Machine learning inference differs from traditional web APIs due to its extensive GPU compute time requirements, often causing significant delays and resource bottlenecks when handling multiple requests. Task queues like Celery and message brokers such as Redis have been used to decouple API requests from computation, allowing for better handling of long-running operations and traffic spikes by processing tasks asynchronously. However, these setups involve complex configurations and infrastructure management challenges, including cold starts, resource management, and scaling coordination. Cerebrium offers a streamlined solution by integrating queuing and scaling directly into a serverless platform, eliminating the need for external queue infrastructure and simplifying configuration with a single autoscaler that uses metrics like concurrency utilization to ensure efficient scaling. This approach reduces operational complexity and costs while maintaining responsiveness and performance for production ML workloads.