The practical choice is less exotic than the category name suggests. Pinecone, Weaviate, Qdrant and Azure AI Search are all capable, but for a large share of mid-size corpora pgvector alongside an existing Postgres instance is the correct, boring answer — no new infrastructure to operate, and one less system in the failure path. Be sceptical of any recommendation made before someone has looked at your scale and latency requirements.
A common and costly error is relying on vector similarity alone. Embedding search performs poorly on exact identifiers — part numbers, clause references, error codes — which is precisely what enterprise users search for most. Combining vector search with keyword search and a reranking pass fixes this, and its absence is the single most frequent defect in underperforming retrieval systems.
Codazz builds this in production — RAG Development.