5 Production Challenges for Running AI Workloads on Kubernetes: From GPU Scheduling to Observability

Kubernetes has evolved from a pure container‑orchestration system into a universal runtime platform, and AI workloads are increasingly being deployed on it. However, moving AI models to production on Kubernetes involves far more than packaging a model into a container image.

1. Complex GPU and accelerator resource management

AI workloads add new resource dimensions—GPU count, video memory, driver versions, topology, health status, and sharing mode—making the resource model far richer than the traditional CPU‑memory‑network‑storage focus. Kubernetes 1.36’s Dynamic Resource Allocation (DRA) continues to evolve to express these finer‑grained resources.

Apparent GPU capacity may be high, yet usable, healthy, and schedulable GPUs are scarce.

Training and inference jobs have differing requirements for memory, topology, and device type.

Expensive GPUs can be under‑utilized while users still complain about insufficient resources.

Platform teams must add visibility into GPU health, resource declaration, quota management, and scheduling policies to avoid both resource starvation and waste.

2. Workload‑aware scheduling for training and batch jobs

Unlike typical online services where independent Pods can be scheduled independently, many AI tasks need a coordinated set of Pods to run simultaneously. Partial scheduling leads to stalled jobs and wasted resources.

Kubernetes 1.36 discusses workload‑aware scheduling features such as PodGroup, gang scheduling, topology‑aware placement, and workload‑level preemption, aiming to treat a group of Pods as a single logical unit.

Distributed training requires GPUs on multiple nodes to be available at the same time.

Batch queues must fairly share resources across teams.

High‑priority inference jobs need guaranteed resources under contention.

Teams need to implement queueing, fair‑share, priority, preemption, and multi‑tenant quota mechanisms.

3. Inference autoscaling must consider GPU metrics

Many teams initially apply classic HPA based on CPU, memory, and request volume, but inference bottlenecks often surface in GPU utilization, memory pressure, or latency rather than CPU load.

The CNCF practice of GPU autoscaling with KEDA shows that scaling decisions should incorporate GPU utilization, memory, temperature, and power consumption.

New replicas may be created without acquiring suitable GPUs.

Model loading time can delay scaling, causing latency spikes.

Scaling may reduce latency but dramatically increase cost.

Platform capabilities must therefore include GPU metric collection, inference latency observation, queue length monitoring, model load‑time assessment, and AI‑specific autoscaling policies.

4. Observability must expand from pod view to model and cost view

Existing observability stacks cover node and pod health, CPU, memory, network, storage, logs, and alerts—adequate for traditional apps but insufficient for AI workloads.

AI workloads demand additional layers:

Resource view: GPU utilization, memory, device health, node pressure, scheduling failures.

Workload view: Status of training, inference, batch, and agent tasks.

Service view: Latency, throughput, error rate, queue length, and per‑request cost.

Only by extending observability to these dimensions can teams shift from post‑mortem debugging to continuous, in‑flight governance.

5. Day 2 governance determines long‑term AI stability

Production AI services involve multiple teams, model versions, training and inference jobs, varied priorities, cost constraints, and compliance requirements. Day 2 operations must address multi‑tenant GPU sharing, isolation between training and serving, quota enforcement, model rollout, gray‑release, rollback, cost attribution, and audit.

Projects such as Kubeflow, Headlamp, Kueue, DRA, and AI Gateway illustrate that AI workloads are being integrated into a holistic cloud‑native platform, requiring coordinated scheduling, resource, gateway, observability, security, and operations components.

In summary, Kubernetes must evolve from merely supporting AI deployment to providing a full production‑grade stack—scheduling, resource management, observability, autoscaling, and governance—that keeps AI workloads efficient, stable, and manageable at scale.

Reference links:

https://kubernetes.io/blog/2026/05/13/kubernetes-v1-36-advancing-workload-aware-scheduling/

https://kubernetes.io/blog/2026/05/07/kubernetes-v1-36-dra-136-updates/