When it comes to resource management and autoscaling in Kubernetes, users have two main options: Cluster Autoscaler or Karpenter. Cluster Autoscaler is one of the most popular options, whereas Karpenter offers exciting new features and capabilities.
This blog will explore the primary differences between Karpenter and Cluster Autoscaler — as well as Compute Copilot, a third option for optimizing your cloud costs.
What Are the Primary Differences Between Cluster Autoscaler, Karpenter and Compute Copilot?
|Based on the resource utilization of existing nodes, Cluster Autoscaler takes a reactive approach to scale nodes.
|Based on the current resource requirements of unscheduled pods, Karpenter takes a proactive approach to provisioning nodes.
|Uses Karpenter to handle what it does best while providing extra signals to Karpenter to supercharge its capabilities.
|Cluster Autoscaler manages nodes based on the resource demands of the present workload, using predefined autoscaling groups.
|Karpenter scales, provisions, and manages nodes based on the configuration of custom Provisioners.
|Manages your Karpenter Provisioner configuration for you to optimize availability and ensure you use the lowest-cost compute.
|Cluster Autoscaler is more focused on node-level scaling, which means it can effectively add more nodes to meet any increase in demand. But this also means it may be less effective in downscaling resources.
|Karpenter offers more effective and granular scaling functionalities based on specific workload requirements. In other words, it scales according to the actual usage. It also allows users to specify particular scaling policies or rules to match their requirements.
|Scaling with Copilot adds RI, Savings plan, and Organization awareness to Karpenter’s already advanced scaling capabilities. In addition, continuous reconsideration means that your nodes may be descheduled and your workloads can be moved to the more cost-optimal resources.
|With Cluster Autoscaler, scheduling is more simple as it is designed to scale up or down based on the present requirements of the workload.
|Karpenter can effectively schedule workloads based on different factors like availability zones and resource requirements. It can try to optimize for the cheapest pricing via spot but is unaware of any commitments like RI’s or Savings Plans.
|It goes beyond Karpenter’s single cluster view to watch your entire AWS ecosystem to automatically schedule EKS resources and maximize savings via RI, Savings Plans, and Spot.
Cluster Autoscaler Vs. Karpenter Vs. Compute Copilot (nKS)
Besides the above-mentioned aspects of the three platforms, they differ in the availability and unavailability of certain features. So, here’s a comprehensive list of haves and have-nots for Karpenter Vs. Cluster Autoscaler Vs. Compute Copilot (nKS).
nOps Compute Copilot constantly manages the scheduling and scaling of your EKS workloads for the best price and stability.
Whether you’re currently using Karpenter or not, Copilot optimizes your RI, SP and Spot automatically for 50%+ savings. It analyzes your organizational usage and market pricing to ensure you’re always on the best options, for zero engineering effort. Think On Demand reliability, with Spot pricing.