A deep dive into managing stateful LLM workloads, scaling inference endpoints, and optimizing GPU utilization in a cloud-native environment.
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Kubernetes has become the de facto standard for container orchestration, but managing complex AI workloads presents unique challenges. In this comprehensive guide, we'll explore how to effectively orchestrate AI agents and LLM workloads on Kubernetes.
AI workloads differ from traditional applications in several key ways:
apiVersion: apps/v1
kind: Deployment
metadata:
name: llm-inference
spec:
replicas: 3
selector:
matchLabels:
app: llm-inference
template:
metadata:
labels:
app: llm-inference
spec:
containers:
- name: inference
image: your-registry/llm-inference:latest
resources:
requests:
nvidia.com/gpu: 1
limits:
nvidia.com/gpu: 1
Orchestrating AI agents on Kubernetes requires careful planning and optimization. By following these best practices, you can efficiently run AI workloads at scale.
For Orchestrating AI Agents on Kubernetes, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Orchestrating AI Agents on Kubernetes, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Orchestrating AI Agents on Kubernetes, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
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