Vector databases may be unnecessary for small RAG projects, benchmark finds

A developer benchmarked exact brute-force search performance for retrieval-augmented generation applications using NumPy. The tests measured search latency across datasets from 10,000 to 1 million vectors with three common embedding dimensions. Results showed that for datasets under 100,000 vectors, simple matrix multiplication provides sufficient speed without a vector database. Between 100,000 and 1 million vectors, performance depends on traffic patterns, with approximate indexes like HNSW offering 10-100x speed gains but requiring build time and potentially imperfect recall. The analysis concludes that developers should evaluate their specific data scale and query patterns before adopting vector database dependencies.