Vector databases are designed to store vector embeddings, which represent data such as text, images, or audio files in a high-dimensional space where similar data points are closer together. These databases provide similarity search capabilities, flexibility in handling high-dimensional data, speed through efficient indexing algorithms, and scalability. They can be used to improve generative AI applications by providing long-term memory, caching, and automatic data vectorization. When choosing a vector database, consider factors such as performance metrics (queries per second, query latency, recall), open-source status, ease of local usage, integration with IT infrastructure, managed cloud options, user interface, fundraising, and cost. With the increasing interest in vector databases, they are becoming more performant and useful for various use cases, including generative AI applications.