One year ago I might agree that Kubernetes is an overkill but today?
Ask your favorite GPT to generate manifests, get primary app into cluster with telepresence or execute straight from container and switch contexts and clusters like it's 90s again.
One reason I dislike Docker Compose and Docker is lack of isolation. Yes sure if you put your arm deep enough you can get it, but on local k8s I can spin cluster per workspace and not worry about conflicting ports between PostgreSQL instances.
Before LLMs writing consistent YAMLs was PITA but today on low/development scale it's pretty much free lunch.
Interesting. I have just started reading about Kubernetes. Is there an reading material that goes over this process you just described?
> One reason I dislike Docker Compose and Docker is lack of isolation. Yes sure if you put your arm deep enough you can get it, but on local k8s I can spin cluster per workspace and not worry about conflicting ports between PostgreSQL instances.
Using Kubernetes because you're unable to grok docker's networking enough so you can't run multiple containers using their own ports and not conflicting with other stuff sounds like a recipe for disaster, even (especially?) if you use agents for this. Particularly if you let them manage a production environment, you're bound to lose important data eventually.
> pretty much free lunch.
Aah, famous last words of the young :)
Strong agree, if there's one thing LLMs are excellent at, it's writing Terraform and Kubernetes deployments (and/or helm charts). What used to be half a day of research, trial and error, is now 20 seconds of AI churn and 98% of the time it nails it on the first try. And then point it at grafana and tell it to write you a dashboard for the new service/s. Easy peasy lemon squeezy. What used to require a team of 4 devops/SRE to support a medium sized company, can now be collapsed down into a a single part time SRE.