What is Embedding?
Verified 5 August 2026
Embeddings turn semantic similarity into geometric proximity, which is what makes vector search possible. Dimensionality is a trade: more dimensions capture more nuance and cost more memory and compute per comparison.
Generation cost varies enormously by model class, and at web scale that cost is often the largest single line in building an index. Which model class a search provider uses is therefore a commercial decision as much as a technical one, and few publish it.
Common questions
What is Embedding?
An embedding is a fixed-length vector representing the meaning of a piece of text, produced by a model so that similar texts land near each other.
How does Embedding work in practice?
Generation cost varies enormously by model class, and at web scale that cost is often the largest single line in building an index. Which model class a search provider uses is therefore a commercial decision as much as a technical one, and few publish it.
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.