EKS Best Practices

Comprehensive guidance for designing, deploying, and operating Amazon EKS clusters. Consolidates guidance from the AWS EKS Best Practices Guide, AWS EKS HA/Resiliency Guide, and terraform-aws-modules/terraform-aws-eks examples.

When to Use This Skill

Activate this skill when:

• Designing a new EKS cluster architecture
• Choosing between EKS compute options (Fargate, MNG, Karpenter, Auto Mode)
• Configuring EKS networking (VPC CNI, ingress, service mesh)
• Implementing EKS security (IAM, pod security, secrets)
• Setting up security operations (staying informed, patch cadence, emergency fixes, build hygiene, event response shape)
• Planning cluster upgrades or migrations
• Reviewing EKS architecture decisions
• Working with terraform-aws-modules/terraform-aws-eks examples
• Optimizing EKS cost or scaling to large clusters

Don't use this skill for:

• Generic Kubernetes concepts (Claude knows these)
• Provider-specific API reference (link to AWS docs)
• Non-EKS container orchestration (ECS, Lambda)
• Step-by-step EKS upgrade execution — this skill covers upgrade strategy and architectural decisions, not the per-version procedures themselves.

EKS Architecture Decision Framework

When to Use EKS

EKS Deployment Models

For choosing among and operating the on-premises/hybrid models, see: Hybrid & On-Premises Deployments.

Routing: the advisory question "which on-prem/hybrid model should I choose?" stays in this skill; generating a design document or diagram → eks-design; generating a full production cluster Terraform project or an air-gapped build → eks-build (the hybrid cloud/cluster-side Terraform example itself lives in this skill's terraform-examples.md).

Shared Responsibility

Compute Selection Matrix

Decision Table

Quick Decision Guide

• Default choice: Karpenter — best balance of flexibility, cost, and automation
• Zero ops priority: EKS Auto Mode — AWS manages nodes, add-ons, and scaling via managed Karpenter. Best for teams that want Kubernetes benefits without operational overhead around upgrades, autoscaling, load balancing, and storage
• Serverless/batch: Fargate — no nodes to manage, per-pod billing
• Predictable, stable: MNG — familiar ASG model, managed updates
• Custom requirements: Self-managed — full control, highest overhead

✅ DO:

• Use Karpenter as the default node autoscaler for new clusters
• Run system components (CoreDNS, Karpenter) on MNG or Fargate
• Use multiple instance types for availability and cost optimization

❌ DON'T:

• Use self-managed nodes without a specific technical requirement
• Run Fargate for GPU or DaemonSet-dependent workloads
• Mix Karpenter and Cluster Autoscaler on the same node groups

Hybrid & On-Premises Deployments

Three models run EKS outside a standard in-Region cluster. Pick by who owns the control plane and whether the site stays connected:

Critical rule: split by why the site is disconnected. Permanently air-gapped/isolated → EKS Anywhere (the only fully air-gapped model). A connected site that must keep operating through Region outages on AWS-owned hardware (data residency/sovereignty) → Outposts local clusters (Outposts racks only) — disconnect-tolerant, not air-gapped (IAM/IRSA/KMS/EBS-PV/Route 53 are unavailable offline). EKS Hybrid Nodes and Outposts extended clusters both depend on a reliable connection to an AWS Region.

For detailed hybrid-deployment guidance, see: Hybrid & On-Premises Deployments

Networking Quick Reference

VPC CNI Mode Decision

Ingress Pattern Selection

IPv4 vs IPv6

For detailed networking guidance, see: Networking — VPC CNI & IP | Networking — Ingress & DNS

Security Essentials

IAM Strategy

Key rules:

• ✅ Use Pod Identity for new workloads — simpler setup, session tags, role chaining
• ✅ Use EKS access entries (API mode) over aws-auth ConfigMap
• ✅ Move VPC CNI permissions from node role to Pod Identity/IRSA
• ❌ Don't use wildcard conditions in IRSA trust policies
• ❌ Don't attach application permissions to node IAM roles

Pod Security Admission

Apply Pod Security Admission (PSA) labels to every namespace. The stance, which the references follow: enforce restricted for application workloads; baseline is the floor, only for a namespace that cannot meet restricted yet, with warn and audit at restricted to track the gap; privileged only for system namespaces that need it (kube-system, CNI, CSI and agent daemonsets).

# Application namespace
metadata:
  labels:
    pod-security.kubernetes.io/enforce: restricted
    pod-security.kubernetes.io/warn: restricted
    pod-security.kubernetes.io/audit: restricted
---
# Floor: a namespace still being brought up to restricted
metadata:
  labels:
    pod-security.kubernetes.io/enforce: baseline
    pod-security.kubernetes.io/warn: restricted
    pod-security.kubernetes.io/audit: restricted

Secrets Management

Envelope encryption is on by default (≥1.28) and covers all Kubernetes API data; layer your own KMS CMK when you need key control and CloudTrail visibility. Source: https://docs.aws.amazon.com/eks/latest/userguide/envelope-encryption.html

For detailed security guidance, see: Security Reference | Runtime & Network | Supply Chain & Compliance

Security Operations

Hardening is half the job; the other half is running security over time. Four practices, detailed in Security Operations:

Live checks: eks-security. This skill does not read a cluster. Route these asks to eks-security:

• "Am I exposed to this CVE, bulletin or advisory?": eks-security C1 CVE and advisory review
• "We have a security event on the cluster; what do we do?": eks-security C2 security event response
• "Would our setup contain a compromised pod?": eks-security C3 posture check
• "How far behind latest are we?" with no CVE in play: eks-upgrade-check

Reliability Essentials

Pod Disruption Budgets

Create PDBs for every production workload with >1 replica:

Health Probe Strategy

Critical rule: Liveness probes must NOT check external dependencies. If the database goes down and liveness checks the DB, ALL pods restart — causing cascading failure.

Graceful Shutdown Pattern

spec:
  terminationGracePeriodSeconds: 60
  containers:
  - lifecycle:
      preStop:
        sleep:
          seconds: 15  # Kubernetes 1.30+; older clusters: exec: command: ["/bin/sh", "-c", "sleep 15"]

Why sleep 15: Gives kube-proxy and load balancer time to remove the pod from traffic routing before SIGTERM. The native sleep action is run by the kubelet, so the image needs no shell (distroless, scratch).

Multi-AZ Distribution

topologySpreadConstraints:
- maxSkew: 1
  topologyKey: topology.kubernetes.io/zone
  whenUnsatisfiable: DoNotSchedule
  minDomains: 3   # 3 assumes 3 zones; set to the zones your nodes can launch in; too high leaves pods Pending
  labelSelector:
    matchLabels:
      app: my-app   # must match the pod's own labels

For detailed reliability guidance, see: Reliability & Resiliency — Core (see also reliability-advanced.md for DR, deployment strategies, and large-cluster guidance)

Cluster Upgrade Strategy

Upgrade Sequence (Strict Order)

1. Control Plane → 2. EKS Add-ons → 3. Data Plane → 4. Custom Add-ons

Pre-Upgrade Checklist

1. Check EKS Cluster Insights for upgrade readiness
2. Scan for deprecated APIs (Pluto, kube-no-trouble)
3. Verify add-on compatibility with target version
4. Test in non-prod environment first
5. Ensure PDBs are configured for graceful node drain
6. Back up cluster state (Velero or GitOps repo)

Upgrade Strategy Decision

Native in-place rollback to N-1 is supported within 7 days of an in-place upgrade (all regions; Auto Mode rolls back nodes automatically; rollback into an extended-support version requires setting the cluster upgrade policy to EXTENDED first). update-cluster-version supports type VersionRollback, gated by a ROLLBACK_READINESS insight; add-ons/data-plane roll back separately. Blue-green retains independent rationale for post-7-day windows, multi-minor jumps, and data-plane isolation — not "because you can't roll back."

Data Plane with Karpenter

Karpenter automatically replaces nodes via drift detection after control plane upgrade. Control the speed with disruption.budgets:

disruption:
  budgets:
  - nodes: "10%"  # Max 10% of nodes replaced at a time

For detailed upgrade guidance, see: Cluster Upgrades Reference

Autoscaling Quick Reference

Node Autoscaler Selection

Cluster Autoscaler's ~60-90s scale-up assumes cold EC2 launches; MNG EC2 warm pools (2026-04) pre-initialize instances and cut that latency substantially for MNG-backed node groups.

Pod Autoscaler Selection

For detailed autoscaling guidance, see: Autoscaling Reference | Karpenter Reference

Preparing for a Known Traffic Peak

For a planned peak — a flash sale, marketing push, product launch, or seasonal event — the arrival is a near-instantaneous step at a known clock time, so favor scheduled pre-scaling (set floors ahead of the trigger) over relying on reactive autoscaling to catch up. Pre-warm all three layers (control plane, nodes, pods) and hold the floor across the whole event window rather than scaling down between peaks.

Critical rule: A load test that passes on request rate can still miss the failure that hits production — if it never reproduces the resource-consumption pattern (memory working-set, connection count) of the event's actual access patterns, especially for new features. Test consumption shapes, not just throughput, and act on findings before the event.

For detailed surge-readiness guidance, see: Surge Readiness Reference

Terraform Examples Quick Start

Based on terraform-aws-modules/terraform-aws-eks.

Example Selection

Common Deployment Topologies

Private cluster with Karpenter:

VPC (3 AZs, terraform-aws-modules/vpc/aws)
├── Private subnets → EKS nodes (MNG for system, Karpenter for workloads)
├── Public subnets  → ALB (internet-facing)
├── Intra subnets   → EKS control plane ENIs
└── NAT Gateway     → 1 per AZ for production

Multi-tenant platform:

EKS Cluster (terraform-aws-modules/eks/aws)
├── kube-system         (platform: CoreDNS, kube-proxy, VPC CNI)
├── karpenter           (Karpenter controller on MNG)
├── monitoring          (shared: Prometheus, Grafana)
├── ingress             (shared: AWS LBC)
├── team-a namespace    (RBAC, NetworkPolicy, ResourceQuota)
├── team-b namespace    (RBAC, NetworkPolicy, ResourceQuota)
└── team-c namespace    (RBAC, NetworkPolicy, ResourceQuota)

For detailed examples and terraform patterns, see: Terraform Examples Reference

Cost Optimization Quick Wins

For detailed cost guidance, see: Cost Optimization Reference | For scalability guidance, see: Scalability Reference

Observability Quick Reference

Essential: Enable EKS audit logging and GuardDuty EKS Runtime Monitoring for security visibility.

For detailed observability guidance, see: Observability Reference

EKS Capabilities

EKS Capabilities are AWS-managed features installed and updated as part of the EKS platform. They run in AWS-owned infrastructure separate from your clusters, with AWS handling scaling, patching, and upgrading.

Combined pattern: ArgoCD deploys ACK resources + KRO compositions via GitOps, providing a single workflow for both infrastructure and applications.

For detailed ArgoCD patterns, see: ArgoCD Patterns Reference

Sources:

• EKS Capabilities Documentation
• AWS Blog — Deep dive: Simplifying resource orchestration with Amazon EKS Capabilities

Detailed References

This skill uses progressive disclosure — essential guidance is in this main file, detailed reference material is loaded on demand:

• Security — IAM, Cluster Access Manager, Pod Identity, IRSA, pod security standards, multi-tenancy, secrets management, data encryption
• Security — Runtime & Network — Runtime threat detection (GuardDuty, seccomp, AppArmor, Falco), network policies, SG for pods, encryption in transit, detective controls
• Security — Supply Chain & Compliance — Image security (SBOMs, attestations, ECR hardening), infrastructure hardening (Bottlerocket, CIS benchmarks), regulatory compliance, incident response
• Security Operations: staying informed (bulletins, CVE feeds, alerting), patch cadence and the emergency fix path, build hygiene (digest pinning, lockfiles, scanner limits), security event response shape, routing to eks-security for live checks
• Networking — VPC CNI modes (secondary IP, prefix delegation, custom networking), subnet/CIDR planning, IPv4 vs IPv6, Security Groups for Pods, IP address management
• Networking — Ingress & DNS — Ingress patterns (ALB, NLB, Gateway API), AWS Load Balancer Controller, service mesh, DNS/CoreDNS tuning, private cluster connectivity
• Reliability & Resiliency — Core — HA patterns, PDBs, health probes, load balancer health checks, lifecycle hooks, topology spread, resource management
• Reliability & Resiliency — Advanced — disaster recovery, zonal shift, deployment strategies, large cluster guidance, chaos engineering, admission-controller topology enforcement
• Surge Readiness — preparing for a known traffic peak (flash sale, marketing push, product launch, seasonal event): spike shape vs magnitude, scheduled pre-scaling, capacity assurance (ODCR, quotas, LB warm-up), load-test realism, graceful degradation, and a descriptive pre-event readiness checklist
• Autoscaling — Autoscaler selection, Cluster Autoscaler (IAM, Spot, overprovisioning, parameter tuning), HPA, VPA, KEDA, CoreDNS autoscaling
• Karpenter — Operational best practices, NodePools, EC2NodeClass, Spot/interruption handling, consolidation, multiple NodePool strategy, cost controls, resource management, private clusters, CoreDNS with Karpenter
• Cluster Upgrades — In-place and blue-green upgrades, pre-upgrade validation, add-on management, API deprecation detection, version skew policy, Bottlerocket updates, rollback procedures
• Cost Optimization — CFM framework, compute/networking/storage cost strategies, observability cost management, Spot, Graviton, tagging, Kubecost
• Scalability — Scaling theory (churn rate, QPS), control plane (APF, config parameters, monitoring), data plane (node sizing, diversity), cluster services (CoreDNS, Metrics Server), workload patterns, IPVS, large-cluster guidance
• Observability — Observability strategy, CloudWatch Container Insights & Application Signals, Prometheus/Grafana, control plane monitoring, network performance monitoring, logging architecture, distributed tracing, GPU/AI-ML observability, detective controls, alerting patterns
• Terraform Examples — terraform-aws-modules/terraform-aws-eks examples, submodules, add-on management, Provisioned Control Plane, EFA, VPC patterns, deployment topologies
• ArgoCD Patterns — ArgoCD architecture, App of Apps, ApplicationSets, GitOps Bridge, multi-cluster patterns (hub-and-spoke, decentralized, hybrid), EKS ArgoCD Capability (managed vs self-managed, migration), ACK/KRO integration, multi-tenant RBAC
• Container Registry — ECR architecture, operating models, image promotion, vulnerability scanning, base image curation, lifecycle policies, pull-through cache, repository creation templates, managed signing (AWS Signer), archival storage class, registry configuration
• EKS Auto Mode — Auto Mode architecture, managed NodePools/NodeClasses, migration from standard EKS, comparison with self-managed Karpenter, limitations and FAQ
• Hybrid & On-Premises Deployments — EKS Hybrid Nodes, EKS Anywhere, and EKS on Outposts: model-selection decision boundary, per-model networking/CIDR/CNI, disconnection behavior, compute & autoscaling support, identity, lifecycle, and a cross-model support matrix

How to use: When you need detailed information on a topic, reference the appropriate guide. Claude will load it on demand.

Sources

• AWS EKS Best Practices Guide
• terraform-aws-modules/terraform-aws-eks