How Vector Databases Search Millions Without Brute Force

Vector databases use embeddings to represent data as points in high-dimensional space, enabling similarity search via distance metrics like cosine similarity. Instead of comparing a query to every stored vector, they employ approximate nearest neighbor algorithms such as HNSW or IVF to find close matches in logarithmic time. This allows searching millions of vectors without checking each one individually.