Kubernetes scales down pretty well. I don't use network layers or crazy ingress setups. I keep it simple and Kubernetes works great. What's wonderful is that w

Kubernetes scales down pretty well. I don't use network layers or crazy ingress setups. I keep it simple and Kubernetes works great. What's wonderful is that when I work on multiple clouds, my knowledge transfers just fine. I don't think of the AWS solution or the GCS solution, I use the same kubectl to check out both, view logs, inspect and fix. Even when I got tired of waiting for GKE to spin up a node, running Github actions on a self-hosted microk8s meant instant pod starts and very little fuss. But using Kubernetes meant I got to take advantage of the Github operator, which let me reuse the same machine for multiple builds without the headaches. When I want to run some open source, I often find a helm chart the helps me get set up. Nowadays running open source packages can involve all kinds of dependencies, but getting it running on a k8s cluster to check it out, or even in prod, is a relatively straight forward editing of some values files. I've recently ran Uptrace and Superset that way. They're not a bajillion requests per second setups, they don't have to be, and it was far easier to set up than most methods. I would say your friend is right. Few people _need_ k8s but it's one interface to a bunch of complicated proprietary stuff. I can know core small, core set of k8s tools really well and forget half of the junk that I ever knew about public clouds. It's all the same patterns, transferable and reliable.