Scalable Inference Architectures for Compound AI Systems: A Production Deployment Study

ArXi:2604.25724v1 Announce Type: new Modern enterprise AI applications increasingly rely on compound AI systems - architectures that compose multiple models, retrievers, and tools to accomplish complex tasks. Deploying such systems in production demands inference infrastructure that can efficiently serve concurrent, heterogeneous model invocations while maintaining cost-effectiveness and low latency.