The ability to ensure optimal performance while managing costs is only possible when Kubernetes clusters are managed effectively. The most advanced feature that should be available is the ability to automatically and immediately adjust the scaling of clusters with the changes in workload demands. This is where tools for Kubernetes autoscaling come into play. A popular topic of discussion is Karpenter versus Cluster Autoscaler. Which one would be better suited for your environment ?

In this post, the battle of Karpenter vs Cluster Autoscaler is explored with the goal of providing guidance on which solution is ideal for your use case based on their features, advantages, and constraints.

What Is Cluster Autoscaler ?

The widely sought feature is that every component within a Kubernetes ecosystem has the capacity to autoscale by itself. This has been made possible by the Cluster Autoscaler – the most traditional and popular SaaS solution crafted by the SIG Autoscaling community. To give a guided description, Cluster Autoscaler automatically resizes a Kubernetes cluster. Trim unused nodes ? Yeah. Make sure all allocated pods are placed ? Done.

The method that Game Theory uses is attributed to monitoring quite a few nodes (with resources bound) pods, then dynamically scaling the node count by the set node group limit. It also has the ability to prune nodes that are severely idling and lacking vital workloads.

Key Features of Cluster Autoscaler :

Deployment is straightforward.

Dynamically sizes clusters to maintain pod availability.

Works with all major cloud cost providers—AWS, GCP & Azure.

Supports predefined instance types.

Established and adopted as production-grade software.

Limitations :

Scaling capabilities can sometimes lack in efficiency and responsiveness.

Needs predefined node clusters.

Minimal flexibility on node class selection during scaling up.

What is Karpenter ?

Karpenter is a relatively new open source project by AWS, whose objective is to bring modern and elastic scaling strategies to Kubernetes. Unlike Cluster Autoscaler, Karpenter does not work with pre-defined node groups. It provisions compute resources based on actual pod requirements in realtime.

Key features of Karpenter :

Provisioning of nodes is limited to only what is necessary for the workload.

Automatically adjusts the number of active nodes to below market value.

Supports a broad array of types of instances automatically.

Rapid removal of underutilized nodes.

The design is agnostic to cloud providers (AWS is the most developed).

Known issues :

Compared to Cluster Autoscaler, it is newer.

Best performance currently seen on AWS.

Some features still under development or not fully available outside AWS.

When to choose Cluster Autoscaler

If you’re using a multi-cloud platform or a different provider than AWS, Cluster Autoscaler remains a rock-solid choice. It integrates seamlessly with nearly every major cloud provider and is proven in production environments across the board.

Choose Cluster Autoscaler if :

The node groups are already put in place.

You require a fully mature solution.

Usually operate on a non-AWS cloud.

You would want complete control of instance types and access to node groups.

When Karpenter Works Best

Karpenter does best in fast-paced, high-scale situations. If your workloads demand real-time provisioning and you don’t want to manage node groups, Karpenter is the tool for you if you are on AWS.

Use Karpenter if :

You primarily operate on AWS.

You don’t want to manage node groups.

You require faster performance for scale-up.

You are developing a dynamic infrastructure that experiences changes in workload.

Is it possible to use both ?

Yes, it is possible to use both technically, but it is not ideal to run Karpenter and Cluster Autoscaler on the same cluster unless they are segregated by node pools and workloads. Having both will lead to conflicting decisions regarding the scaling of resources, resulting in unpredictable cluster behavior.

As a best practice, first choose a tool that aligns with your cluster strategy and then use it to ensure reliability and consistency.

Karpenter versus Cluster Autoscaler : Closing Comments

Your particular use case, environment, and preferred level of automation determine whether you will go for Karpenter or Cluster Autoscaler. Karpter may be best if you are heavily invested in AWS, but if you want proven stability and support for most cloud services, Cluster Autoscaler remains the best option. In contrast, Karpenter offers modern and flexible auto-scaling features.

There is no wrong choice between the two tools as they are both open source and kept up to date by vibrant communities. It is only a matter of contradicting them based on your situation. As Karpenter develops, it may be adopted beyond AWS and it could take on Kubernetes autoscaling features in the future.

Keeping Karpenter Autoscaler and Cluster Autoscaler guides in mind while Kubernetes autoscaling maintains a good balance in cost, performance, and simplicity of operations is a good idea for future development.