Learned cost models (LCMs) have recently gained traction as a promising alternative to traditional cost estimation techniques in data management, offering improved accuracy by capturing complex interactions between queries, data, and runtime behavior. This paper explores extending learned cost models from batch to streaming systems, addressing the unique challenges of continuous query optimization in streaming environments.