Compound AI systems explained

This post explains the concept of compound AI systems, which are workflows composed of multiple interacting AI models and processing steps. It details the benefits of this modular approach, including improved performance through specialized hardware allocation (e.g., CPUs for chunking, GPUs for transcription), increased flexibility by allowing individual components to be swapped or upgraded, and cost efficiency by using smaller, specialized models or task-specific hardware. The post also outlines the challenges in building, optimizing, and serving compound AI systems in production, such as coordination logic, I/O latency between models, and the need for infrastructure that mirrors the system's modularity. Baseten's 'Chains' SDK is presented as a framework for building and orchestrating these systems, enabling developers to combine business logic with ML models, customize hardware and scaling for distinct processing steps, and gain visibility into performance metrics across the entire workflow.