Orchestration when you actually need it
Kubernetes is powerful and complex, so we reach for it only when the workload justifies it: many services, independent scaling, or strict availability needs. When it fits, it gives you self-healing deployments, rolling updates, and fine-grained scaling. When it does not, we are honest and use something simpler, because the wrong tool here is expensive to run.
- Self-healing deployments
- Rolling updates with no downtime
- Used only when it earns its place
Networking and ingress done properly
We set up ingress, service discovery, and sensible network policies so traffic flows where it should and nowhere it should not. Each workload gets the resources it needs with limits that protect the rest of the cluster. The aim is a cluster that is predictable under load, not one that falls over when one service misbehaves.
- Ingress and service discovery
- Resource limits per workload
- Network policies for isolation
Autoscaling and observability
We configure horizontal autoscaling so pods follow demand, and add metrics, logs, and alerts so you can see what the cluster is doing. Problems surface with the context to fix them, and capacity adjusts on its own. Without that visibility, Kubernetes turns every incident into guesswork, so we treat observability as part of the setup.
- Horizontal pod autoscaling
- Metrics, logs, and alerts
- Capacity that follows demand
Defined as code, on AWS
We run Kubernetes on AWS with EKS, with manifests and infrastructure managed as code so the cluster is reviewable and repeatable. Deployments go through pipelines, not manual kubectl from a laptop. That keeps the cluster consistent across environments and makes handover to your team straightforward.
- EKS on AWS
- Manifests and infra as code
- Pipeline-driven deployments
Part of the wider stack
Kubernetes is one option within our cloud architecture and DevOps work. It suits microservices and systems built to scale.
Kubernetes questions
Often not. For many products ECS Fargate runs your containers with far less overhead. We recommend Kubernetes when you have many services, independent scaling needs, or strict availability requirements, and we say so plainly when something simpler is the better fit.
Yes. We run it on EKS, with networking, autoscaling, and monitoring configured, and the cluster defined as code so it is repeatable and reviewable.
Yes. We can audit an existing Kubernetes setup, tighten resource limits, networking, and observability, and bring it under infrastructure as code.
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