I'm not against k8 per se, but its complexity isn't something to ignore. It's all good if you have the people to run this but most places I know don't.
I think we have reached peak k8-hype last year when F. Campoy at GOTO suggested to wrap K8 around GO to make Go as resilient like Erlang/BEAM (see video below ...) https://www.youtube.com/watch?v=ScE9TnoWltA&t=1437s
as for ML I really think there are cool use-cases, but why would a pre-MVP start-up already be talking about hiring data scientists, ... when they don't even have any data (exception being they are an analytics start-up obviously).
this is how you get ants.
Nowadays it's super easy to setup a CICD pipeline. The basic settings and intro examples alone are enough to build and publish container images to a container registry.
> and where 1 of the devs loves K8 and the others don't really know it (but don't object because it's nice to have it on the CV), whether they should really already focus on orchestration and scaling before they even have a MVP.
That doesn't make any sense. Developing services to run on kubernetes is a software architecture problem, one which only affects some minot dev level details such as supporting env variable settings. Devs can be entirely oblivious to kubernetes and still be effective.
> I think we have reached peak k8-hype last year when F. Campoy
That's largely irrelevant and misses the whole point. Just because some people present some gimmicky take on a technology it doesn't mean the fundamentals are not good. Kubernetes allow distributed services to be deployed transparently across multiple providers (thus you are not held hostage by a seller), and allow services to automatically scale horizontally with demand and within resource limits gracefully degradate after that point. That doesn't go away if someone decides to reenact Inception with buzzworthy technologies.