Karpenter has emerged as the best EKS autoscaling technology currently available, and with Karpenter 1.0 General Availability (GA) recently announced, it’s receiving a lot of well-deserved attention.

This comprehensive guide covers everything you need to know about Karpenter and how to cost optimize with it: why adopt Karpenter, how Karpenter compares to Cluster Autoscaler, how to migrate to Karpenter, and best practices and tips for getting the most out of Karpenter.

What is Karpenter?

Karpenter is an open-source, flexible, and high-performance Kubernetes cluster autoscaler, offering advanced scheduling and scaling capabilities. Karpenter can improve application availability and cost efficiency by rapidly launching right-sized compute resources in response to changing application load.
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Why Karpenter?

Karpenter automates and simplifies node management while enhancing pod placement for optimal resource utilization. It proactively and dynamically provisions nodes in anticipation of actual workload needs, helping you avoid overprovisioning, waste, and unnecessary costs.

But Karpenter has become more than just an autoscaler. Teams are embracing Karpenter as an elegant and efficient way to manage their workloads, because it provides a high level of control over scheduling decisions and resource allocation. This unlocks possibilities across new dimensions like EC2 Spot utilization, which natively integates with Karpenter. To understand all the benefits, check out the full ebook guide.

Related resource(s) on the benefits of Karpenter:

Karpenter General Availability

With the recent release of Karpenter 1.0 General Availability (GA), the technology has reached a level of maturity that many organizations have been waiting for, even as others have already been running it successfully in production.

Karpenter 1.0 comes with a host of benefits including increased stability, new features like disruption budgets and node consolidation, security enhancements and more. Read about them here:

Related resource(s) on Karpenter recent releases:

How Karpenter Works & How to Get Started

Karpenter simplifies and improves provisioning in Kubernetes clusters. With Karpenter, it becomes possible to combine all the resource requests for unscheduled pods and automate decision-making for launching new nodes or terminating the existing nodes. This, in turn, leads to significantly decreased infrastructure costs and 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.

A diagram illustrating how Karpenter works. Pending Kubernetes pods are checked against existing capacity. If there are insufficient resources, Karpenter is triggered to provision just-in-time capacity, allowing unschedulable pods to be scheduled.

Migrating to Karpenter via EKS managed node groups or Fargate is straightforward thanks to its compatibility with existing Kubernetes clusters and its ability to leverage standard Kubernetes resources.

It takes 20 minutes on average and involves minimal disruption to your existing operations. Full instructions and code are linked below.

Related resource(s) on how Karpenter works & how to get started:

Need help with your Karpenter migration? Find out how nOps can help make your transition to Karpenter easy.

Cluster Autoscaler versus Karpenter

Cluster Autoscaler was revolutionary when introduced in 2017, and it’s still the most popular solution for Kubernetes scaling. However, it has limitations compared to Karpenter, including latency in scaling, complex configurations, dependency on metrics, and more. Particularly for today’s highly dynamic workloads, it can be complex and time-consuming to use compared to Karpenter, as well as limited in features.

Karpenter is a major advancement over Cluster Autoscaler — for detailed information on how they compare in terms of resource management, node management, scaling, schedule and more, check out the complete guide.

Related resource(s) on Karpenter versus Cluster Autoscaler

Karpenter Cost Optimization Strategies & Best Practices

There are two basic pillars to your cost optimization strategy: #1, optimize for price, and #2, optimize for efficiency.

Optimizing for price

This strategy involves ensuring you’re getting the most discounts on your resources.

AWS offers four models:

  1. On-Demand: The simplest option, where users pay only for what they use without long-term commitments. It’s the easiest to use, but also the costliest. You don’t receive any discounts on On-Demand spend.
  2. Reserved Instances: If you have long-running predictable workloads, Reserved Instance commitments offer discounts in exchange for 1 or 3-year commitments. You can pay fully or partially upfront for additional savings. If you are not an EDP customer, you can use a third-party platform to buy and sell RIs for you, so you have the flexibility of On-Demand with RI discounts.
  3. Savings Plans: If you are an EDP customer, Savings Plans offer more flexibility than Reserved Instances. They offer similar savings with more usage type flexibility to accommodate your dynamic environment. Savings Plan discounts are applied automatically across all components, potentially reducing time and overhead spent on resource management.
  4. Spot Instances: Spot Instances are available at a discount of up to 90%, but the catch is that AWS can reclaim these instances with just two minutes’ notice. While long-term commitments like RI and SP aren’t good for covering spiky workloads, Spot can be used very effectively to get discounts when your usage spikes.

It’s extremely complex to manually balance your dynamically scaling workloads between Reserved Instances, Savings Plans, and Spot.

However, AI can continuously monitor your workloads and automatically balance these price optimizations in your clusters in real time. To find out how you can effortlessly get maximum discounts on all of your compute with a 100% utilization guarantee, book a demo with one of our FinOps experts.

A line graph comparing the cost of cloud computing with and without nOps. The x-axis represents time, and the y-axis represents cost. The blue line shows the cost without nOps, which fluctuates significantly. The green line shows the cost with nOps, which is consistently lower and more stable. The graph highlights the savings potential of using nOps for cloud computing.

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Optimizing for waste and efficiency

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To reduce costs, you can pay less for what you use (see above), or you can use less.

Karpenter helps us to choose and size nodes efficiently, scale more granularly and efficiently, and take action to optimize resource efficiency.

However, Karpenter’s efficiency heavily depends on how well your Node Pools are configured for optimal workload packing. Misconfigured node pools or improper rules can lead to inefficient node utilization, causing increased costs and availability issues. If nodes are not packed efficiently, Karpenter may not achieve its full potential in costs and performance.

By having nOps constantly tuning and monitoring your Karpenter settings, you ensure that node pools are optimized for workload packing, leading to highly efficient operations.

Here at nOps, we’ve helped many teams to get the most out of Karpenter. For proven best practices for saving with Karpenter, check out the following resources.

Karpenter + nOps is even better

nOps Compute Copilot is an intelligent workload provisioner that continuously manages, scales, and optimizes all of your AWS compute to get you the lowest cost with maximum stability.

The benefits of having nOps manage your Karpenter include:

  • EKS Observability & Management
  • Karpenter Deployment, Management & Continuous Optimization
  • Shift Dynamic Usage to Spot with High Reliability
  • Container Rightsizing
  • Cluster Node Efficiency
  • Automated Commitment Management
  • 100% Utilization of Commitments
  • Intelligent Instance Selection Based on Workload Requirements
  • Graceful Pod Rebalancing
  • Real-Time Workload Reconsideration

Book a demo to find out how nOps + Karpenter can make running on Spot better and easier, freeing your engineering team to focus on building and innovating.

nOps was recently ranked #1 with five stars in G2’s cloud cost management category, and we optimize $1.5+ billion in cloud spend for our customers.