Kubernetes
Kubernetes Node Pool Optimization: Right-Size Your Infrastructure
Node pools are the foundation of your Kubernetes cluster's compute capacity. Choosing the right instance types, configuring autoscaling correctly, and managing multiple pools for different workload types can make the difference between a cost-efficient cluster and one that burns money.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
Choosing Instance Types for Kubernetes
Match instance types to workload profiles. For general web applications, balanced instances like AWS m6i or GCP e2-standard work well. For memory-intensive workloads (Elasticsearch, Redis), use memory-optimized instances like r6i. For CPU-bound workloads (video encoding, compilation), use compute-optimized instances like c6i. Favor fewer large instances over many small ones: a cluster of 5 nodes with 16 CPUs each has less scheduling overhead than 20 nodes with 4 CPUs each, and large instances waste fewer resources on system-reserved capacity (kubelet, kube-proxy, OS overhead).
System Pool vs Workload Pool Separation
Create a dedicated system node pool for cluster infrastructure: CoreDNS, ingress controllers, monitoring stack, and logging agents. Taint these nodes with `node-role=system:NoSchedule` and add tolerations to system workloads. This prevents application pods from consuming resources needed by critical cluster services. For application workloads, create one or more workload pools. Use node affinity and taints to direct specific applications to appropriate pools: GPU workloads to GPU nodes, batch jobs to spot instance pools, production apps to on-demand pools.
Autoscaling and Bin Packing Efficiency
The Cluster Autoscaler scales node pools independently based on pending pods. Configure each pool with appropriate min and max sizes. Use `--expander=least-waste` to prefer the node pool that would have the least idle resources after scheduling. Monitor bin packing efficiency: if your nodes are only 40% utilized on average, you are over-provisioned. The Vertical Pod Autoscaler can right-size pod requests, which improves bin packing. On AWS, Karpenter replaces the Cluster Autoscaler with a more intelligent provisioner that selects optimal instance types per workload, combining multiple instance families and sizes dynamically.
Why Anubiz Engineering
100% async — no calls, no meetings
Delivered in days, not weeks
Full documentation included
Production-grade from day one
Security-first approach
Post-delivery support included
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.