A GenAI engineer has fine-tuned a small custom classifier (a scikit-learn model wrapped in an MLflow pyfunc) that runs on a RAG pipeline's incoming queries to route them to different downstream handlers. The classifier is lightweight, receives sporadic traffic during business hours, and has near-zero traffic overnight. The team wants to minimize serving cost while accepting that occasional first-request latency after idle periods is acceptable. Which Model Serving endpoint configuration best fits these requirements?