kubernetesschedulingPractitioner

Unlocking Performance with Kubernetes Pod-Level Resource Managers

5 min read Kubernetes BlogMay 1, 2026Reviewed for accuracy

Practitioner — Hands-on experience recommended

Kubernetes v1.36 brings an exciting alpha feature: Pod-Level Resource Managers. This innovation addresses the need for a more flexible and powerful resource management model, particularly for performance-sensitive workloads. By enabling pod-level resources, you can optimize resource allocation, ensuring that your applications run smoothly under demanding conditions.

To implement this, you need to configure the PodLevelResourceManagers and PodLevelResources feature gates. This setup allows the kubelet to create hybrid resource allocation models, which is crucial for high-performance workloads. You can specify the topology manager scope as either 'pod' or 'container', and ensure that the CPU and Memory Managers are set to static policy. This configuration enables predictable performance through NUMA alignment, which is vital for workloads that require consistent resource availability.

However, while this feature opens up new possibilities, it’s essential to remember that it is still in alpha. Review the limitations and caveats carefully, especially regarding compatibility and downgrade instructions. This will help you avoid pitfalls during implementation and ensure that your workloads benefit from the new resource management capabilities.

Key takeaways

→Enable PodLevelResourceManagers and PodLevelResources feature gates for hybrid resource allocation.
→Configure the topology manager with a policy other than none for effective resource management.
→Use static policy for both CPU and Memory Managers to ensure predictable performance.
→Understand NUMA alignment to optimize resource allocation for performance-critical workloads.

Why it matters

This feature significantly enhances resource management for performance-sensitive applications, allowing for better resource utilization and predictable performance, which is crucial in production environments.

Code examples

YAML

1apiVersion: v1
2kind: Pod
3metadata:
4  name: tightly-coupled-database
5spec:
6  # Pod-level resources establish the overall budget and NUMA alignment size.
7  resources:
8    requests:
9      cpu: "8"
10      memory: "16Gi"
11    limits:
12      cpu: "8"
13      memory: "16Gi"
14  initContainers:
15  - name: metrics-exporter
16    image: metrics-exporter:v1
17    restartPolicy: Always
18  - name: backup-agent
19    image: backup-agent:v1
20    restartPolicy: Always
21  containers:
22  - name: database
23    image: database:v1
24    # This Guaranteed container gets an exclusive 6 CPU slice from the pod's budget.
25    # The remaining 2 CPUs and 4Gi memory form the pod shared pool for the sidecars.
26    resources:
27      requests:
28        cpu: "6"
29        memory: "12Gi"
30      limits:
31        cpu: "6"
32        memory: "12Gi"

YAML

1apiVersion: v1
2kind: Pod
3metadata:
4  name: ml-workload
5spec:
6  # Pod-level resources establish the overall budget constraint.
7  resources:
8    requests:
9      cpu: "4"
10      memory: "8Gi"
11    limits:
12      cpu: "4"
13      memory: "8Gi"
14  initContainers:
15  - name: service-mesh-sidecar
16    image: service-mesh:v1
17    restartPolicy: Always
18  containers:
19  - name: ml-training
20    image: ml-training:v1
21    # Under the 'container' scope, this Guaranteed container receives exclusive,
22    # NUMA-aligned resources, while the sidecar runs in the node's shared pool.
23    resources:
24      requests:
25        cpu: "3"
26        memory: "6Gi"
27      limits:
28        cpu: "3"
29        memory: "6Gi"