Size bare metal Kubernetes from real microservice demand
Run internal microservices on isolated dedicated servers sized to CPU, memory, storage I/O, and failure tolerance while your team owns orchestration, observability, upgrades, and recovery.
Match bare metal servers to performance workloads
Size every node from measured demand
Map CPU, memory, storage I/O, replicas, and headroom to measured demand before locking the cluster topology.
Separate workload roles by failure domain
Separate control-plane, worker, ingress, stateful, and observability roles so saturation stays attributable under load.
Keep orchestration under your team's control
Own Kubernetes, Docker, or Podman, networking, secrets, and deployment automation on isolated hosts we supply.
Place capacity around service dependencies
Select regions and bandwidth from service locality, data paths, and the systems each internal microservice calls.
Available configurations
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Measured demand drives the node plan
Internal microservices do not become operationally clear just because they run in containers. Replica growth, east-west traffic, stateful I/O, and observability load can saturate a host before the cluster tells a useful story. Start with measured demand and failure tolerance, then assign control-plane, worker, ingress, and stateful roles deliberately before production rollout.
At Melbicom, we provide bare metal server hosting on isolated, configurable hosts, including Intel and AMD processor options, location-specific memory and storage, regional deployment, and selectable bandwidth. The host boundary makes CPU pressure, eviction risk, queue depth, and storage contention easier to attribute without implying a managed Kubernetes layer.
Your team owns the runtime, orchestration, CNI, secrets, registry, deployment automation, observability, backups, upgrades, and recovery. We provide the underlying infrastructure and 24/7 technical support for infrastructure issues. Validate nodes, migrate services in phases, keep rollback gates, and review capacity as demand changes; the boundary stays explicit at every step.
Map workload-role pressure across Kubernetes clusters
Align node capacity with service demand, failure tolerance, and operating ownership.
Attributable CPU pressure
Visible eviction pressure
Traceable storage contention
Explicit failure domains
Measured capacity headroom
Clear recovery ownership
Size each node for its workload role
Configure each host around measured demand while your team retains full ownership of the container platform.
Configurable Intel and AMD nodes
Location-specific memory choices
Storage sized for stateful roles
Bandwidth matched to east-west load
Regional placement near dependencies
Isolated hosts for failure domains
Customer-chosen container runtime
Customer-owned orchestration policy
Access to 24/7 technical support
Bandwidth up to 200 Gbps
Build the node plan
Compare configurations to demand, failure tolerance, and location needs before ordering.
Talk to an expert
Map Kubernetes on bare metal to workload roles
Control-plane capacity
Reserve CPU, memory, and failure tolerance for schedulers, controllers, and cluster-state work under sustained load.
General worker pools
Set worker capacity from replica counts, request pressure, queue depth, and measured headroom for service growth.
Ingress and API paths
Separate ingress and internal APIs when concurrency, routing, or authentication load requires distinct capacity.
Stateful service nodes
Allocate storage and memory to databases, queues, and persistent services with explicit backup and recovery plans.
Observability workloads
Isolate metrics, logs, and traces when ingestion or retention demand can hide application-node saturation.
Batch and scheduled jobs
Place batch work on dedicated capacity so bursts do not distort latency, eviction pressure, or service SLOs.
Regional service clusters
Deploy clusters near dependent systems when data locality and service paths materially change request behavior.
Migration and rollback
Validate nodes, move services in phases, check health, and keep rollback gates before expanding production.
FAQ
How should internal microservices run on bare metal Kubernetes?
Does Melbicom provide managed Kubernetes?
Can we run Docker or Podman instead?
How should we size cluster nodes?
Should control-plane and worker roles be separated?
What hardware choices are available?
Can clusters be deployed across regions?
How much bandwidth can each server use?
What does Melbicom support cover?
How should we migrate internal microservices?
What contention remains inside a cluster?
Start with a node plan
Create an account, review node options, and map each role to measured demand before ordering.
Create an account
Define the operating boundary
Share service demand and regional needs with an expert to align host fit and operating scope.
Talk to an expert