Why is metadata filtering important in vector search and what are pre- vs post-filtering tradeoffs?

Metadata filtering restricts search to vectors matching structured attributes (date, source, tenant, language, access permissions), which is essential for correctness (multi-tenant isolation, security) and relevance (only search recent or authorized docs). Post-filtering runs ANN search first then drops non-matching results, which is simple but can return too few or zero hits if matches are rare in the top-k. Pre-filtering restricts the candidate set before/during the ANN traversal, guaranteeing enough matching results but potentially breaking the ANN index's efficiency (graph or cluster structure assumes the full set). Modern vector DBs (Qdrant, Weaviate, pgvector, Pinecone) implement filtered ANN that integrates the filter into traversal for a balance. Design metadata schemas and indexes upfront; high-cardinality or selective filters especially need engine-native filtered search to stay both correct and fast.