Learn to scale workloads efficiently, reduce cloud costs, and optimize performance with real-world strategies for event-driven and infrastructure-level scaling

Key Features

• Autoscale Kubernetes workloads and infrastructure using KEDA and Karpenter
• Improve performance, reduce cloud costs, and eliminate resource waste with smarter scaling
• Work with hands-on labs, real-world use cases, and step-by-step guidance from the creator of Karpenter Blueprints

Book Description

Kubernetes is the backbone of modern containerized infrastructure, but scaling it efficiently remains a challenge. Kubernetes Autoscaling equips cloud professionals with this comprehensive guide to dynamically scaling applications and infrastructure using the powerful combination of Kubernetes Event-Driven Autoscaler (KEDA) and Karpenter, AWS’s next-generation cluster autoscaler.

You’ll begin with autoscaling fundamentals, move through HPA and VPA, and then get hands-on KEDA for event-driven workloads and Karpenter for data plane scaling. With the help of real-world use cases, best practices, and detailed patterns, you’ll deploy resilient, scalable, and cost-effective Kubernetes clusters across production environments.

By the end of this book, you’ll be able to implement practical autoscaling strategies to improve performance, reduce cloud costs, and eliminate over-provisioning.

What you Will Learn

• Gain a solid foundation in Kubernetes autoscaling and its components
• Scale deployments, jobs, and StatefulSets using KEDA's CRDs
• Configure event-based scaling strategies using metrics and schedules
• Deploy and manage Karpenter for on-demand infrastructure provisioning
• Explore advanced node disruption and lifecycle techniques
• Combine KEDA and Karpenter to implement full-stack autoscaling
• Optimize costs using Spot Instances, scale-to-zero, and workload placement
• Apply real-world patterns and monitor autoscaling performance in production

Who this Book is for

This book is ideal for DevOps engineers, SREs, cloud architects, and Kubernetes professionals who want to optimize resource usage and improve scalability. A basic understanding of Kubernetes concepts and cloud environments, i.e., AWS, GCP, and Azure, is assumed.

Table of Contents

Part 1: Getting started with Kubernetes Autoscaling

Chapter 1: Introduction to Kubernetes Autoscaling

Chapter 2: Workload Autoscaling Overview

Chapter 3: Workload Autoscaling with HPA and VPA

Part 2: Workload Autoscaling and KEDA

Chapter 4: Kubernetes Event-Driven Autoscaling - Part 1

Chapter 5: Kubernetes Event-Driven Autoscaling - Part 2

Chapter 6: Workload Autoscaling Operations

Part 3: Node Autoscaling and Karpenter

Chapter 7: Data Plane Autoscaling Overview

Chapter 8: Node Autoscaling with Karpenter - Part 1

Chapter 9: Node Autoscaling with Karpenter - Part 2

Chapter 10: Karpenter Management Operations

Part 4: Use Cases, Patterns, and Recommendations

Chapter 11: Practical Use Cases for Autoscaling in Kubernetes

Chapter 12: Patterns and Recommendations

Chapter 13: Unlock Your Exclusive Benefits

About the Author

Christian Melendez is Principal Specialist Solutions Architect and EMEA Lead for Compute at AWS, with a strong background in Kubernetes platform engineering. He has been working with Kubernetes since 2017, helping large enterprises—including telecommunications, airline, and ride-hailing companies—optimize their workloads. Christian is the creator of the Karpenter Blueprints project and an active contributor to autoscaling solutions in the cloud-native space. He frequently delivers talks and workshops on Karpenter and Kubernetes optimization strategies.