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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, and the advantage and disadvantages of each.
What is Cluster Auto Scaler and how does it work?
Cluster Autoscaler (CAS) automatically scales your Kubernetes clusters based on the custom metrics you define. It monitors the resource utilization of nodes in your Kubernetes/EKS Cluster, and scales up or scales down the number of nodes in a node group accordingly.
Cluster Autoscaler evaluates both node usage and factors like pod priority and PodDisruptionBudgets to determine whether to add nodes.
What is Karpenter and how does it work?
Karpenter is an open source, flexible, high-performance EKS cluster autoscaler.
This advanced node scheduling and scaling technology enhances pod placement for optimal resource utilization, proactively and dynamically provisioning nodes in anticipation of actual workload needs.
Karpenter addresses pre-defined node group constraints, enabling more fine-grained control over resource utilization. It employs application-aware scheduling, considering pod-specific requirements like taints, tolerations, and node affinity.
Karpenter is a valuable tool for optimizing resource utilization and reduce cloud costs in many ways, including:
Automated scaling
Rightsizing instances
Instance lifecycle management
What’s better: Karpenter or Cluster Autoscaler
Karpenter, released in 2021, is a significant advancement over Cluster Autoscaler.
It streamlines the process of scaling resources present in a Kubernetes cluster by automating the management of different On-Demand and Spot Instances and resources.
Before the introduction of Karpenter, Kubernetes users had to adjust cluster computing capacity, either through Kubernetes Cluster Autoscaler (CAS) or Amazon EC2 Auto Scaling Groups to prevent wasted node capacity. But Cluster Autoscaler doesn’t make scaling decisions, it simply spins up a new node of a predetermined size if the CPU and memory of the pending pod will not fit on the current nodes. This means if the nodes are oversized, a lot of money can be wasted from underutilized infrastructure — or if too small of a node is chosen, costs increase due to overhead.
Cluster Autoscaler isn’t just more complex and time-consuming but also limited in features.
With the help of Karpenter, it becomes possible to combine all the resource requests for unscheduled pods and automate decision-making for provisioning nodes and terminating existing nodes. This, in turn, leads to decreased infrastructure costs scheduling latencies.
Karpenter also has a high-level API that allows users to manage the low-level details of scaling resources according to the current demand. The most significant benefit of Karpenter is that it can ensure that applications always have the resources they might need to handle traffic peaks, all the while decreasing costs using a strategy of launching right-size nodes and culling unused resources.
The limitations of Cluster Autoscaler
Cluster Autoscaler may work for some environments, but it has some limitations. These include:
Suboptimal Resource utilization in existing nodes
In case there is not enough load generated in the Kubernetes workload, the Cluster Autoscaler may not even get triggered to add new nodes to the cluster – which can in turn lead to underutilization of cluster node resources. In most situations, all the memory of the nodes and CPU cores are also not used to their full capacity, which also causes underutilization.
Manual creation of node groups required:
When deployments need specific CPU architecture or different CPU-based instance types, new node groups have to be manually created when deployed.
Timeout-based error handling:
In case an error occurs when creating nodes in managed node groups, Cluster Autoscaler would not find out about it until the timeout. This can cause an invariable delay in scaling out.
For more information on how and why to deploy Karpenter, check out our ebook. It includes best practices for leveraging Karpenter, step-by-step guides, and advanced techniques for optimizing costs and improving resource allocation.
Complex Configurations:
While Cluster Autoscaler offers a range of configuration options to fine-tune its behavior, setting it up correctly can be complex and time-consuming. Misconfigurations can lead to inefficient scaling decisions.
Interference with Other Autoscaling Mechanisms:
If Horizontal Pod Autoscaler (HPA) or Vertical Pod Autoscaler (VPA) is also in use, there needs to be careful coordination to ensure that the Cluster Autoscaler’s node scaling actions do not conflict with pod-level scaling decisions, which can complicate the overall autoscaling strategy.
What Are the Primary Differences Between Cluster Autoscaler, Karpenter and Compute Copilot?
Let’s recap the differences between Cluster Autoscaler and Karpenter and add a third option into the mix: Compute Copilot. Compute Copilot helps you reduce your EKS and ASG costs by automatically optimizing your environment for Spot, RI, and savings plans.
| Feature | Cluster Autoscaler | Karpenter | Compute Copilot |
| Resource Management | 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 provision nodes. | Uses Karpenter to handle what it does best while providing extra signals to Karpenter to supercharge its capabilities. |
| Node management | 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. |
| Scaling | Cluster Autoscaler focuses 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. |
| Scheduling | 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)
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.
nOps is entrusted with over one billion dollars of cloud spend. Join our satisfied customers who recently named us #1 in G2’s cloud cost management category by booking a demo today.
