Couchbase vs MongoDB Choosing the Right Vector Database for Your AI Apps

Couchbase and MongoDB are both NoSQL databases with vector search capabilities as an add-on. Couchbase is a distributed, open-source, multi-model database that can be adapted to handle vector search functionality using workarounds like tokenizing vectors for Full Text Search (FTS) or performing similarity computations at the application level. MongoDB Atlas Vector Search has native support for vector embeddings and indexing with HNSW for Approximate Nearest Neighbor (ANN) searches, as well as Exact Nearest Neighbors (ENN) for small scale queries. Key differences include search methodology, data handling, scalability and performance, flexibility and customization, integration and ecosystem, ease of use, cost, and security. The choice between Couchbase and MongoDB depends on the specific use case and requirements of the user.