Couchbase vs Kdb: Choosing the Right Vector Database for Your AI Apps

Couchbase and Kdb are both distributed databases with vector search capabilities, but they differ in their core technologies and use cases. Couchbase is a NoSQL document-oriented database that can handle JSON documents with vector embeddings, making it suitable for cloud, mobile, AI, and edge computing applications requiring vector search capabilities. It offers flexibility in implementing vector search through various approaches, such as adapting Full Text Search or integrating with specialized libraries. Kdb is a time series database designed for real-time data processing without needing GPUs, handling raw data, generating vector embeddings, and running similarity searches all in real-time. It's suitable for use cases that require multi-modal performance across various data types, including streaming data and time-series. Users should evaluate these databases based on their specific use case and perform thorough benchmarking with their own datasets to make an informed decision.