Why I Decided to Use Karpenter for Kubernetes Autoscaling

Kubernetes has become the de facto standard for container orchestration, offering unmatched scalability, flexibility, and efficiency. However, managing node autoscaling in Kubernetes has always been a challenge. Traditional Kubernetes Cluster Autoscaler (CA) works well in many cases but comes with limitations in speed, efficiency, and cost optimization.

As I worked on optimizing Kubernetes workloads for production environments, I needed a better, faster, and more cost-efficient autoscaling solution. That’s when I discovered Karpenter—an open-source, high-performance node provisioning tool for Kubernetes. In this blog, I’ll share why I decided to use Karpenter, how it differs from traditional autoscaling solutions, and the benefits it brings to Kubernetes infrastructure.

Understanding Kubernetes Autoscaling

Before diving into Karpenter, let’s briefly discuss autoscaling in Kubernetes. There are three main types of autoscaling in a Kubernetes cluster:

1. Horizontal Pod Autoscaler (HPA) – Scales the number of pods based on CPU/memory usage.
2. Vertical Pod Autoscaler (VPA) – Adjusts the CPU and memory limits of individual pods.
3. Cluster Autoscaler (CA) – Scales nodes based on pending pod demands.

While HPA and VPA focus on pod-level scaling, Cluster Autoscaler (CA) manages node-level scaling. The Cluster Autoscaler works by adding or removing nodes from the cluster based on pod scheduling requirements. However, it has several drawbacks that led me to consider Karpenter.

Challenges with Traditional Kubernetes Cluster Autoscaler

While the Cluster Autoscaler is widely used, it has some limitations:

❌ Slow Node Provisioning

• The Cluster Autoscaler relies on cloud provider autoscaling groups, which can take minutes to provision new nodes. This delay can lead to service disruptions when workloads suddenly spike.

❌ Fixed Instance Types

• CA pre-defines instance types in the autoscaling group, limiting flexibility. If your workload requires a specific instance type, you must update the autoscaling group manually.

❌ Inefficient Resource Allocation

• It scales nodes based on predefined rules, which may lead to over-provisioning (wasting resources) or under-provisioning (causing performance issues).

❌ Lack of Spot Instance Support

• CA does not natively optimize for spot instances, making cost savings difficult for workloads that can tolerate interruptions.

These challenges led me to explore Karpenter, a Kubernetes-native autoscaler that overcomes many of these limitations.

What is Karpenter?

Karpenter is an open-source high-performance autoscaler that provisions nodes on-demand to meet application needs dynamically. Unlike the Cluster Autoscaler, which works with autoscaling groups, Karpenter directly communicates with the cloud provider API to provision nodes.

It offers faster, more flexible, and cost-efficient scaling for Kubernetes workloads. Karpenter was developed by AWS but is cloud-agnostic and can work with other cloud providers as well.

Why I Chose Karpenter Over Cluster Autoscaler

After evaluating Karpenter for my Kubernetes infrastructure, I found several key advantages:

✅ 1. Faster Node Provisioning

• Unlike CA, which depends on autoscaling groups, Karpenter directly requests compute resources from the cloud provider API.
• Nodes are provisioned within seconds instead of minutes, reducing the risk of pod scheduling delays.

✅ 2. Intelligent Resource Allocation

• Karpenter selects the most efficient instance type based on workload requirements instead of using pre-defined autoscaling groups.
• It ensures better resource utilization, reducing the risk of over-provisioning or under-provisioning.

✅ 3. Native Spot Instance Support

• One of the biggest reasons I switched to Karpenter is its native support for Spot Instances.
• It intelligently provisions a mix of On-Demand and Spot Instances, optimizing cost without compromising reliability.

✅ 4. Works with Any Cloud Provider

• While Karpenter was initially designed for AWS, it’s cloud-agnostic and supports other cloud providers like GCP and Azure.
• This makes it a great choice for multi-cloud Kubernetes clusters.

✅ 5. Automated Node Cleanup

• Karpenter automatically deprovisions underutilized nodes based on workload demand.
• This helps reduce unnecessary costs and keeps the cluster efficient.

✅ 6. Simplified Configuration

• Unlike Cluster Autoscaler, which requires node groups and scaling policies, Karpenter only needs a simple provisioner YAML file to define scaling behavior.

How I Implemented Karpenter

Integrating Karpenter into my AWS EKS cluster was straightforward. Here’s a high-level overview of the setup:

1. Install Karpenter

helm repo add karpenter https://charts.karpenter.sh/
helm repo update
helm install karpenter karpenter/karpenter --namespace karpenter --create-namespace

2. Create a Karpenter Provisioner

apiVersion: karpenter.k8s.aws/v1alpha5
kind: Provisioner
metadata:
  name: default
spec:
  provider:
    instanceProfile: "KarpenterNodeInstanceProfile"
  limits:
    resources:
      cpu: "1000"
  ttlSecondsAfterEmpty: 30
  requirements:
    - key: "node.kubernetes.io/instance-type"
      operator: In
      values: ["t3.medium", "m5.large", "c5.large"]

• This configuration allows Karpenter to provision different instance types dynamically based on demand.
• The ttlSecondsAfterEmpty ensures that underutilized nodes are removed after 30 seconds, preventing waste.

3. Test Autoscaling

• I deployed a sample workload and observed how Karpenter automatically provisioned the best-fit instance in seconds.
• I also ran spot instance workloads and saw significant cost savings compared to using only on-demand nodes.

Final Thoughts – Is Karpenter Worth It?

After using Karpenter in production, I can confidently say that it outperforms the traditional Cluster Autoscaler in terms of:
✅ Speed – New nodes spin up within seconds, preventing pod scheduling delays.
✅ Efficiency – Nodes are provisioned based on actual workload needs, reducing wasted resources.
✅ Cost Savings – Spot instance optimization leads to lower cloud bills.
✅ Simplicity – No more managing complex autoscaling groups or node pools.

If you’re running Kubernetes clusters in the cloud and want a smarter, faster, and more cost-effective autoscaling solution, Karpenter is a game-changer.

Should You Use Karpenter?

If you:
✅ Run cloud-based Kubernetes clusters (AWS, Azure, GCP)
✅ Need fast and efficient autoscaling
✅ Want to reduce cloud costs with Spot Instances
✅ Prefer simplified autoscaler configurations

Then YES! Karpenter is absolutely worth trying.

I’d love to hear your thoughts! Have you used Karpenter in your Kubernetes clusters? Let’s discuss in the comments!

🔹 #Kubernetes #DevOps #Karpenter #CloudNative #AWS #EKS #Autoscaling