Cluster autoscaler vs Karpenter

Absolutely, Pavan — let’s make a comprehensive, interview-ready comparison of Cluster Autoscaler vs Karpenter from a DevOps/SRE perspective. I’ll cover definition, setup, how it works, results, pros & cons, and the winner in detail.

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1️⃣ Cluster Autoscaler (CA)

Definition

Cluster Autoscaler is a Kubernetes component that automatically adjusts the size of a cluster (number of nodes) based on the resource requests of pods. It adds nodes when pods are pending due to insufficient resources and removes nodes when they are underutilized.

• Supported on AWS EKS, GKE, AKS, and other managed Kubernetes services.

• Focuses on node scaling, not pod-level scaling.

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Setup

1. Install Helm chart / Deployment

helm repo add autoscaler https://kubernetes.github.io/autoscaler
helm install cluster-autoscaler autoscaler/cluster-autoscaler \
  --namespace kube-system \
  --set autoDiscovery.clusterName=<CLUSTER_NAME> \
  --set awsRegion=<REGION>

1. IAM Role (EKS-specific): Cluster Autoscaler requires a node IAM role with permissions to scale the Auto Scaling Groups.

2. Auto Scaling Groups: CA works with AWS ASGs, so your EKS nodes must belong to ASGs.

3. Pod Annotations (Optional): To prevent certain pods from triggering scale-down.

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How it Works

1. Watches pending pods → adds nodes to the cluster if no existing nodes can fit them.

2. Checks underutilized nodes → removes nodes that are empty or low utilization while respecting PodDisruptionBudgets.

3. Works with ASGs, respecting min/max node limits.

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Result

• Cluster scales up slowly (depends on ASG launch time).

• Scaling down is conservative (to avoid evicting pods abruptly).

• Stable for long-running clusters with predictable workloads.

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Pros

• Mature, widely used in production.

• Multi-cloud support (EKS, GKE, AKS).

• Well-documented, large community.

• Integrates with PodDisruptionBudgets and taints/tolerations.

Cons

• Slower to scale nodes → pending pods may wait longer.

• Limited optimization for instance types and Spot instances.

• Works only at node level, not intelligent cost optimization.

• Scaling policies tied to ASGs, which can be rigid.

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2️⃣ Karpenter

Definition

Karpenter is a next-generation Kubernetes node autoscaler developed by AWS for EKS. It provisions nodes dynamically in real-time based on pod requirements, optimizes costs, and supports heterogeneous workloads including Spot instances and GPU nodes.

• Focuses on fast, event-driven node provisioning.

• Deeply integrated with AWS APIs (EC2, IAM, LaunchTemplates).

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Setup

1. Install Karpenter via Helm / Manifest

kubectl apply -f https://github.com/aws/karpenter/releases/latest/download/karpenter-crds.yaml
kubectl apply -f https://github.com/aws/karpenter/releases/latest/download/karpenter-controller.yaml

1. IAM Role / OIDC:

   • Associate IAM OIDC provider to EKS cluster.

   • Create Karpenter node IAM role with EC2/Spot permissions.

2. Provisioner Configuration

apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
  name: default
spec:
  requirements:
    - key: "node.kubernetes.io/instance-type"
      operator: In
      values: ["t3.medium","t3.large"]
  limits:
    resources:
      cpu: "1000"
  provider:
    subnetSelector:
      karpenter.sh/discovery: my-cluster
    securityGroupSelector:
      karpenter.sh/discovery: my-cluster

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How it Works

1. Watches pending pods and pod requirements (CPU, memory, GPU).

2. Dynamically launches nodes optimized for the workload (Spot/On-Demand mix).

3. Consolidates underutilized nodes (removes empty nodes quickly).

4. Handles heterogeneous workloads — multiple instance types, architectures, and capacity types.

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Result

• Scaling up is much faster than Cluster Autoscaler.

• Cost-efficient → uses Spot instances and instance type flexibility.

• Handles dynamic, bursty workloads gracefully.

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Pros

• Real-time scaling → reduces pending pods latency.

• Cost optimization → heterogeneous node support + Spot instances.

• Flexible → can run GPU, memory-optimized, or burstable workloads in same cluster.

• Event-driven and integrates well with AWS services.

Cons

• AWS-specific → limited multi-cloud support.

• Newer tool → smaller community and less battle-tested outside AWS.

• Some advanced configurations have a learning curve.

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3️⃣ Direct Comparison Table

Feature	Cluster Autoscaler	Karpenter
Definition	Node autoscaler for existing ASGs	Dynamic, event-driven node provisioning
Cloud Support	Multi-cloud (EKS, GKE, AKS)	AWS EKS only
Speed	Slow → ASG launches	Fast → real-time, pod-optimized
Cost Optimization	Limited → ASG instance types fixed	High → Spot, mixed instance types
Workload Types	Standard nodes	Supports GPU, memory-optimized, heterogeneous
Setup Complexity	Moderate	Moderate → IAM + OIDC + Provisioners
Scaling Down	Conservative, respects PDB	Aggressive consolidation of unused nodes
Community / Maturity	Large, well-tested	Growing, newer

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4️⃣ Winner

• For traditional, multi-cloud clusters or legacy workloads: Cluster Autoscaler

• For AWS EKS, dynamic workloads, cost optimization, GPU/heterogeneous workloads: Karpenter ✅

Interview Tip:

• Mention that Karpenter + KEDA can complement each other for event-driven scaling of pods and nodes.

• Show that you understand limitations — Karpenter only watches pending pods, not stuck pods.

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Pavan, I can also make a visual diagram comparing Cluster Autoscaler vs Karpenter workflows — showing scaling up, scaling down, and node optimization. That’s a big plus for interview discussion.

Do you want me to create that diagram?