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

Couchbase and Vald are two popular vector databases used in AI applications. A vector database is specifically designed to store and query high-dimensional vectors, which are numerical representations of unstructured data. Common use cases include e-commerce product recommendations, content discovery platforms, anomaly detection in cybersecurity, medical image analysis, and natural language processing (NLP) tasks. Couchbase is a distributed multi-model NoSQL document-oriented database with vector search capabilities as an add-on. It combines the best of relational databases with the flexibility of JSON and allows developers to store vector embeddings within Couchbase documents as part of their JSON structure. These vectors can be used in similarity search use cases such as recommendation systems or retrieval-augmented generation based on semantic search. Vald is a purpose-built vector database designed for handling billions of vectors and can easily grow as your needs get bigger. It uses a super quick algorithm called NGT to find similar vectors and spreads the index across different machines, allowing searches to continue even during updates. Vald also automatically backs up your index data. When selecting between Couchbase and Vald for vector search, consider factors such as search methodology, data handling, scalability and performance, flexibility and customization, integration and ecosystem, ease of use, cost, and security features. Ultimately, the choice will depend on specific needs and priorities.