Kubernetes consulting for clusters that need to scale

What does Kitecorp deliver in a Kubernetes engagement?

Four things, in order. A written assessment of the clusters you have: workloads, node pools, networking, storage, the autoscaling that is configured and the autoscaling that is actually happening. An implementation plan that names the changes, the order they ship in and the proof for each one. The implementation itself, done with your engineers on your repositories, so nothing lands that only we understand. A handover with runbooks, dashboards and alerts your on-call rotation can run without us.

The assessment is the part most clients underestimate. A cluster that “works” often has requests and limits set once and never revisited, a horizontal pod autoscaler keyed on CPU for a service bound by memory, and a node group sized for the last traffic peak rather than the next one. Reading those out takes days, not weeks, and it is where most of the cost and reliability gains come from.

When is Kubernetes the right choice, and when is it not?

Kubernetes earns its complexity when you run many services with different scaling profiles, need rolling deploys with health checks and rollback, and want one scheduling layer across AWS, GCP or Azure. Products that serve millions of users through operators fit that description: dozens of services, traffic that spikes with marketing campaigns, and a release cadence that cannot wait for a maintenance window.

It is the wrong choice for a single monolith with steady traffic, a team of three with no one on call, or a batch job that runs nightly. Those run cheaper and simpler on managed containers or plain virtual machines. We say so in the assessment when that is the answer, because a cluster nobody can operate is a liability, not an asset.

How do we scale a cluster without scaling the bill?

Three levers, applied in this order. Right-size requests from measured usage, because requests are what the scheduler packs and what you pay for; most clusters we read run at a fraction of their reserved CPU. Scale pods on the signal that actually limits the service, which is often request latency, queue depth or connection count rather than CPU. Then let the cluster autoscaler or Karpenter follow the pods with the cheapest node shapes that fit, with spot or preemptible capacity for the workloads that can tolerate a restart.

Multi-region adds a fourth lever: run active-active only for the services that need it, keep stateful systems in one region with a tested failover, and route users to the nearest healthy region at the edge. Done this way, growth in users does not mean the same growth in spend.

Which tools do we work with?

Amazon EKS, Google GKE and Azure AKS, plus self-managed clusters where a client runs their own control plane. Helm or Kustomize for packaging, Argo CD or Flux for GitOps delivery, Prometheus and Grafana for metrics and dashboards, and the cloud’s own load balancers and ingress controllers. We fit the tooling your team already knows where it is sound, and replace it only when it is the cause of the problem you called about.

How does an engagement run?

A discovery call to understand the system and the deadline. A written assessment with findings and a plan you can accept in full or in part. The implementation with your team, in small reviewed changes that ship behind the same pipeline your own work uses. A handover with runbooks, dashboards and alerts, and a short period of support while the on-call rotation gets used to them. Pricing follows the discovery call, once we know the scope.