Traditional vs. vector search

You’ve probably run into some of these situations:

  • Searching for information about a specific topic
  • Looking for something you can describe but can’t name directly
  • Finding a song by humming or playing a short clip into an app
  • Using a map app to locate a place you want to visit
  • Getting recommendations for places to go from a mobile app
  • Finding a good product recommendation to go with the item you just added to your cart

What do these examples have in common? How do they differ? And in some cases, could a single type of search handle them—or do you need a combination?

What is traditional search?

Traditional search is what most people think of when they type into a search bar. It matches tokenized keywords, numbers, or IDs directly or with simple fuzzing (regular expressions, partial matches, etc.). It’s reliable, fast, and constantly being refined.

It works best when:

  • You have a unique ID to look up a document directly
  • You know the exact name and spelling of what you want
  • You’re comparing structured data like strings or numbers

But it struggles in other areas:

  • Performance: At very large scale, even with tricks like parallelization, search slows down
  • Accuracy and context: Keywords can’t capture meaning. Expanding queries with fuzzy matches often introduces noise, and related words don’t always share the same context

As data volumes grew and expectations for relevance increased, these limitations opened the door to new approaches.

What is vector search?

Vector search represents information as embeddings: multi-dimensional numerical vectors that capture meaning and context. Instead of matching exact words, vectors let you measure similarity mathematically.

Embeddings can model text, audio, images, or other data, turning complex concepts into numbers. Search algorithms like k-Nearest Neighbor (kNN) or Approximate Nearest Neighbor (ANN) then find the closest matches.

This approach makes it possible to:

  • Match based on meaning rather than exact words
  • Handle misspellings, synonyms, or ambiguous queries
  • Compare across modalities (like audio clips or images)

Vector search doesn’t replace traditional search—it complements it. Each excels at different things, and the real power comes when they’re combined.

The power of hybrid search: Combining the best of both worlds

Hybrid search blends the two approaches. Traditional search handles exact matches quickly, while vector search adds contextual relevance. Together, they deliver results that are both precise and meaningful.

Examples include:

  • Finding related items in an e-commerce store with vector embeddings, then filtering them with traditional search for price or availability
  • Locating the exact book in a library catalog via keywords, then using vectors to suggest other titles a reader might like
  • Searching the web for research: keywords return direct matches, while vectors expand the results to cover related concepts and spelling variations

It isn’t about choosing one approach over the other. The best search experiences combine both: traditional search for precision, vector search for context. Together, they deliver faster, smarter, and more relevant results.

If you’d like to try it yourself, spin up a free trial cluster of OpenSearch on the NetApp Instaclustr Platform and experiment with kNN search using your own embeddings.