Hybrid Search vs Vector Search

If you've just learned about vector search and you're thinking, "that sounds better than what I have, should I just use that?" then this page is for you.

The short answer is: vector search and hybrid search aren't competing options. Vector search is a capability. Hybrid search is a strategy that uses it.

Vector search does one thing really well

Vector search finds results based on meaning. It's what lets a user type "cozy cabin vibe" and get back results that match the intent, even if none of those words appear in your content. It excels at surfacing relevant results even when the query is loose, conversational, or conceptual.

But on its own, vector search isn't great at precision. Ask it for "SKU-4872" hoping you’ll get the exact product, and it'll return a pile of results that are mathematically near that term, not necessarily the one thing you actually wanted.

Hybrid search uses vector search as one of two tools

Hybrid search combines vector search with lexical search so your application can handle both kinds of queries well. Precise lookups go through lexical. Meaning-based queries go through vector. And when you're not sure which kind of query you're dealing with, you can run both and merge the results.

The practical upside is that you don't have to choose which kind of user you're building for. If you have a product catalog with both SKUs and rich descriptions, or a knowledge base where some users search by topic and others by exact phrase, hybrid search lets you serve all of them with the same search bar.

So which should you use?

If your users only ever search by meaning, concepts, or descriptions, vector search alone might be sufficient. But most real-world applications have users who search in both ways, often without realizing it. In those cases, hybrid search is the more resilient choice.