Introducing Modal Auto Endpoints: Optimized inference you actually own

Modal Auto Endpoints offer a streamlined approach to managing large language model (LLM) inference, enabling teams to maintain control over their inference processes without sacrificing cost-performance or developer efficiency. Unlike traditional proprietary models, Modal emphasizes transparency by providing access to the underlying code, metrics, and performance data, allowing users to optimize and understand their inference engines fully. This service eliminates the need for extensive GPU reservations by using a pay-as-you-go model and leverages a robust autoscaling system to handle varying demand efficiently. The platform includes Modal Servers for ultra-low-latency routing, ensuring reliable performance with minimal overhead, and offers a declarative interface for easy configuration based on workloads and service level objectives (SLOs). By focusing on open-source development and providing comprehensive benchmarking tools, Modal positions itself as a forward-thinking solution, aiming to automate and enhance inference performance continually.