We all love Kubernetes for its self-healing, scaling, and container orchestration magic. But let's talk about the elephant in the room: managed Kubernetes costs.
Most teams start with managed services (EKS, GKE, AKS), which are incredibly convenient. However, underneath the hood, your worker nodes are usually virtual machines. That means you are paying a hypervisor "tax" (often 5–15% CPU overhead) and dealing with I/O contention from noisy neighbors.
At Leo Servers, we've been analyzing the shift back to bare metal. Running K8s on a dedicated server gives you:
Zero Hypervisor Overhead: You get the hardware you pay for.
Predictable Storage: Direct access to NVMe SSDs.
Fixed Billing: Traffic spikes shouldn't mean bill shock.
With the rise of GPU scheduling for AI inference workloads this year, the consistent resources of a dedicated server combined with Kubernetes' Horizontal Pod Autoscaling (HPA) is a game-changer.
We just published a comprehensive guide covering K8s architecture, scaling mechanisms, security best practices (RBAC, Service Meshes), and how to structure your control plane/worker nodes on bare metal.
For the deep dive, hardware specs, and to read more, visit the blog link here: [https://www.leoservers.com/blogs/kubernetes-on-dedicated-servers/]
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