When AI developer and ops teams first adopt large models on Kubernetes, the focus is usually on getting something to run. Initially, the inference service may respond correctly, latency is acceptable, and a proof of concept becomes a production endpoint. It can feel like the hard part is over.
As adoption grows, however, operations start to change. Running a model is relatively straightforward on Day 0, but operating inference infrastructure reliably across regions, unpredictable traffic patterns, and cloud providers is where Day 1 and 2…
