Managed Kubernetes Services in India (2026)

Traffic needs a defined entry point: a load balancer or ingress controller with a real certificate, a redirect from HTTP, and a health check that reflects the application rather than the pod's existence.

Certificate automation is the piece most often skipped. When it is manual, the first expiry becomes an outage on a Sunday, and the fix is a two-hour scramble for something that should have renewed itself.

Without requests and limits, one runaway container can consume a node and take its neighbours with it. Setting them requires knowing the application's real usage, which means measuring before guessing.

We measure under load, set requests from the observed baseline, and keep limits generous enough that throttling does not become the new outage.

Pods that need to read a bucket or send a message should get identity from the cluster, not a downloaded key. On EKS that is IRSA or pod identity, on AKS it is workload identity, on GKE it is workload identity federation.

This is the change that removes the most common credential leak, and it is much cheaper during the build than after an audit.

Scaling on CPU alone fails for queue workers and for applications waiting on a database. The right signal is the one that actually saturates first: queue depth, request concurrency or latency.

Cluster autoscaling also needs a node pool design that can grow without exhausting quotas, and a ceiling so an incident does not become an invoice.

Container logs disappear with the pod. Anything needed for an investigation has to be shipped somewhere durable, with retention chosen deliberately because log storage grows quietly.

We ship logs to the platform's managed service or a hosted collector, keep metrics for capacity planning, and alert on the four signals that precede a customer complaint.

Stateful workloads need volumes with a lifecycle and a backup that can be restored into a new cluster, not just a snapshot tied to the old one. Database operators help, but they still need a restore test.

For most teams the better answer is to keep the database outside the cluster and let Kubernetes run stateless services.

Kubernetes releases three minor versions a year and each cloud supports a window. An unpatched cluster eventually loses support, and the upgrade becomes a multi-step project with API deprecations in between.

We plan one upgrade per supported window, test it in staging, and keep manifests free of deprecated APIs so the move is routine.

The control plane is charged by the provider, nodes are charged by size, and load balancers, storage volumes and egress add to it. Rates vary by region and change, so we do not publish them.

What we control is the shape: right-sized node pools, spot capacity for tolerant workloads, and no idle clusters left running after a project ends.

One application, one team, no need for multi-service orchestration: a virtual machine and a deploy script will be cheaper to run and far easier to debug. We say that before selling a cluster.

Kubernetes earns its place when several services share a platform, deployments must be standardised, and someone can own the platform layer.

Send the services and their resource profiles, the provider you are on, whether a cluster already exists, and who will own upgrades. If a cluster exists, send its version and the current manifest repository.

Read next: cloud services for the platform picture, DevOps and CI/CD for the pipeline, or cloud cost optimisation if the cluster bill is the problem.