Vector databases excel at storing and querying high-dimensional vector embeddings, enabling AI applications to find semantic and perceptual similarities through specialized index structures optimized for nearest-neighbor search. In-memory databases prioritize extreme performance by storing data primarily in system memory rather than on disk, delivering microsecond-level latency and exceptional throughput for time-sensitive applications. As applications increasingly demand both AI-powered insights and ultra-low latency, the boundaries between these specialized database categories are beginning to blur. Many vector databases now offer in-memory components for performance-critical operations, while some in-memory databases are adding vector support to accommodate AI workloads. For architects and developers designing systems in 2025, understanding when to leverage each technology—and when they might complement each other—has become essential for building applications that balance sophisticated AI capabilities with the performance demands of modern, real-time systems. The decision often hinges on your specific workload characteristics, latency requirements, and scaling needs rather than simply choosing one approach over the other.