The field of database systems is witnessing a significant shift towards integrating vector search capabilities into operational databases, leveraging the benefits of distributed databases such as high availability, durability, and scale. This convergence is driven by the need for efficient and cost-effective solutions for high-dimensional vector searching, a crucial aspect of various applications including AI and semantic search. Recent developments focus on optimizing vector search quality and cost within cloud-native operational databases, rather than relying on specialized vector databases. Furthermore, there is a growing emphasis on workload-driven cost optimization in key-value stores and multi-tenant NoSQL serverless databases, aiming to enhance resource utilization and reduce costs in large-scale data serving environments. Noteworthy papers in this area include: Cost-Effective, Low Latency Vector Search with Azure Cosmos DB, which presents a scalable and high-performance vector search system built inside Azure Cosmos DB, and TierBase: A Workload-Driven Cost-Optimized Key-Value Store, which introduces a cost model and a distributed key-value store that optimizes total cost by strategically synchronizing data between cache and storage tiers. These innovations underscore the direction of the field towards more integrated, efficient, and cost-effective database solutions.