What is Vector search?
Verified 5 August 2026
Exact nearest-neighbour search means comparing the query against every vector, which does not scale. Approximate methods trade a small amount of recall for large speed gains — clustering the space and searching only the nearest cells, or building a navigable graph over the vectors.
The binding constraint at scale is memory. Holding raw float vectors in RAM costs about 3 KB each at 768 dimensions, so a billion vectors would need several terabytes. Quantisation reduces this dramatically: one-bit quantisation stores that same vector in about 96 bytes.
How a given engine resolves that trade-off is one of the main things separating vector search products, and most treat it as proprietary.
How unlob handles this: Benchmarks
Common questions
What is Vector search?
Vector search finds the nearest neighbours of a query vector among document vectors, usually using an approximate index because exact search over millions of vectors is too slow.
How does Vector search work in practice?
The binding constraint at scale is memory. Holding raw float vectors in RAM costs about 3 KB each at 768 dimensions, so a billion vectors would need several terabytes. Quantisation reduces this dramatically: one-bit quantisation stores that same vector in about 96 bytes.
See it working
The free tier is 10,000 requests a month with no card — enough to test any of this against your own queries.