Vector Database Architecture: What Tech Buyers Should Evaluate
Couchbase, Monday, September 7th, 2026
Couchbase's buyer guide explains vector database architecture and eight questions to ask vendors before choosing one.
The guide breaks vector database architecture into embeddings, indexing, retrieval and storage layers, and explains how each affects the performance, cost and accuracy of AI search and RAG applications.
It notes that embedding model choice drives dimensionality and re-embedding costs, and covers semantic, vector and hybrid search. Couchbase argues the key decision is choosing a multi-model database that stores vectors alongside operational data rather than a standalone vector store.
It closes with eight vendor questions on indexing, recall tuning, hybrid search, scalability, deployment and security.
Couchbase CATALYST
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