# Karpenter — Just-in-Time Kubernetes Node Autoscaler for AWS

> AWS-origin cluster autoscaler that launches the right EC2 instance shape and size for pending pods in ~30 seconds.

## Install

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# Karpenter — Just-in-Time Kubernetes Node Autoscaler for AWS

## Quick Use
```bash
# Install on an existing EKS cluster (IRSA required)
helm upgrade --install karpenter oci://public.ecr.aws/karpenter/karpenter 
  --namespace kube-system --version 1.0.6 
  --set "settings.clusterName=${CLUSTER}" 
  --set "serviceAccount.annotations.eks\.amazonaws\.com/role-arn=${ROLE_ARN}"

# Minimal NodePool + EC2NodeClass
kubectl apply -f - <<EOF
apiVersion: karpenter.sh/v1
kind: NodePool
metadata: {name: default}
spec:
  template:
    spec:
      requirements:
        - {key: karpenter.k8s.aws/instance-category, operator: In, values: [c, m, r]}
        - {key: karpenter.sh/capacity-type, operator: In, values: [on-demand, spot]}
      nodeClassRef: {group: karpenter.k8s.aws, kind: EC2NodeClass, name: default}
  disruption:
    consolidationPolicy: WhenEmptyOrUnderutilized
EOF
```

## Introduction
Karpenter is an AWS-originated, now CNCF-hosted, open-source Kubernetes node autoscaler that watches pending pods and launches the right-size EC2 instance in ~30 seconds — an order of magnitude faster than Cluster Autoscaler + ASGs. It also continuously right-sizes and consolidates nodes, cutting compute spend by 30-60% for many workloads.

## What Karpenter Does
- Watches `Pending` pods and computes the cheapest instance type that fits
- Launches the node directly via EC2 `RunInstances` (no ASG round trip)
- Binds pending pods to the new node as soon as kubelet registers
- Continuously consolidates underutilized nodes by moving pods and terminating idle hosts
- Supports Spot, On-Demand, Graviton, GPU, and Bottlerocket AMIs

## Architecture Overview
Karpenter runs as a pair of Deployments in `kube-system`: the controller watches pod events and reconciles `NodePool` + `NodeClass` CRDs, and the webhook validates them. It calls EC2 APIs via IRSA (IAM Roles for Service Accounts) — so no long-lived credentials. Since v1.0 it supports the Cluster API interface, and community providers for Azure and GCP exist.

## Self-Hosting & Configuration
- Requires EKS or a Kubernetes cluster with IRSA / Pod Identity
- IAM role needs EC2 RunInstances, TerminateInstances, DescribeSubnets, etc.
- `NodePool` defines workload requirements; `EC2NodeClass` defines AMI + userdata
- Tune with `limits.cpu`, `disruption.budgets`, `consolidationPolicy`
- Export metrics to Prometheus; dashboards ship on grafana.com

## Key Features
- ~30 second scale-up latency (vs 2-5 min for Cluster Autoscaler)
- Picks the optimal instance type per batch of pending pods
- Consolidates under-utilized nodes automatically
- Native Spot interruption handling with graceful draining
- No Node Group / ASG sprawl — one `NodePool` covers dozens of instance shapes

## Comparison with Similar Tools
- **Cluster Autoscaler** — the classic; scales ASG node groups, slower, simpler
- **KEDA** — scales workloads, not nodes; often paired with Karpenter
- **Azure AKS Karpenter Provider** — Karpenter for Azure (community, graduating)
- **GKE Autopilot** — managed equivalent; hides nodes entirely, GCP-only
- **Fargate** — serverless pods, no node concept; simpler but pricier for steady load

## FAQ
**Q:** Does Karpenter replace Cluster Autoscaler?
A: Yes on EKS. Uninstall CA and let Karpenter manage all elastic capacity; keep static NG for system pods.

**Q:** Spot interruption handling?
A: Karpenter subscribes to EventBridge rebalance/interruption notifications and pre-drains nodes.

**Q:** Can it run on non-AWS clusters?
A: Core is cloud-agnostic; community `AKSNodeClass` works on Azure AKS; GCP provider is in progress.

**Q:** How does consolidation decide which nodes to kill?
A: It simulates rescheduling pods to cheaper / fewer nodes and executes when the delta is positive.

## Sources
- https://github.com/kubernetes-sigs/karpenter
- https://karpenter.sh

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Source: https://tokrepo.com/en/workflows/de139dca-38ef-11f1-9bc6-00163e2b0d79
Author: AI Open Source