Managed Kubernetes services like EKS, GKE, and AKS have made container orchestration accessible to every engineering team. But as workloads grow and latency requirements tighten, some organizations are moving Kubernetes back to bare metal. The performance gains are real, but so are the operational demands. Here is what you need to know before making the switch.
Why Bare Metal Still Matters
Cloud VMs add a layer of virtualization between your workload and the hardware. For most applications, this overhead is negligible. But for latency-sensitive workloads like real-time trading systems, game servers, video encoding, and AI inference, the difference is measurable. Bare metal eliminates the hypervisor layer entirely, giving your pods direct access to CPU, memory, and network interfaces.
Network performance is where bare metal shows its biggest advantage. Cloud networking introduces additional hops through virtual switches and NAT layers. On bare metal, you can use SR-IOV (Single Root I/O Virtualization) to give pods direct access to physical network interfaces, cutting inter-node latency from milliseconds to microseconds. For distributed databases and message queues, this translates directly to higher throughput.
Setting Up Kubernetes on Bare Metal
Without a cloud provider handling infrastructure, you need to solve several problems yourself. First is the operating system: most teams choose Ubuntu Server or a lightweight distribution like Talos Linux, which is purpose-built for running Kubernetes. The initial cluster can be bootstrapped using kubeadm, which handles certificate generation, etcd setup, and control plane configuration.
Networking requires more thought than in the cloud. You will need a CNI plugin — Cilium is the most popular choice for bare metal because it uses eBPF for packet processing, which is faster than iptables-based alternatives. For load balancing, MetalLB provides a software-defined load balancer that assigns real IP addresses to Kubernetes Services from a configured pool.
Storage is another consideration. Cloud Kubernetes services provide dynamic persistent volume provisioning through their storage backends. On bare metal, you will need to run a storage solution like Longhorn, Rook-Ceph, or OpenEBS. These distribute storage across the cluster's local disks and handle replication and failover automatically.
Operational Challenges
The biggest cost of bare metal Kubernetes is operational. When a node fails in the cloud, you replace it with an API call. On bare metal, someone needs to physically or remotely diagnose the hardware, replace a drive or memory module, and bring the node back into the cluster. Your team needs runbooks for every type of hardware failure.
Monitoring needs to cover both the application layer and the hardware layer. This means tracking disk S.M.A.R.T. data, CPU temperature sensors, power supply status, and network interface error counters alongside your standard Prometheus and Grafana dashboards. IPMI or Redfish APIs provide remote management capabilities, but they require their own network infrastructure and security hardening.
Security patching and OS upgrades also become your responsibility. Managed services handle kernel updates and security patches automatically. On bare metal, you need a rolling update strategy that drains nodes, applies updates, and returns them to the cluster without downtime. Tools like Ansible and Terraform can automate this, but the automation itself needs maintenance.
When Does Bare Metal Make Sense?
Bare metal Kubernetes is the right choice when you have stable, predictable workloads that can fill dedicated hardware efficiently. If your compute needs fluctuate wildly, the cloud's elasticity is hard to beat. But if you are running hundreds of nodes at steady-state, bare metal can cut your infrastructure costs by 40-60% compared to equivalent cloud instances.
It also makes sense when you have specific compliance requirements that mandate physical hardware control, or when your workloads genuinely need sub-millisecond inter-node latency. Companies running large-scale ML training, high-frequency trading platforms, and real-time video processing are the most common adopters.
The key is being honest about your team's operational capacity. Bare metal Kubernetes requires deep systems expertise and a commitment to building the operational tooling that cloud providers give you for free. If you have the team and the workload to justify it, the performance and cost benefits are substantial.