10 best practices for optimizing Kubernetes on AWS

Key points:

• Architect for exhaustion: Enable VPC CNI Prefix Delegation before you scale, or watch IPv4 exhaustion break your production clusters.
• Kill idle compute: Automate the termination of non-production environments to enforce strict FinOps controls and stop wasting AWS credits.
• Centralize your fleet: Stop writing bespoke Terraform for every EKS cluster and move to intent-based agentic orchestration.

Treating Kubernetes like a massive, single Linux server guarantees spiraling cloud bills and constant firefighting. At fleet scale, manual interventions and default configurations crumble under their own weight.

To extract actual ROI from Amazon EKS, infrastructure teams must shift from reactive patching to intentional, automated fleet governance. These ten practices outline the concrete steps required to secure, scale, and optimize AWS Kubernetes environments for long-term production viability.

The 1,000-cluster reality: why manual EKS management fails

Managing a single Kubernetes cluster on AWS is a solved problem. As enterprises scale from a handful of development clusters to thousands of production environments, manual scaling interventions become a severe bottleneck. Platform Architects face configuration drift, fractured RBAC policies, and unpredictable cloud bills.

Scaling Kubernetes is no longer about configuring a single Auto Scaling Group. It requires agentic automation. Intent-based configurations must dictate scaling behavior globally to free SREs from manual YAML fatigue.

Day 2 Operations & Scaling Checklist

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10 best practices for optimizing Kubernetes on AWS

Treating Kubernetes like a massive, single Linux server will cause it to eagerly consume every dollar and IP address you provide. The following practices are required to optimize Amazon EKS fleets securely and cost-effectively.

1. Standardize on Amazon EKS

Do not build your own control plane on Amazon EC2. Managing etcd backups, API server upgrades, and controller manager high availability drains engineering capacity. AWS manages the Amazon EKS control plane highly effectively. Offloading the master node components allows your team to focus exclusively on worker node capacity and workload optimization.

2. Enable prefix delegation to prevent IP exhaustion

By default, the Amazon VPC CNI assigns a single secondary IPv4 address to every pod. On smaller EC2 instances, you will hit the Elastic Network Interface (ENI) limit and exhaust your subnet IPs rapidly. You must enable Prefix Delegation. This assigns a full /28 prefix to an ENI, drastically increasing pod density.

# enable Prefix Delegation in AWS VPC CNI
kubectl set env daemonset aws-node -n kube-system ENABLE_PREFIX_DELEGATION=true

3. Replace cluster autoscaler with Karpenter

The legacy AWS Cluster Autoscaler relies on Auto Scaling Groups, which are notoriously slow to provision new capacity. Karpenter bypasses Auto Scaling Groups entirely. It reads the requirements of unschedulable pods and provisions the exact right-sized EC2 instance directly.

apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: default
spec:
  template:
    spec:
      requirements:
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["spot", "on-demand"]
        - key: kubernetes.io/arch
          operator: In
          values: ["amd64", "arm64"]
      nodeClassRef:
        name: default
  limits:
    cpu: 1000

4. Stop overlapping HPA and VPA

Engineers often configure the Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) to watch the same CPU utilization metrics. The HPA scales replicas out, dropping the average CPU usage. This prompts the VPA to shrink the pod size, which spikes CPU again. The cluster thrashes indefinitely. Tie your HPA to external queue metrics and restrict VPA to baseline memory profiling.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: backend-worker-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: backend-worker
  minReplicas: 3
  maxReplicas: 50
  metrics:
    - type: External
      external:
        metric:
          name: sqs_queue_length
        target:
          type: AverageValue
          averageValue: 30

5. Enforce strict resource quotas per namespace

Without hard limits, a memory leak in a single application will consume the entire cluster capacity. Enforce ResourceQuotas via native Kubernetes architecture constructs to fence off environments and protect workloads.

apiVersion: v1
kind: ResourceQuota
metadata:
  name: compute-quota
  namespace: development
spec:
  hard:
    requests.cpu: "10"
    requests.memory: 20Gi
    limits.cpu: "20"
    limits.memory: 40Gi

6. Automate environment teardowns for FinOps

Leaving staging clusters running over the weekend destroys cloud budgets. Budget and Risk Owners must enforce strict Kubernetes cost optimization policies. If an environment is not actively receiving traffic during business hours, an agentic system must terminate it.

7. Restrict RBAC to namespace boundaries

Granting cluster-admin privileges to CI/CD pipelines is a catastrophic security risk. Follow the principle of least privilege. Use RoleBindings locked to specific namespaces rather than ClusterRoleBindings.

8. Use AWS load balancer controller for ingress

Do not use the legacy in-tree Kubernetes load balancers. Install the AWS Load Balancer Controller to natively provision Application Load Balancers (ALB) and Network Load Balancers (NLB). This ensures your ingress traffic routes efficiently directly to pod IPs via the AWS VPC CNI.

9. Centralize observability with Prometheus and Datadog

At scale, logs and metrics must leave the cluster. Relying on basic terminal commands is completely unscalable across 1,000 clusters. Stream your metrics to a centralized Prometheus, Grafana, or Datadog instance to identify latency spikes and out-of-memory errors globally.

10. Adopt agentic fleet management

Managing these configurations across a single cluster requires high engineering effort. Enforcing them across thousands of clusters requires an Agentic Kubernetes Management Platform. Qovery abstracts these infrastructure components into intent-based configurations.

# .qovery.yml
application:
  backend-api:
    build_mode: docker
    auto_scaling:
      min_instances: 3
      max_instances: 50
      cpu_threshold: 75

🚀 Real-world proof

Nextools encountered significant challenges managing multi-cloud deployments manually across hundreds of client instances.

⭐ The result: Reduced deployment time for new clusters from days to 30 minutes. Read the Nextools case study.

Conclusion

By utilizing Qovery, platform teams eliminate toil, standardize multi-cluster deployments, and enforce FinOps controls automatically without requiring developers to understand the underlying AWS compute primitives.

We are the new standard for Kubernetes management

Book a demo with our team or sign up to Qovery for free, and see how Qovery can help optimize your Kubernetes applications on AWS

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FAQs

What is the primary cause of IP exhaustion in Amazon EKS?

The default behavior of the Amazon VPC CNI assigns a secondary IP address to every individual pod from the underlying subnet. On smaller instance types, you hit Elastic Network Interface limits rapidly. You must enable Prefix Delegation to assign a block of IPs to the network interface to solve this problem.

Why is Karpenter preferred over the default AWS Cluster Autoscaler?

Karpenter directly provisions right-sized EC2 instances based on the exact compute requirements of unschedulable pods. It bypasses the rigid restrictions of Auto Scaling Groups, significantly reducing scheduling latency and lowering costs by dynamically selecting cheaper instance types and prioritizing spot capacity.

How do you prevent the Horizontal Pod Autoscaler and Vertical Pod Autoscaler from conflicting?

Never bind the HPA and VPA to the exact same metric, such as CPU utilization. If both trigger simultaneously, the cluster will enter an infinite scaling loop. Tie HPA to external load metrics like AWS SQS queue length, and use VPA exclusively for analyzing and right-sizing historical memory consumption.