Karpenter

Karpenter

Deploy Karpenter instantly from the marketplace for fast, automated Kubernetes autoscaling with zero hassle

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Karpenter is an open-source, next-generation Kubernetes cluster autoscaler built to intelligently manage infrastructure in real time. Unlike the traditional Kubernetes Cluster Autoscaler, which relies on predefined scaling rules, Karpenter makes dynamic scheduling decisions based on actual pod requirements — ensuring optimal performance and cost-efficiency.

How Karpenter Works

Pod-driven scaling – Instead of scaling based on static metrics, Karpenter looks at unschedulable pods and provisions nodes that precisely match their CPU, memory, GPU, or custom resource needs.

Intelligent instance selection – Evaluates available instance types, families, zones, and capacity pools to launch the best-fit nodes in seconds.

Bin-packing optimization – Efficiently packs pods onto nodes to reduce fragmentation and maximize utilization.

Consolidation – Continuously monitors workloads and can terminate underutilized nodes, shifting workloads to fewer machines to reduce costs.

Technical Benefits

  • Ultra-fast provisioning – Nodes come online in seconds vs. minutes with legacy autoscalers.

  • Flexibility across workloads – Works for stateless services, ML/AI training jobs, batch processing, and high-throughput systems.

  • Customizable scheduling – Developers can define requirements with constraints (e.g., zone affinity, GPU requirements, instance families).

  • Native Kubernetes integration – Runs as a controller inside your cluster, using CRDs (Custom Resource Definitions) like Provisioner and NodePool for fine-grained control.

Why Developers Love Karpenter

  • Less manual tuning – No need to constantly adjust ASG settings or predefined scaling rules.

  • Cost-efficient scaling – Automatically chooses Spot, On-Demand, or mixed pools based on constraints.

  • Improved pod scheduling success – Reduces pending pods and ensures SLA adherence.

  • Observability-ready – Works seamlessly with Prometheus, Grafana, and logging systems for monitoring autoscaling events.

With Westack + Karpenter, your team gets the power of automation with the confidence of efficiency.

FAQ

  • Karpenter is an open-source, flexible Kubernetes node autoscaler that focuses on pod-driven, real-time infrastructure management. Unlike the traditional Cluster Autoscaler—which scales clusters based on predefined node groups and resource thresholds—Karpenter provisions and terminates nodes directly based on actual pending pod requirements, delivering the best-fit compute and cost-efficient scaling without the need for constant manual tuning.

  • Karpenter evaluates unschedulable pods and instantly provisions nodes that meet their CPU, memory, GPU, and custom resource needs. It intelligently chooses instance types, families, and zones, supporting Spot and On-Demand instances, bin-packing multiple pods per node for maximum utilization, and consolidating workloads onto fewer machines to reduce fragmentation and cut costs.

  • Karpenter is workload-agnostic, excelling with stateless services, ML/AI jobs, bursty and high-throughput systems, batch processing, and more. While initially focused on AWS (EKS), it is rapidly gaining multi-cloud and hybrid support, working with any Kubernetes deployment, including on-premises and edge use cases.

  • Karpenter dramatically reduces the need for manual Auto Scaling Group (ASG) adjustments, makes scaling nearly instant (seconds, not minutes), improves pod scheduling success (fewer pending pods), and works natively with Kubernetes APIs (Provisioner, NodePool CRDs) for fine-grained scheduling and constraints (zone, GPU, instance type, etc.). Observability is streamlined with native integration for Prometheus, Grafana, and standard logging tools.

  • Karpenter improves resource utilization in Kubernetes by dynamically provisioning nodes that precisely match the resource needs of unschedulable pods. It optimizes node usage through intelligent instance selection, bin-packing pods efficiently on nodes to reduce fragmentation, and continuously consolidates workloads onto fewer nodes to lower costs. This real-time, pod-driven approach enables faster scaling and ensures compute resources are used effectively, avoiding waste and minimizing cloud expenses.