When Postgres is used for generative AI workloads, it may face challenges with scalability and performance issues, data privacy, and high availability. However, distributed PostgreSQL deployments can address these problems by providing scalability, load balancing, and geo-partitioning, ensuring data residency compliance and uninterrupted operations. By leveraging these distributed systems, you can build scalable gen AI applications that scale and never fail. Distributed Postgres can be used to reduce storage and memory usage by distributing embeddings across multiple nodes, and to improve performance by utilizing specialized indexes for vectorized data. Additionally, geo-partitioning allows for the distribution of information across locations required by data regulators, ensuring compliance with data residency requirements. Finally, distributed Postgres provides high availability, allowing gen AI apps to remain operational even during zone, data center, or regional outages.