Vector Library versus Vector Database

Vector Search uses vector embeddings generated by Machine Learning models to represent data objects such as text, images, or audio. Both Vector Databases and Libraries use Approximate Nearest Neighbor (ANN) algorithm for efficient search through vectors. Key differences between them include the ability of databases to store and update data, support CRUD operations, real-time search, and additional features like sharding, replication, automated backups, deployment ecosystems, SDKs, execution methods, communication with apps, multi-tenancy, arbitrary aggregations, and module ecosystem. Vector Libraries are commonly used for applications that do not have changing data while vector databases are great to use for applications where data is constantly changing.