If you have 200 YAML files for a single service and 46 clusters I think you're using k8s wrong. And 5 + 3 different monitoring and logging tools could be a symptom of chaos in the organization.

k8s, and the go runtime and network stack have been heavily optimized by armies of engineers at Google and big tech, so I am very suspicious of these claims without evidence. Show me the resource usage from k8s component overhead, and the 15 minute to 3 minute deploys and then I'll believe you. And the 200 file YAML or Helm charts so I can understand why in gods name you're doing it that way.

This post just needs a lot more details. What are the typical services/workloads running on k8s? What's the end user application?

I taught myself k8s in the first month of my first job, and it felt like having super powers. The core concepts are very beautiful, like processes on Linux or JSON APIs over HTTP. And its not too hard to build a CustomResourceDefinition or dive into the various high performance disk and network IO components if you need to.

I do hate Helm to some degree but there are alternatives like Kustomize, Jsonnet/Tanka, or Cue https://github.com/cue-labs/cue-by-example/tree/main/003_kub.... You can even manage k8s resources via Terraform or Pulumi