Traditional ML and LLM serving share the same needs on Kubernetes: consistent deployment APIs, reliable scaling, and an operational model teams can standardize and automate. This session gives an architecture-first overview of KServe, a Kubernetes-native model serving control plane for classic inference and LLM workloads (a CNCF incubating project since Nov 2025). We then explain why KServe introduced LLMInferenceService (LLM-D integration): to support LLM-focused serving patterns within the same KServe foundation, with clear responsibilities and a Kubernetes-native request flow. You’ll leave with a strong mental model of how KServe and LLMInferenceService fit together and how to approach adoption on real platforms. We close with a short demo installing KServe and deploying a minimal inference service (no benchmarking or tuning).