The text explores the strategic considerations for data architecture in AI applications, highlighting the benefits and challenges of split versus unified architectures. A unified architecture, such as MongoDB Atlas, integrates both operational data and vector search functionalities, simplifying data management, ensuring consistency through atomic transactions, and reducing development complexity. This approach is contrasted with split architectures, which separate operational and vector databases, leading to increased synchronization challenges and potential inconsistencies. The unified model is presented as a cost-effective and future-proof solution, particularly suited for AI workloads, where rapid development, consistency, and integration are crucial. The article underscores the importance of selecting the right data architecture to enhance AI capabilities, improve developer velocity, and maintain operational reliability, with the unified approach providing a streamlined, scalable, and efficient foundation for AI-driven innovations.