Generate embeddings with Neon AI Gateway

Neon added embedding models to its AI Gateway, serving them on the same OpenAI-compatible /v1/embeddings endpoint and with the same credential customers already use for chat, the company said. The embeddings run on Databricks Foundation Model APIs and are billed through Neon at each lab's published per-token price with no markup, with vectors written into Lakebase Postgres and queried via Lakebase Search. Lakebase Search ships as two extensions, lakebase_vector with the lakebase_ann index and lakebase_text with the lakebase_bm25 index, and Neon said a single lakebase_ann index scales past 1 billion vectors.

We're building backends When a coding agent ships an app today, it deploys the Neon backend https://neon.com/blog/neon-backend-is-ga - Lakebase Postgres https://neon.com/docs/postgres/overview our database plus Object Storage https://neon.com/docs/storage/overview , Functions https://neon.com/docs/compute/functions/overview , Managed Better Auth https://neon.com/docs/auth/overview , and AI Gateway https://neon.com/docs/ai-gateway/overview . Every primitive branches with your data. Neon now includes AI Gateway https://neon.com/blog/llms-belong-in-your-backend , our primitive for calling LLMs. You get frontier and open-weight models hosted by Databricks, billed through Neon at the labs' own prices no markup . The latest addition to AI Gateway: it now serves embedding models https://neon.com/docs/ai-gateway/embeddings on the same OpenAI-compatible endpoint and with the same credential you already use for chat. Point your SDK at /v1/embeddings , write the vectors into Lakebase Postgres https://neon.com/lakebase , and query them with Lakebase Search https://neon.com/docs/ai/lakebase-search . The whole retrieval pipeline, from the uploaded file to the generated answer, runs inside one Neon branch. Ask your agent to set it up: Or set it up yourself with the OpenAI SDK: Quick intro on the primitives The pipeline above involves three pieces of the Neon backend: Lakebase Postgres https://neon.com/docs/postgres/overview our database , AI Gateway https://neon.com/docs/ai-gateway/overview , and Lakebase Search https://neon.com/docs/ai/lakebase-search . In case you're new here, here's a quick primer on each. Lakebase Postgres Lakebase Postgres https://neon.com/docs/postgres/overview is the Neon database, the center of the Neon backend: - It's 100% Postgres, but serverless: instant to provision, with autoscaling, and scale to zero, with usage-based pricing https://neon.com/pricing and a generous free plan https://neon.com/pricing - It's built on the lakebase architecture https://neon.com/docs/introduction/architecture-overview : compute is separated from versioned, copy-on-write storage - Lakebase Postgres also branches: a branch https://neon.com/docs/introduction/branching is an isolated copy of your database that's ready in seconds and doesn't duplicate storage - teams use it to automate all kinds of workflows that would otherwise involve a "dev instance". The rest of the Neon backend primitives also branch, following the database AI Gateway AI Gateway https://neon.com/docs/ai-gateway/overview lets you call models from Neon instead of collecting lab accounts: - A Neon credential with the ai gateway:invoke scope reaches the whole model catalog https://neon.com/docs/ai-gateway/models . Switching models means changing a string - Models are served on Databricks Foundation Model APIs https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/ , and we pass through each lab's published per-token price - Chat completions and embeddings sit on an OpenAI-compatible /v1 path, so the OpenAI SDK works once you change the base URL and key - Every branch gets its own gateway host, and neon env pull writes NEON AI GATEWAY TOKEN and NEON AI GATEWAY BASE URL for the branch you're on. Inside a Neon Function https://neon.com/functions , both are injected for you Lakebase Search Lakebase Search https://neon.com/docs/ai/lakebase-search is vector, keyword, and hybrid search inside Lakebase Postgres, delivered as two extensions: - lakebase vector adds the lakebase ann index for vector similarity search. It uses the same vector types, distance operators, and query syntax as pgvector , so there's nothing to migrate, and a single index scales past 1 billion vectors. - lakebase text adds the lakebase bm25 index for keyword search, with real BM25 ranking and top-K pushdown on standard tsvector columns. Both indexes live in the same Postgres as your app data, so one query can combine them, join your tables, and filter by tenant. Lakebase Search is also built for scale to zero: indexes live in storage, so they're ready after a cold start and available on every branch with no rebuild. Leading on price-performance We recently ran VectorDBBench on LAION-100M, and Lakebase Search led the tested systems on price-performance. See the results in Lakebase Search: the retrieval primitive for agents on Neon https://neon.com/blog/lakebase-search-retrieval-agents . What's new: embeddings on AI Gateway AI Gateway now exposes POST /v1/embeddings . It accepts a single string or a batch of up to 150 strings in one request, and returns vectors in the standard OpenAI response shape. Two models are available at launch: | Model | Dimensions | Normalized | Price as of Oct 2026 | |---|---|---|---| | qwen3-embedding-0-6b | 1024 configurable | Yes | $0.02 per 1M input tokens | | gte-large-en | 1024 | No, use cosine distance | $0.13 per 1M input tokens | Availability AI Gateway is available on the Launch and Scale plans, paid with prepaid credits https://neon.com/docs/ai-gateway/prepaid-credits . If you'd like to try it for free, tell us on Discord https://discord.gg/92vNTzKDGp : we have credits to give. Choosing a model If you're unsure what to choose, start with qwen3-embedding-0-6b , since it's the cheapest option. Use gte-large-en if you already have vectors produced by it and need new embeddings to match. The Embeddings guide https://neon.com/docs/ai-gateway/embeddings covers the request and response fields, Python and curl examples, the dimensions parameter, and how to use the embedding models with the Vercel AI SDK through @neon/ai-sdk-provider https://www.npmjs.com/package/@neon/ai-sdk-provider . Build a complete retrieval pipeline on a Neon branch Having embeddings in AI Gateway is useful on its own, but it gets more interesting when you zoom out and look at the whole pipeline they're part of. If you think about the features teams are constantly shipping these days a support bot that answers from your docs, search across the files your users upload, an agent with memory , under the hood, they all have a similar shape. Content comes in → gets turned into vectors → gets stored → gets searched when a question arrives → the best matches go to a model that writes the answer. Now that AI Gateway serves embeddings, every step is backed by a Neon primitive: 1. Ingest → Object Storage + Functions: e.g. a user uploads a file to Object Storage https://neon.com/docs/storage/overview , and a Function Trigger https://neon.com/docs/compute/functions/triggers/object-storage invokes a Neon Function https://neon.com/docs/compute/functions/overview to process it 2. Embed → AI Gateway: the Function splits the file into chunks and turns each one into a vector with AI Gateway's /v1/embeddings endpoint 3. Store → Lakebase Postgres: the chunks and their vectors go into Lakebase Postgres, next to the rest of your app data, so they can be joined and filtered like any other rows 4. Retrieve → Lakebase Search: when a question arrives, Lakebase Search finds the most relevant chunks with vector, keyword, or hybrid search 5. Generate → AI Gateway: the matches go to a chat model through the same gateway and credential, and the model writes the answer To build something like this, hand a prompt like this to your agent: Branching is the connective tissue of this experience Create a Neon branch and the whole pipeline follows: it reflects production exactly, it's ready immediately, and it stays lightweight, because none of your rows or files are duplicated. For a retrieval pipeline, this makes it safe and easy to switch embedding models or vector sizes. On a branch, you can re-embed, run your evals against real production data, and keep the change only if it wins. The same loop works for all kinds of experiments: a new chunking strategy, a different hybrid-search weighting, or a different chat model. Try it Generate, store, and search embeddings in one Neon backend. If you're building with a coding agent, start by setting it up with Neon https://neon.com/docs/get-started/with-an-agent : one command installs the Neon CLI, agent skills, and MCP server. It's also good to point your agent to these docs: - Embeddings on AI Gateway https://neon.com/docs/ai-gateway/embeddings - for the endpoint, models, and request options - Lakebase Search quickstart https://neon.com/docs/ai/lakebase-search-get-started - for vector, keyword, and hybrid search - Trigger a function on object upload https://neon.com/docs/compute/functions/triggers/object-storage - to automate the ingest step - Tour the Neon backend https://neon.com/docs/get-started/backend-overview - for how the primitives fit together in one neon.ts Every docs page is also available as markdown add .md to the URL , and llms.txt https://neon.com/docs/llms.txt indexes all of them, so your agent can read the exact reference.