Vector databases are useful, but hybrid search often wins

Vector search is strong when users describe ideas in natural language. It can connect similar meanings even when the exact words do not match. That is useful for product discovery, document search, and RAG.

But exact terms still matter. Product names, SKUs, legal terms, error codes, dates, categories, and permissions often need keyword search or metadata filters. That is why hybrid search is common in serious systems.

It is easy to start by comparing vector databases. A better start is to define the search experience, data size, freshness, filters, permissions, latency, and evaluation method.

Once those needs are clear, the team can choose the right store, index settings, embedding model, reranker, and ingestion process.