Karpenter vs Cluster Autoscaler: Which Kubernetes Autoscaler Should You Choose?
Karpenter vs Cluster Autoscaler: Which Kubernetes Autoscaler Should You Choose?
Blog Article
As Kubernetes continues to dominate the container orchestration landscape, efficient resource management has become a top priority for platform teams. Autoscaling—automatically adjusting the size of your compute resources based on demand—is a critical component of that efficiency.
Two popular options for Kubernetes cluster autoscaling are Cluster Autoscaler (CA) and Karpenter. While they both aim to optimize resource usage and cost, they differ significantly in how they achieve that goal.
In this blog, we’ll explore how Karpenter and Cluster Autoscaler work, their pros and cons, and which one might be a better fit for your Kubernetes workloads.
What Is Cluster Autoscaler?
Cluster Autoscaler is the default Kubernetes project for managing node scaling. It automatically adjusts the size of your cluster when:
Pods are pending due to insufficient resources.
Nodes are underutilized and their pods can be moved elsewhere.
It’s tightly integrated with Kubernetes and supported by all major cloud providers like AWS, GCP, and Azure.
???? How It Works
Cluster Autoscaler monitors the Kubernetes scheduler and scales node groups (like EC2 Auto Scaling Groups on AWS) up or down depending on pending pods or resource utilization.
What Is Karpenter?
Karpenter is an open-source autoscaling solution built by AWS. Unlike Cluster Autoscaler, Karpenter doesn't rely on pre-defined node groups. Instead, it dynamically provisions just the right compute resources (e.g., EC2 instances) in response to workload requirements.
Karpenter is designed to be fast, flexible, and cost-efficient—automatically picking instance types and sizes based on actual pod needs.
???? How It Works
When unscheduled pods are detected, Karpenter quickly launches the best-fitting instance based on CPU, memory, architecture, and availability zone preferences. It bypasses node groups entirely and interacts directly with the cloud provider APIs.
When to Use Cluster Autoscaler
You're using a managed Kubernetes service (like EKS, GKE, or AKS).
You already have node groups configured.
Your scaling requirements are moderate and predictable.
You want a mature, battle-tested autoscaler with wide cloud support.
Pros:
Stable and well-documented.
Multi-cloud support.
Minimal configuration for basic use cases.
Cons:
Slower scaling.
Less flexible in instance type selection.
Can over-provision or under-utilize nodes.
When to Use Karpenter
You're on AWS and want dynamic, just-in-time scaling.
Your workloads have diverse or bursty resource needs.
You're looking to optimize cost with Spot Instances and flexible compute.
Pros:
Blazing fast scaling.
No need to manage node groups.
More efficient use of cloud resources.
Cons:
AWS-only (for now).
Newer, with less community support.
Requires more setup and IAM permissions.
Can You Use Both Together?
Yes. In fact, some teams run Cluster Autoscaler for managed node groups and Karpenter for on-demand scaling flexibility. This hybrid model gives you the best of both worlds—stable base capacity with the flexibility of rapid scale-ups.
However, care must be taken to avoid conflicts, such as both autoscalers trying to scale the same workloads or nodes.
Conclusion
Karpenter and Cluster Autoscaler solve the same problem—autoscaling Kubernetes nodes—but with different philosophies.
Cluster Autoscaler is great for teams that prefer predictability, are on non-AWS clouds, or rely on existing node group management.
Karpenter shines for AWS users looking to maximize agility, flexibility, and cost savings.
Choosing between the two (or combining them) ultimately depends on your cloud provider, workload characteristics, and performance goals.
Need help scaling your Kubernetes infrastructure? Whether you're migrating to Karpenter or fine-tuning your current autoscaler, our experts at Kapstan can help you design an optimized, cost-effective scaling strategy tailored to your workloads.
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