Model serving is a critical phase in developing machine learning products, involving the packaging, API creation, performance monitoring, and scaling of models to meet demand. The choice of model-serving tools is influenced by project-specific needs such as framework compatibility, ease of use, and deployment strategies, and these tools are categorized into model-serving runtimes and platforms. Leading runtimes like BentoML, TensorFlow Serving, and Triton Inference Server focus on optimizing model inference, while platforms like KServe and Seldon Core manage deployment and scaling. The selection process should consider factors such as compatibility, integration, complexity, performance, and cost. Each tool has distinct advantages and limitations, making it crucial to evaluate based on the project's specific requirements to ensure effective deployment and scaling of machine learning models.