SingleStore and Vald are two powerful tools designed specifically for storing and querying high-dimensional vectors, which are numerical representations of unstructured data. A vector database is a type of database that stores and queries vectors, which are used to encode complex information such as text, images, or product attributes. Vector databases play a crucial role in AI applications, enabling efficient similarity searches and advanced data analysis. Both SingleStore and Vald offer unique strengths and weaknesses, with SingleStore embedding vector search within a SQL database, allowing for combined traditional database operations and vector search, while Vald takes a focused approach using the NGT algorithm as its core search mechanism, designed for high-speed similarity search across large vector datasets. The choice between SingleStore and Vald depends on project requirements, with SingleStore suitable for applications that need both traditional database operations and vector search, and Vald well-suited for pure vector search scenarios. Thorough benchmarking with actual datasets and query patterns is key to making a decision between these two powerful but different approaches to vector search in distributed database systems.