A vector database is designed to store and query high-dimensional vectors, which encode complex information such as semantic meaning of text or visual features of images. Vector databases play a crucial role in AI applications like e-commerce product recommendations, content discovery platforms, anomaly detection, medical image analysis, natural language processing tasks, and Retrieval Augmented Generation (RAG). These databases support various indexing methods, including exact k-nearest neighbors (kNN) search, approximate nearest neighbor (ANN) search, and hybrid search. There are different types of vector databases available, such as purpose-built vector databases like Milvus, Zilliz Cloud, and lightweight vector databases like Chroma. SingleStore is a distributed relational SQL database management system with vector search capabilities, while Vearch is a purpose-built vector database designed for fast and efficient similarity searches. The choice between SingleStore and Vearch depends on the specific use case, type of data, and performance requirements. Thorough benchmarking with actual datasets and query patterns will be key to making an informed decision.