Vector databases are designed for efficient storage, retrieval, and similarity search of high-dimensional vector data. They use a process called embedding to represent vector data in a continuous and meaningful high-dimensional vector space. This allows for efficient similarity searches, including cosine similarity or Euclidean distance, and can be used in applications such as generative AI, image classification, recommendation engines, and natural language processing. Vector databases offer advantages like efficient similarity search, scalability, support for high-dimensional data, and native support for vector operations, but also have limitations such as limited SQL support, no full CRUD capabilities, indexing being time-consuming, and questionable enterprise features. Pure vector databases are designed specifically for storing and retrieving vectors, while full-text search databases, vector libraries, and vector-capable NoSQL databases offer alternative approaches to handling vector data. SingleStoreDB is a robust and full-context vector database that allows users to store and query vector data alongside traditional structured data, providing a unified platform for various types of queries and analysis. It offers benefits like simplicity, lower costs, and the ability to mix and match metadata, SQL, and JSON, making it suitable for enterprise gen AI use cases.