When (Not) to Use Vector DB

Vector databases are not always the optimal choice for Retrieval-Augmented Generation (RAG) systems, especially when dealing with ephemeral data and a low volume of queries. The primary trade-off is the significant upfront time cost of building an index, which may not be justified if the system doesn't perform enough queries to benefit from the faster search speeds. For short-lived contexts, a simpler approach like an in-memory K-Nearest Neighbors (KNN) search or using a key-value store like Redis can be more efficient and faster end-to-end. A benchmark analysis shows that the break-even point where indexing becomes worthwhile often requires thousands of queries. This makes a key-value store a better fit for applications that generate fresh, temporary context for each user request.