All of this means that I don't worry about most things you mention. PG upgrade? Failover and upgrade the pod. Upgrade fails? wal-g clone from object storage and rejoin cluster. Scaling up? Adjust the resource claims. If resource claims necessitate node migration, see failover scenario. It's so resilient. And this is all with raid 10 nvme direct attached storage just as fast as any other setup.
You mention etcd but people don't run etcd the way you're describing postgres. You run a redundant cluster that can achieve quorum and tolerate losses. If you follow that paradigm, you end up with postgres on k8s.
This is fine if you have a small single node database that can have some downtime. Once you need replicas, to fit your database in ram for performance, or reliability with a hot standby for failovers it becomes a lot more complicated.
You should consider what (if anything) you miss out on by just running in a single vm that you can scale out much easier down the road should you need to. Alternatively pay extra for a hosted solution that simplifies those steps further.
I’m not sure how experience could lead you to this conclusion. This wouldn’t work for any of our production needs.
This is great for the cloud provider as those are high margin services, I for one would rather have a single orchestration API that I have to interact with, that being the one offered by k8s. All the benefits of running workloads in k8s apply equally to data stores. Cattle, not pets. If your data store needs coddling then it's a pet and you're doing it wrong.