A unified database architecture significantly reduces development complexity and improves data consistency compared to a split architecture. In a unified approach, a single database platform handles both operational data and vector search functionalities, simplifying the data model and eliminating synchronization requirements between separate systems. This leads to increased developer velocity, reduced time-to-market, and improved operational reliability. Unified architectures also eliminate issues like "ghost documents" and reduce the complexity of synchronization mechanisms. In contrast, split architectures introduce additional complexity, network latency, and operational overhead due to the need for synchronization between separate systems. However, a unified architecture may not be suitable for all use cases, particularly those requiring specialized search or vector database expertise. Ultimately, the choice between unified and split architectures depends on specific requirements and constraints, and organizations should evaluate their own architecture to ensure it enables them to ride the wave of AI innovation.