Vector databases are specialized databases that store high-dimensional vectors generated by machine learning models, such as natural language processing and computer vision models. These vectors represent objects or data points in a numerical format, enabling efficient similarity searches and classification. Vector databases offer benefits like fast query performance, scalability, and cost efficiency, making them suitable for applications involving content discovery, recommendation systems, chatbots, and generative AI. They provide an optimized solution for working with high-dimensional data, outperforming alternatives like relational databases and NoSQL databases in certain use cases. By integrating vector databases into software development workflows, developers can improve the accuracy and speed of queries, as well as enhance storage and cost efficiency.