Kubernetes scheduling looks simple from the outside: a Pod needs a node, the scheduler picks one. But real platforms quickly discover that “where should this workload run?” is not one question. It is many.

Batch jobs need queues and fairness. AI workloads need GPUs and gang scheduling. Multi-tenant platforms need quotas, priorities, preemption, and isolation. Edge or hybrid clusters need placement across failure domains. For agentic workloads everything is about latency. Some teams can stay with the default Kubernetes scheduler, while others eventually reach for Kueue, Volcano, custom scheduler plugins, deschedulers, autoscalers, or external orchestration systems.

In this talk, we will travel through several “worlds” of Kubernetes orchestration and scheduling. For each world, we will look at the problem it solves, the trade-offs it introduces, and when it is probably the wrong tool. The goal is not to crown one scheduler as the answer to everything, but to build a practical mental map for choosing the right orchestration pattern for your workload.

And yes, we will end up with 42 scheduling problems you should ask about before building your next platform.