Kubernetes platforms your developers can ship on without filing a ticket

Not always, and we will say so if you do not. Kubernetes pays off when you run many services, several teams deploy independently, or you need fine-grained scaling and isolation. A single application with modest traffic is often simpler and cheaper on managed container services such as Amazon ECS with AWS Fargate, Azure Container Apps or Google Cloud Run. We compare the options against your team size, traffic pattern and operating budget before recommending one.

An internal developer platform is the set of self-service tools, templates and automated workflows that let developers build, deploy and run software without deep knowledge of the infrastructure underneath. It usually combines a service catalogue or portal, golden path templates, CI/CD pipelines, environment provisioning and built-in observability. A platform team maintains it as a product, with developers as its users and their feedback shaping what gets built next.

A golden path is a supported, pre-built route for a common task, such as creating a new API service, with sensible defaults already in place. It typically includes a repository template, Dockerfile, deployment configuration, health checks, logging, metrics and a pipeline. Developers can step off the path when they have a good reason, but staying on it means less setup, consistent security and fewer surprises once the service is in production.

For most organisations a managed service such as Amazon EKS, Azure AKS or Google GKE is the right starting point, because the provider runs the control plane and handles much of the patching. The choice between them usually follows where your data, identity and other workloads already live. Self-managed clusters mainly suit on-premises, edge or strict data residency cases, and they need noticeably more operational effort to run well.

We migrate in waves, starting with a low-risk service that exercises the main golden path. Each application is containerised with Docker, given health checks and externalised configuration, then deployed alongside the current setup. Traffic shifts gradually through the load balancer or DNS, and the old environment stays available for rollback until the new one has run cleanly under real load. Databases usually remain on managed services rather than moving into the cluster.

We give each team or service its own namespace with resource quotas, then deny network traffic between namespaces by default and open only the paths a service needs. Every workload runs under its own service account, mapped to cloud permissions through EKS Pod Identity, AKS Workload Identity or GKE Workload Identity, so pods never share stored cloud keys. Pod security standards block privileged containers. Where tenants need stronger separation, such as a large customer or a sensitive workload, we use dedicated node pools or a separate cluster.

Upgrades are planned, rehearsed and applied one minor version at a time. We check your manifests and Helm charts for deprecated APIs, upgrade a non-production cluster first and run smoke tests, then roll production node pools gradually so workloads move without interruption. Because the cluster is defined in Terraform and GitOps, each upgrade is a reviewed code change rather than a list of manual commands someone has to remember.

Yes, where measured usage shows waste, and in clusters it tends to sit in a few predictable places. Resource requests set far above real consumption stop the scheduler packing pods onto fewer nodes, so we right-size them from Prometheus data. We tune the cluster autoscaler so empty nodes are removed, move interruption-tolerant workloads to spot or preemptible node pools, and label namespaces so cost can be reported per team. Headroom for peaks stays in place.

We use both, depending on what each workload needs. Helm suits packaging reusable services and third-party add-ons with configurable values, while Kustomize works well for layering environment-specific changes over plain manifests. Many platforms combine them, with Helm charts for shared components and overlays per environment. Either way, the rendered configuration lives in Git and Argo CD applies it, so environments stay consistent and every change is traceable.

It depends on how many services, environments and teams the platform must support from the start, and on whether your applications already run in containers. A managed cluster with GitOps delivery, monitoring and one golden path, proven on a single well-defined service, is a realistic first release within a few weeks of discovery. Onboarding a large estate takes longer and is planned team by team, so developers use the platform well before every service has moved.

Kubernetes platform work is quoted as a fixed price for each agreed phase, set after discovery once the scope is clear. The main drivers are the number of clusters and regions, how many services must be containerised or onboarded, whether you need a developer portal or only templates and pipelines, and whether we support upgrades afterwards. We estimate cluster running costs separately, covering control-plane fees, node pools, load balancers and log storage, so the build cost and the monthly bill are both visible.

Not always, but someone must clearly own the platform. Kubernetes ships around three minor releases a year and managed providers retire older versions on a published schedule, so upgrades, add-on patching and alert response are recurring work rather than a one-off. A single managed cluster can often be run by a few engineers alongside their other duties. Several clusters and teams usually justify a dedicated platform team. We agree ownership in writing, train your engineers with runbooks and pairing, or continue handling upgrades for you.