Vector databases are a type of database that indexes, stores, and provides access to structured or unstructured data alongside its vector embeddings. They allow for efficient similarity search and retrieval of data based on their vector distance or vector similarity at scale. Core concepts around vector databases include vector embeddings and vector search, which enable efficient vector search by leveraging approximate nearest neighbor (ANN) algorithms. Vector databases are essential for efficiently managing and searching high-dimensional vector embeddings, enabling real-time accurate similarity searches that perform a critical function in the AI native app stack.