Embeddings are numerical vector representations of text (or images, audio, or other data) that capture semantic meaning. Similar content produces vectors that are close together in high-dimensional space, enabling similarity search, clustering, and classification without keyword matching.
Embeddings are the backbone of modern AI search, recommendation, and RAG systems. A query like "affordable residential proxies" embedded as a vector will surface semantically related results even if they share no exact words. Most embedding models output vectors of 768–3072 dimensions. Popular providers include OpenAI, Cohere, Google, and Cloudflare's built-in AI binding.