Kubernetes platforms like Amazon EKS have made it easier than ever to run Kubernetes clusters at scale—but with great flexibility comes great responsibility. Left unchecked, resource inefficiencies can silently drive up cloud costs. That's where smart resource monitoring comes into play.
In this blog, we'll walk through the key metrics you should monitor to optimize Kubernetes resource usage and reduce costs—especially in cloud environments. Whether you're running production workloads on EKS or just getting started, these best practices can help you stay lean and efficient.
Why Resource Monitoring Matters for K8s Cost Optimization
Kubernetes abstracts infrastructure away, but cloud bills remain painfully real. Poor observability often leads to:
- Overprovisioned workloads (paying for unused CPU/memory)
- Underutilized nodes (wasting instance hours)
- Zombie workloads (idle pods or forgotten namespaces)
- Unbalanced scheduling (causing skewed utilization)
Monitoring helps you catch these early and make informed decisions on scaling, scheduling, and rightsizing.
Key Metrics to Monitor for Cost Optimization
Let's break down the metrics that matter most, and what you can do with them.
1. CPU and Memory Requests vs Usage
Why it matters: Over-provisioning leads to wasted resources; under-provisioning causes instability.
What to monitor:
kube_pod_container_resource_requests_cpu_coresvscontainer_cpu_usage_seconds_totalkube_pod_container_resource_requests_memory_bytesvscontainer_memory_usage_bytes
What to look for:
- Workloads consistently use <30% of their requested resources.
- Pods OOM-killed due to under-provisioned memory.
Actionable tip: Use Vertical Pod Autoscaler (VPA) in recommendation mode to identify tuning opportunities.
2. Node Utilization (CPU/Memory)
Why it matters: Low node utilization means you're paying for idle EC2 capacity.
What to monitor:
node_cpu_utilizationnode_memory_utilization
What to look for:
- Nodes consistently are under 50% utilization.
- Skewed workloads causing some nodes to stay mostly empty.
Actionable tip: Use tools like Karpenter to consolidate underutilized nodes.
If you're looking for an autonomous solution that does this (and more) out of the box, CloudPilot AI intelligently monitors node utilization and automatically replaces underutilized infrastructure with more cost-effective options—no manual tuning required.
3. Pod Scheduling Failures
Why it matters: Failed pod scheduling may lead to cluster overprovisioning.
What to monitor:
kube_pod_status_unschedulablekube_pod_status_phase{phase="Pending"}
What to look for:
- Frequent unschedulable events due to insufficient memory or CPU.
- Scheduling constraints (e.g. taints, affinities) that reduce packing efficiency.
Actionable tip: Revisit affinity/anti-affinity rules, tolerations, and resource requests to allow better bin-packing.
Also consider cost-aware autoscalers like Karpenter or CloudPilot AI to rebalance workloads dynamically and reduce failed scheduling events.
4. Persistent Volume Usage
Why it matters: EBS volumes incur ongoing costs, even if idle or unmounted.
What to monitor:
kubelet_volume_stats_used_byteskube_persistentvolumeclaim_info(to detect unbound PVCs)
What to look for:
- Volumes with little or no data but large allocations.
- Orphaned PVCs and EBS volumes are not attached to any pod. Actionable tip: Regularly audit unused volumes. Consider lifecycle policies to auto-delete old EBS snapshots.
5. Idle Namespaces & Resources
Why it matters: Forgotten test workloads or zombie services can drain resources and rack up costs.
What to monitor:
- Namespaces with no active pods.
- Services without endpoints.
What to look for:
- Old, unused dev/test namespaces.
- CronJobs or Deployments with no traffic.
Actionable tip: Use cleanup scripts or TTL controllers to automatically clean up idle resources over time.
Setting Up Metrics Monitoring on EKS
To track these metrics effectively, you'll need a robust monitoring stack. Here’s a simple setup to get started:
Use Prometheus + Grafana
Installation:
Use Helm to install the kube-prometheus-stack:
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
helm install monitoring prometheus-community/kube-prometheus-stack --namespace monitoring --create-namespace
This will deploy:
- Prometheus (metrics collection)
- Grafana (visualization)
- Alertmanager (optional)
Tip: Use default dashboards for node and pod resource usage. Customize them for idle resource detection and request-vs-usage comparisons.
Enable Cloud Cost Allocation
AWS supports native cost metrics via CloudWatch Container Insights. You can also enrich these metrics by exporting them to Prometheus or third-party cost observability platforms for deeper analysis.
Automate Alerts for Cost Risks
Use Prometheus alert rules for:
- CPU/memory usage below thresholds
- Unschedulable pods
- Unused PVCs
- Underutilized nodes
You can route these alerts to Slack, PagerDuty, or email.
Tools That Make It Easier
| Tool | Use Case |
|---|---|
| CloudPilot AI | AI-powered automation to optimize node usage, spot pricing, and cost efficiency across EKS clusters |
| Karpenter | Smart autoscaling with efficient bin-packing |
| VPA | Suggests optimal resource requests |
| Goldilocks | Helps rightsize deployments using VPA |
| Lens | GUI to monitor pods, nodes, and workloads |
Conclusion
Kubernetes doesn't magically reduce your cloud bill. In fact, without visibility, it's easy to overspend. But with the right metrics and monitoring practices in place, you can make smart decisions that balance performance and cost.
Start with small wins: identify underutilized pods, tweak requests, and reclaim idle volumes. Or go a step further with tools like CloudPilot AI, which brings intelligent automation to your EKS cluster—detecting cost risks, optimizing node selection, and managing Spot interruptions in real time.
Less waste, more performance—because every core and gigabyte counts.
