseline meets admission, placement, tenancy, and visibility requirements.
- Evaluate Kueue when queueing, quota consumption, fair sharing, and admission are the missing functions and you want Kubernetes to retain node scheduling and lifecycle ownership.
- Evaluate Run:ai when you need a broader, packaged platform for resource governance, GPU-aware policies, workload submission, monitoring, and multi-cluster operations, and can support its additional components.
- Configure Ray in addition to either choice when the application uses Ray tasks, actors, or placement groups.
6. Run representative contention tests
Use real or production-shaped jobs: a short interactive request, a long single-node training run, a multi-node training run, a burst of inference replicas, and competing teams with different priorities. Test empty-cluster startup, full-cluster contention, preemption, retries, node failure, autoscaling, topology constraints, and cancellation. Record queue latency, time to first worker, completed work, failed work, operator actions, and upgrade impact.
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Troubleshooting by symptom
| Symptom | Likely layer | First checks |
|---|---|---|
| Training job has not created Pods. | Admission or workload controller | Queue state, quota, priority, preemption, admission events, and controller logs. |
| Pod is Pending with insufficient GPU. | Kubernetes | GPU requests, node capacity, labels, taints, tolerations, affinity, and device-plugin health. |
| Some distributed workers start and others wait. | Admission, Kubernetes placement, or gang policy | Whether all workers were admitted together, node topology, quotas, and scheduler events. |
| Ray task waits while worker Pods are Ready. | Ray | Task resource declarations, actor requirements, placement groups, and available Ray resources. |
| One team consumes every accelerator. | Admission and quota policy | Queue hierarchy, borrowing rules, fair-sharing configuration, priorities, and preemption. |
| Dashboard shows cluster capacity but not useful job context. | Observability layer | Workload integration, labels, framework metrics, RBAC visibility, and whether externally submitted workloads receive platform features. |
How to treat utilization and performance claims
No neutral, like-for-like published benchmark establishes that Kubernetes alone is always slower or less efficient than an AI workload control plane. Results depend on request accuracy, workload mix, queue policy, GPU topology, autoscaling, framework behavior, and operator configuration.
The NVIDIA Run:ai overview displays “10x GPU Availability,” “20x Workloads Running,” “5x GPU Utilization,” and “0 Manual Intervention” at nvidia.com/en-us/software/run-ai/. The inspected page does not provide a dated study method or independent comparator beside those figures, so treat them as vendor claims rather than validated comparative statistics. The KAI Scheduler project is available at github.com/kai-scheduler/KAI-Scheduler, but a repository link is not a benchmark of one architecture against another.
Your own contention test should be the evidence for a purchase or architecture decision. Compare the same jobs, requests, node pools, topology rules, and failure scenarios before and after adding a layer. A higher utilization graph is not automatically an improvement if queue latency, failure recovery, fairness, or operational effort gets worse.
Bottom-line selection
Start with Kubernetes unless a concrete requirement exceeds its native primitives. Kubernetes remains the cluster foundation even when you add Kueue, Run:ai, or Ray. Choose Kueue when the missing capability is primarily queueing, quota consumption, fair sharing, priority, or admission and you want to preserve Kubernetes ownership of Pod placement and lifecycle. Consider Run:ai when you need a broader productized governance and scheduling platform and accept its additional components, version checks, and operational responsibility. Use Ray’s scheduler as a separate layer for Ray tasks and actors, regardless of how their worker Pods are admitted and placed.
The right architecture is the smallest combination that meets your workload, tenancy, policy, and observability requirements—and that performs predictably under the contention and failure conditions your teams actually face.
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