Karpenter Autoscaling for Amazon EKS

Intelligent, high-performance node autoscaling for Amazon EKS that provisions nodes in seconds, automatically selects optimal instance types, and reduces costs by 20-70% through Spot integration and consolidation.

Overview

Karpenter is the recommended autoscaler for production EKS workloads (2025), replacing Cluster Autoscaler with:

Speed: Provisions nodes in seconds (vs minutes with Cluster Autoscaler)
Intelligence: Automatically selects optimal instance types based on pod requirements
Flexibility: No need to configure node groups - direct EC2 instance provisioning
Cost Optimization: 20-70% cost reduction through better bin-packing and Spot integration
Consolidation: Automatic node consolidation when underutilized or empty

Real-World Results:

20% overall AWS bill reduction
Up to 90% savings for CI/CD workloads
70% reduction in monthly compute costs
15-30% waste reduction with faster scale-up

When to Use

Replacing Cluster Autoscaler with faster, smarter autoscaling
Optimizing EKS cluster costs (target: 20%+ savings)
Implementing Spot instance strategies (30-70% Spot mix)
Need sub-minute node provisioning (seconds vs minutes)
Workloads with variable resource requirements
Multi-instance-type flexibility without node group management
GPU or specialized instance provisioning
Consolidating underutilized nodes automatically

Prerequisites

EKS cluster running Kubernetes 1.23+
Terraform or Helm for installation
IRSA or EKS Pod Identity enabled
Small node group for Karpenter controller (2-3 nodes)
VPC subnets and security groups tagged for Karpenter discovery

Quick Start

1. Install Karpenter (Helm)

# Add Karpenter Helm repo
helm repo add karpenter https://charts.karpenter.sh
helm repo update

# Install Karpenter v1.0+
helm upgrade --install karpenter karpenter/karpenter \
  --namespace kube-system \
  --set settings.clusterName=my-cluster \
  --set settings.interruptionQueue=my-cluster \
  --set controller.resources.requests.cpu=1 \
  --set controller.resources.requests.memory=1Gi \
  --set controller.resources.limits.cpu=1 \
  --set controller.resources.limits.memory=1Gi \
  --wait

See: references/installation.md for complete setup including IRSA/Pod Identity

2. Create NodePool and EC2NodeClass

NodePool (defines scheduling requirements and limits):

apiVersion: karpenter.sh/v1
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"]
        - key: karpenter.k8s.aws/instance-category
          operator: In
          values: ["c", "m", "r"]  # Compute, general, memory-optimized
        - key: karpenter.k8s.aws/instance-generation
          operator: Gt
          values: ["4"]  # Gen 5+
      nodeClassRef:
        group: karpenter.k8s.aws
        kind: EC2NodeClass
        name: default
  limits:
    cpu: "1000"
    memory: "1000Gi"
  disruption:
    consolidationPolicy: WhenUnderutilized
    consolidateAfter: 30s
    budgets:
      - nodes: "10%"
---
apiVersion: karpenter.k8s.aws/v1
kind: EC2NodeClass
metadata:
  name: default
spec:
  amiFamily: AL2023  # Amazon Linux 2023
  role: KarpenterNodeRole-my-cluster
  subnetSelectorTerms:
    - tags:
        karpenter.sh/discovery: my-cluster
  securityGroupSelectorTerms:
    - tags:
        karpenter.sh/discovery: my-cluster
  blockDeviceMappings:
    - deviceName: /dev/xvda
      ebs:
        volumeSize: 100Gi
        volumeType: gp3
        encrypted: true
        deleteOnTermination: true

kubectl apply -f nodepool.yaml

See: references/nodepools.md for advanced NodePool patterns

3. Deploy Workload and Watch Autoscaling

# Deploy test workload
kubectl create deployment inflate --image=public.ecr.aws/eks-distro/kubernetes/pause:3.7 \
  --replicas=0

# Scale up to trigger node provisioning
kubectl scale deployment inflate --replicas=10

# Watch Karpenter provision nodes (seconds!)
kubectl logs -f -n kube-system -l app.kubernetes.io/name=karpenter -c controller

# Verify nodes
kubectl get nodes -l karpenter.sh/nodepool=default

# Scale down to trigger consolidation
kubectl scale deployment inflate --replicas=0

# Watch Karpenter consolidate (30s after scale-down)
kubectl logs -f -n kube-system -l app.kubernetes.io/name=karpenter -c controller

4. Monitor and Optimize

# Check NodePool status
kubectl get nodepools

# View disruption metrics
kubectl describe nodepool default

# Monitor provisioning decisions
kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter | grep -i "launched\|terminated"

# Cost optimization metrics
kubectl top nodes

See: references/optimization.md for cost optimization strategies

Core Concepts

Karpenter v1.0 Architecture

Key Resources (v1.0+):

NodePool: Defines node scheduling requirements, limits, and disruption policies
EC2NodeClass: AWS-specific configuration (AMIs, instance types, subnets, security groups)
NodeClaim: Karpenter's representation of a node request (auto-created)

How It Works:

Pod becomes unschedulable
Karpenter evaluates pod requirements (CPU, memory, affinity, taints/tolerations)
Karpenter selects optimal instance type from 600+ options
Karpenter provisions EC2 instance directly (no node groups)
Node joins cluster in 30-60 seconds
Pod scheduled to new node

Consolidation:

Continuously monitors node utilization
Consolidates underutilized nodes (bin-packing)
Drains and deletes empty nodes
Replaces nodes with cheaper alternatives
Respects Pod Disruption Budgets

NodePool vs Cluster Autoscaler Node Groups

Feature	Karpenter NodePool	Cluster Autoscaler
Provisioning Speed	30-60 seconds	2-5 minutes
Instance Selection	Automatic (600+ types)	Manual (pre-defined)
Bin-Packing	Intelligent	Limited
Spot Integration	Built-in, intelligent	Requires node groups
Consolidation	Automatic	Manual
Configuration	Single NodePool	Multiple node groups
Cost Savings	20-70%	10-20%

Common Workflows

Workflow 1: Install Karpenter with Terraform

Use case: Production-grade installation with infrastructure as code

# Karpenter module
module "karpenter" {
  source = "terraform-aws-modules/eks/aws//modules/karpenter"
  version = "~> 20.0"

  cluster_name = module.eks.cluster_name
  irsa_oidc_provider_arn = module.eks.oidc_provider_arn

  # Enable Pod Identity (2025 recommended)
  enable_pod_identity = true

  # Additional IAM policies
  node_iam_role_additional_policies = {
    AmazonSSMManagedInstanceCore = "arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore"
  }

  tags = {
    Environment = "production"
  }
}

# Helm release
resource "helm_release" "karpenter" {
  namespace        = "kube-system"
  name             = "karpenter"
  repository       = "oci://public.ecr.aws/karpenter"
  chart            = "karpenter"
  version          = "1.0.0"

  set {
    name  = "settings.clusterName"
    value = module.eks.cluster_name
  }

  set {
    name  = "settings.interruptionQueue"
    value = module.karpenter.queue_name
  }

  set {
    name  = "serviceAccount.annotations.eks\\.amazonaws\\.com/role-arn"
    value = module.karpenter.iam_role_arn
  }
}

Steps:

Review references/installation.md
Configure Terraform module with cluster details
Apply infrastructure: terraform apply
Verify installation: kubectl get pods -n kube-system -l app.kubernetes.io/name=karpenter
Tag subnets and security groups for discovery
Deploy NodePool and EC2NodeClass

See: references/installation.md for complete Terraform setup

Workflow 2: Configure Spot/On-Demand Mix (30/70)

Use case: Optimize costs while maintaining availability (recommended: 30% On-Demand, 70% Spot)

Critical NodePool (On-Demand only):

apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: critical
spec:
  template:
    spec:
      requirements:
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["on-demand"]
        - key: karpenter.k8s.aws/instance-category
          operator: In
          values: ["m", "c"]
      nodeClassRef:
        group: karpenter.k8s.aws
        kind: EC2NodeClass
        name: default
      taints:
        - key: "critical"
          value: "true"
          effect: "NoSchedule"
  limits:
    cpu: "200"
  weight: 100  # Higher priority

Flexible NodePool (Spot preferred):

apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: flexible
spec:
  template:
    spec:
      requirements:
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["spot", "on-demand"]
        - key: karpenter.k8s.aws/instance-category
          operator: In
          values: ["c", "m", "r"]
        - key: karpenter.k8s.aws/instance-generation
          operator: Gt
          values: ["4"]
      nodeClassRef:
        group: karpenter.k8s.aws
        kind: EC2NodeClass
        name: default
  limits:
    cpu: "800"
  disruption:
    consolidationPolicy: WhenUnderutilized
    budgets:
      - nodes: "20%"
  weight: 10  # Lower priority (use after critical)

Pod tolerations for critical workloads:

spec:
  tolerations:
    - key: "critical"
      operator: "Equal"
      value: "true"
      effect: "NoSchedule"
  nodeSelector:
    karpenter.sh/capacity-type: on-demand

Steps:

Create critical NodePool for databases, stateful apps (On-Demand)
Create flexible NodePool for stateless apps (Spot preferred)
Use taints/tolerations to separate critical workloads
Monitor Spot interruptions: kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter | grep -i interrupt

See: references/nodepools.md for Spot strategies

Workflow 3: Enable Consolidation for Cost Savings

Use case: Reduce costs by automatically consolidating underutilized nodes

Aggressive consolidation (development/staging):

spec:
  disruption:
    consolidationPolicy: WhenEmptyOrUnderutilized
    consolidateAfter: 30s  # Consolidate quickly
    budgets:
      - nodes: "50%"  # Allow disrupting 50% of nodes

Conservative consolidation (production):

spec:
  disruption:
    consolidationPolicy: WhenUnderutilized
    consolidateAfter: 5m  # Wait 5 minutes before consolidating
    budgets:
      - nodes: "10%"  # Limit disruption to 10% of nodes at a time
      - schedule: "0 9-17 * * MON-FRI"  # Only during business hours
        nodes: "20%"
      - schedule: "0 0-8,18-23 * * *"  # Off-hours
        nodes: "5%"

Pod Disruption Budget (protect critical pods):

apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: critical-app-pdb
spec:
  minAvailable: 2
  selector:
    matchLabels:
      app: critical-app

Steps:

Review references/optimization.md
Set consolidation policy (WhenEmpty, WhenUnderutilized, WhenEmptyOrUnderutilized)
Configure consolidateAfter delay (30s-5m)
Set disruption budgets (% of nodes)
Create PodDisruptionBudgets for critical apps
Monitor consolidation: kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter | grep consolidat

Expected savings: 15-30% additional reduction beyond Spot savings

See: references/optimization.md for consolidation best practices

Workflow 4: Migrate from Cluster Autoscaler

Use case: Upgrade from Cluster Autoscaler to Karpenter for better performance and cost savings

Migration strategy (zero-downtime):

Install Karpenter (runs alongside Cluster Autoscaler)

helm install karpenter karpenter/karpenter --namespace kube-system

Create NodePool with distinct labels

spec:
  template:
    metadata:
      labels:
        provisioner: karpenter

Migrate workloads gradually

# Add node selector to new deployments
spec:
  nodeSelector:
    provisioner: karpenter

Monitor both autoscalers

# Watch Karpenter
kubectl logs -f -n kube-system -l app.kubernetes.io/name=karpenter

# Watch Cluster Autoscaler
kubectl logs -f -n kube-system -l app=cluster-autoscaler

Gradually scale down CA node groups

# Reduce desired size of CA node groups
aws eks update-nodegroup-config \
  --cluster-name my-cluster \
  --nodegroup-name ca-nodes \
  --scaling-config desiredSize=1,minSize=0,maxSize=3

Remove Cluster Autoscaler tags

# Remove tags from node groups
# k8s.io/cluster-autoscaler/enabled
# k8s.io/cluster-autoscaler/<cluster-name>

Uninstall Cluster Autoscaler

helm uninstall cluster-autoscaler -n kube-system

Testing checklist:

Karpenter provisions nodes successfully
Pods schedule on Karpenter nodes
Consolidation works as expected
Spot interruptions handled gracefully
No unschedulable pods
Cost metrics show improvement

Rollback plan: Keep CA node groups at min size until confident in Karpenter

Workflow 5: GPU Node Provisioning

Use case: Automatically provision GPU instances for ML workloads

GPU NodePool:

apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: gpu
spec:
  template:
    spec:
      requirements:
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["on-demand"]  # GPU typically on-demand
        - key: karpenter.k8s.aws/instance-family
          operator: In
          values: ["g4dn", "g5", "p3", "p4d"]
        - key: karpenter.k8s.aws/instance-gpu-count
          operator: Gt
          values: ["0"]
      nodeClassRef:
        group: karpenter.k8s.aws
        kind: EC2NodeClass
        name: gpu
      taints:
        - key: "nvidia.com/gpu"
          value: "true"
          effect: "NoSchedule"
  limits:
    cpu: "1000"
    nvidia.com/gpu: "8"
---
apiVersion: karpenter.k8s.aws/v1
kind: EC2NodeClass
metadata:
  name: gpu
spec:
  amiFamily: AL2  # AL2 with GPU drivers
  amiSelectorTerms:
    - alias: al2@latest  # Latest GPU-enabled AMI
  role: KarpenterNodeRole-my-cluster
  subnetSelectorTerms:
    - tags:
        karpenter.sh/discovery: my-cluster
  securityGroupSelectorTerms:
    - tags:
        karpenter.sh/discovery: my-cluster
  userData: |
    #!/bin/bash
    # Install NVIDIA device plugin
    /etc/eks/bootstrap.sh my-cluster

GPU workload:

apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  tolerations:
    - key: "nvidia.com/gpu"
      operator: "Exists"
      effect: "NoSchedule"
  containers:
  - name: cuda-container
    image: nvidia/cuda:11.8.0-base-ubuntu22.04
    command: ["nvidia-smi"]
    resources:
      limits:
        nvidia.com/gpu: 1

See: references/nodepools.md for GPU configuration details

Key Configuration

NodePool Resource Limits

Prevent runaway scaling:

spec:
  limits:
    cpu: "1000"       # Max 1000 CPUs across all nodes in pool
    memory: "1000Gi"  # Max 1000Gi memory
    nvidia.com/gpu: "8"  # Max 8 GPUs

Disruption Controls

Balance cost savings with stability:

spec:
  disruption:
    # When to consolidate
    consolidationPolicy: WhenUnderutilized | WhenEmpty | WhenEmptyOrUnderutilized

    # Delay before consolidating (prevent flapping)
    consolidateAfter: 30s  # Default: 30s

    # Node expiration (security patching)
    expireAfter: 720h  # 30 days

    # Disruption budgets (rate limiting)
    budgets:
      - nodes: "10%"  # Max 10% of nodes disrupted at once
        reasons:
          - Underutilized
          - Empty
      - schedule: "0 0-8 * * *"  # Off-hours: more aggressive
        nodes: "50%"

Instance Type Flexibility

Maximize Spot availability and cost savings:

spec:
  template:
    spec:
      requirements:
        # Architecture
        - key: kubernetes.io/arch
          operator: In
          values: ["amd64", "arm64"]  # Include ARM for savings

        # Instance categories (c=compute, m=general, r=memory)
        - key: karpenter.k8s.aws/instance-category
          operator: In
          values: ["c", "m", "r"]

        # Instance generation (5+ for best performance/cost)
        - key: karpenter.k8s.aws/instance-generation
          operator: Gt
          values: ["4"]

        # Instance size (exclude large sizes if not needed)
        - key: karpenter.k8s.aws/instance-size
          operator: NotIn
          values: ["metal", "32xlarge", "24xlarge"]

        # Capacity type
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["spot", "on-demand"]

Result: Karpenter selects from 600+ instance types, maximizing Spot availability

Monitoring and Troubleshooting

Key Metrics

# NodePool status
kubectl get nodepools

# NodeClaim status (pending provisions)
kubectl get nodeclaims

# Node events
kubectl get events --field-selector involvedObject.kind=Node

# Karpenter controller logs
kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter -c controller --tail=100

# Filter for provisioning decisions
kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter | grep "launched instance"

# Filter for consolidation events
kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter | grep "consolidating"

# Spot interruption warnings
kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter | grep "interrupt"

Common Issues

1. Nodes not provisioning:

# Check NodePool status
kubectl describe nodepool default

# Check for unschedulable pods
kubectl get pods -A --field-selector=status.phase=Pending

# Review Karpenter logs for errors
kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter | grep -i error

Common causes:

Insufficient IAM permissions
Subnet/security group tags missing
Resource limits exceeded
No instance types match requirements

2. Excessive consolidation (pod restarts):

# Increase consolidateAfter delay
spec:
  disruption:
    consolidateAfter: 5m  # Increase from 30s

3. Spot interruptions causing issues:

# Reduce Spot ratio
- key: karpenter.sh/capacity-type
  operator: In
  values: ["on-demand"]  # Use more on-demand

Best Practices

Cost Optimization

✅ Use 30% On-Demand, 70% Spot for optimal cost/stability balance
✅ Enable consolidation (WhenUnderutilized)
✅ Include ARM instances (Graviton) for 20% additional savings
✅ Set instance generation > 4 for best price/performance
✅ Use multiple instance families (c, m, r) for Spot diversity

Reliability

✅ Set Pod Disruption Budgets for critical applications
✅ Use multiple availability zones
✅ Configure disruption budgets (10-20% for production)
✅ Test Spot interruption handling
✅ Use On-Demand for stateful workloads (databases)

Security

✅ Use IRSA or Pod Identity (not node IAM roles)
✅ Enable EBS encryption in EC2NodeClass
✅ Set expireAfter for regular node rotation (720h/30 days)
✅ Use Amazon Linux 2023 (AL2023) AMIs
✅ Tag resources for cost allocation

Performance

✅ Use dedicated NodePool for Karpenter controller (On-Demand, no consolidation)
✅ Set appropriate resource limits to prevent runaway scaling
✅ Monitor provisioning latency (should be <60s)
✅ Use topology spread constraints for pod distribution

Reference Documentation

Detailed Guides (load on-demand):

references/installation.md - Complete installation with Helm, Terraform, IRSA, Pod Identity
references/nodepools.md - NodePool and EC2NodeClass configuration patterns
references/optimization.md - Cost optimization, consolidation, disruption budgets

Official Resources:

Community Examples:

Quick Reference

Installation

helm upgrade --install karpenter oci://public.ecr.aws/karpenter/karpenter \
  --version 1.0.0 \
  --namespace kube-system \
  --set settings.clusterName=my-cluster

Basic NodePool

apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: default
spec:
  template:
    spec:
      requirements:
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["spot", "on-demand"]
      nodeClassRef:
        kind: EC2NodeClass
        name: default
  limits:
    cpu: "1000"
  disruption:
    consolidationPolicy: WhenUnderutilized

Monitor

kubectl logs -f -n kube-system -l app.kubernetes.io/name=karpenter

Cost Savings Formula

Spot instances: 70-80% savings vs On-Demand
Consolidation: Additional 15-30% reduction
Better bin-packing: 10-20% waste reduction
Total: 20-70% overall cost reduction

Next Steps: Install Karpenter using references/installation.md, then configure NodePools with references/nodepools.md

karpenter-autoscaling

Karpenter Autoscaling for Amazon EKS

Overview

When to Use

Prerequisites

Quick Start

1. Install Karpenter (Helm)

2. Create NodePool and EC2NodeClass

3. Deploy Workload and Watch Autoscaling

4. Monitor and Optimize

Core Concepts

Karpenter v1.0 Architecture

NodePool vs Cluster Autoscaler Node Groups

Common Workflows

Workflow 1: Install Karpenter with Terraform

Workflow 2: Configure Spot/On-Demand Mix (30/70)

Workflow 3: Enable Consolidation for Cost Savings

Workflow 4: Migrate from Cluster Autoscaler

Workflow 5: GPU Node Provisioning

Key Configuration

NodePool Resource Limits

Disruption Controls

Instance Type Flexibility

Monitoring and Troubleshooting

Key Metrics

Common Issues

Best Practices

Cost Optimization

Reliability

Security

Performance

Reference Documentation

Quick Reference

Installation

Basic NodePool

Monitor

Cost Savings Formula