Generate embeddings with Neon AI Gateway

Neon has added embedding-model support to its AI Gateway, allowing developers to generate vectors through an OpenAI-compatible `/v1/embeddings` endpoint using the same credentials and SDK setup used for chat models. At launch, the service offers qwen3-embedding-0-6b, a configurable 1,024-dimensional normalized model priced at $0.02 per million input tokens, and gte-large-en, a 1,024-dimensional model priced at $0.13 per million tokens. The feature integrates with Neon’s Lakebase Postgres database and Lakebase Search extensions, enabling vector, keyword, and hybrid retrieval alongside application data. Neon presents this as a complete retrieval-augmented generation workflow in which uploaded files trigger Functions, are chunked and embedded through AI Gateway, stored in Postgres, retrieved through vector or BM25 search, and passed to chat models for answer generation. Because Neon’s database, storage, functions, gateway, and search capabilities branch together, teams can test alternative embedding models, vector dimensions, chunking methods, and ranking strategies against production-like data without duplicating storage.

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