Kubernetes

What is Kubernetes?

The AI models, APIs and databases you run in production usually live inside containers. But once you have a lot of them, who decides which machine they go on, how many resources they get, and what happens when one dies? Kubernetes — usually just K8s — is that traffic controller. You say "run three replicas," and it assigns machines, watches health, restarts failures, and even scales up or down with demand.

How's it different from Docker?

Docker handles packaging
Docker bundles your app and its dependencies into a standard container that runs anywhere.
Kubernetes handles orchestration
K8s commands that whole fleet of containers — which machine, how many, how to recover — the bigger "port management system" one level up.

Why does it matter for AI?

GPU scheduling
Inference workloads are hungry for GPUs. K8s routes requests to nodes with free GPUs so your expensive cards don't sit idle.
Auto-scaling
It adds replicas at peak and reclaims them in the trough, keeping costs down without dropping traffic.
Self-healing
When a service crashes, K8s restarts it or moves it to another machine, and users barely notice.

What pain does it actually solve?

Without an orchestrator, ops teams hand-type commands and restart services one by one — chaos once you have dozens. K8s turns deploy, scale, rollback and health checks into declarative config: you describe the desired state, it does the rest.

Bottom line: Kubernetes is the dispatcher for your containers, keeping hundreds of AI services in place and running smoothly.