Side by side diagram of semantic vector search versus keyword search

Vector Database or Full-Text Search? The Question Most Teams Skip

Vector search became the default answer to "how do we build search" the moment embeddings got cheap and easy. It's not always the right default — plain full-text search still wins for a lot of real use cases.

✅ When Full-Text Search Still Wins

If users mostly search with specific terms they expect to match exactly (product names, SKUs, exact phrases), full-text search is faster, cheaper, more debuggable, and doesn't introduce the "why did it return this irrelevant result" problem semantic search can produce.

✅ When Vector Search Actually Earns Its Place

When queries are conceptual rather than exact — someone describing what they want rather than naming it precisely, or needing to match meaning across different phrasing — that's the case vector search was built for, and full-text search genuinely can't do it.

🔀 The Hybrid Reality Most Production Systems Land On

Most real search features that matter end up combining both — full-text for exact-match confidence, vector for conceptual fallback — rather than picking one. Starting with "which one" is often the wrong framing from day one.