Compound AI systems integrate multiple models and processing steps to form an integrated workflow, offering flexibility, performance, and cost efficiency over monolithic workflows. They are becoming increasingly popular in various industries as new AI models set records across different domains, enabling the creation of more complex tasks such as multi-modal chatbots, phone call agents, video editors, and healthcare diagnostic support systems. The modularity of compound AI systems allows developers to iterate on individual components, reuse them in different pipelines, and make cost-effective design options. However, building, optimizing, and serving these systems in production can be challenging due to coordination logic requirements, intra-cloud roundtrips, and monolithic server limitations. To address these challenges, open-source frameworks like Chains are emerging, providing tools for building and orchestrating compound AI systems with ease.