Building high-performance compound AI applications with MongoDB Atlas and Baseten

Compound AI systems integrate multiple AI models and processing steps to form a cohesive workflow capable of handling complex tasks. These multi-step processes can introduce high latency and performance bottlenecks in production applications. Using MongoDB Atlas and Baseten’s Chains framework for compound AI, developers can build high-performance compound AI systems like RAG that can scale to handle massive production traffic without introducing bottlenecks. By combining MongoDB Atlas Vector Store for data retrieval and Baseten for model inference, developers can create scalable, secure, performant compound AI applications.