Intermediate-Advanced|20 hours|35 lessons

Kubernetes Performance Optimization

A deep, scenario-driven course on making Kubernetes clusters faster, leaner, and properly tuned. Covers control plane tuning, workload optimization, network and storage performance, autoscaling, and cloud-managed cluster optimization. Every lesson starts with a real performance problem, diagnoses the root cause, and implements the fix with measurable results. Built for DevOps engineers, SREs, and platform engineers who need to squeeze every last bit of performance out of their clusters and ace the interview question 'your cluster is slow, what do you do?'

Text-based, no videos

7 modules, 35 lessons

Lifetime access

What you'll learn

The full diagnostic toolkit for 'the cluster is slow': measure, identify the bottleneck, fix with confidence

Control plane tuning at scale: API server, etcd, scheduler, and controller manager

Resource right-sizing for hundreds of deployments without manual profiling

CPU throttling, memory limits, and the cgroup mechanics that drive container performance

Network performance from CNI choice through DNS to cross-zone traffic costs

Storage performance for stateful workloads, including local NVMe and dedicated etcd disks

Autoscaling that actually works: HPA, KEDA, Cluster Autoscaler, Karpenter, and capacity planning for peak events

Cloud-managed cluster optimization: EKS, GKE, AKS performance trade-offs and provider-specific tuning

Curriculum

7 modules · 35 lessons

Performance Foundations: Measuring Before Optimizing

How to measure cluster performance, the metrics that actually matter, and the resource-tuning fundamentals every later module builds on.

5 lessons

The Performance Mindset: Measure, Don't Guess30 minFREE Kubernetes Metrics Deep Dive35 minFREE Resource Requests and Limits: The Foundation of Everything40 minFREE Right-Sizing Workloads with VPA and Goldilocks35 minFREE CPU Throttling: The Silent Performance Killer40 minFREE

Control Plane Performance: The Cluster's Brain

API server, etcd, scheduler, controller manager: tuning each for clusters from 100 to 5000 nodes.

5 lessons

API Server Performance Tuning40 min etcd Performance: The Speed of State45 min Scheduler Performance: How Fast Can Pods Start?35 min Controller Manager and Reconciliation Performance35 min Self-Managed Control Plane Sizing40 min

Node and Workload Performance

Node sizing, pod startup, kernel tuning, CPU pinning, and runtime-specific quirks for Java, Python, Node, and Go.

5 lessons

Node Sizing Strategy: Fewer Big Nodes vs Many Small Nodes35 min Pod Startup Optimization: From Scheduled to Running40 min Kernel and OS Tuning for Kubernetes Nodes35 min CPU Manager, Topology Manager, and NUMA40 min Java, Python, and Node.js on Kubernetes: Runtime-Specific Tuning40 min

Network Performance

CNI choice, kube-proxy modes, DNS, cross-zone traffic, and service mesh overhead.

5 lessons

CNI Performance: Choosing and Tuning Your Network Plugin40 min kube-proxy and Service Routing Performance35 min DNS Performance: The Invisible Bottleneck35 min Cross-Zone and Cross-Region Traffic Optimization35 min Service Mesh Performance Overhead40 min

Storage and Stateful Workload Performance

Persistent volumes, local NVMe, etcd disk performance, image pull optimization, and StatefulSet patterns.

5 lessons

Persistent Volume Performance: IOPS, Throughput, and Latency40 min Local Storage and Node-Local SSDs35 min etcd Storage Performance (Deep Dive)40 min Ephemeral Storage and Image Pull Performance40 min StatefulSet Performance Patterns40 min

Autoscaling Performance

HPA, custom metrics, Cluster Autoscaler, Karpenter, scale-to-zero, and capacity planning for peak events.

5 lessons

HPA: Getting Autoscaling Right35 min Custom Metrics Autoscaling: Beyond CPU40 min Cluster Autoscaler vs Karpenter: Node-Level Scaling40 min Scaling to Zero and Cold Start Optimization35 min Capacity Planning and Over-Provisioning40 min

Cloud-Managed Kubernetes Performance

EKS, GKE, AKS performance tuning and the capstone audit that ties every module together.

5 lessons

EKS Performance Optimization40 min GKE Performance Optimization40 min AKS Performance Optimization40 min Multi-Cloud and Managed K8s Comparison35 min Capstone: Performance Audit and Optimization Plan60 min

About the Author

Sharon Sahadevan

AI Infrastructure Engineer

Building production GPU clusters on Kubernetes. H100s, large-scale model serving, and end-to-end ML infrastructure across Azure and AWS.

10+ years designing cloud-native platforms with deep expertise in Kubernetes orchestration, GitOps (Argo CD), Terraform, and MLOps pipelines for LLM deployment.

Author of KubeNatives, a weekly newsletter read by 3,000+ DevOps and ML engineers for production insights on K8s internals, GPU scheduling, and model-serving patterns.

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