FIRST: Use the parent neon skill for a Neon overview, getting started with Neon, Neon development best practices, and more.

If the neon skill is not installed, fetch it from https://neon.com/docs/ai/skills/neon/SKILL.md or install it with:

neon skills -s neon -y

Neon AI Gateway

Currently available in aws-us-east-2, aws-us-east-1, aws-eu-central-1, and aws-ap-southeast-1.

The Neon AI Gateway brings LLM and embedding inference to your Neon branch. One API and one Neon credential give you access to frontier and open-source models from many providers (Anthropic, OpenAI, Google, Meta, and more), plus embedding models, all hosted and powered by Databricks. The catalog shifts over time, so treat /v1/models and the models.dev Neon page as the source of truth rather than a fixed provider list. For embedding model IDs and dimensions, see the embeddings guide. Your existing OpenAI/Anthropic/Gemini SDK works by changing only the base URL.

Use this skill to help the user send model calls through the gateway, wire it into the AI SDK or Mastra, and switch providers without rewiring code. Deliver a working inference request, a configured agent, or a precise answer from the official Neon docs.

When to Use

Reach for the AI Gateway whenever an app or agent needs to call an LLM or generate text embeddings and the user would rather not manage model providers themselves:

One credential instead of many provider accounts. A single Neon credential reaches the entire model catalog across every provider Databricks hosts. No separate OpenAI / Anthropic / Google billing, keys, or signups to provision and rotate.
Switch models without rewiring. The unified endpoint is OpenAI-compatible and works with every model in the catalog — change one model field to move between Claude, GPT, and Gemini. Standard SDKs (OpenAI, Anthropic, google-genai) work with just a base-URL change.
AI follows your branches. Each branch has its own gateway endpoint, scoped with the same lineage as your database. AI requests from a preview/feature branch are isolated to that branch — the same isolation your data already gets — which makes preview, CI, and agent environments self-contained.
No extra infrastructure, and it's already next to your data. The gateway lives inside your Neon project (and is injected into Neon Functions automatically), runs on the same Databricks infrastructure that serves trillions of tokens a month, and supports streaming (SSE) out of the box.

If the user already has a deep, single-provider integration and no interest in Neon branching or multi-model routing, a direct provider SDK is fine — but the moment they want one credential, model portability, or branch-scoped AI, this is the reason to use it.

What It Does

One API for all models — Frontier and open-source models behind a single endpoint, addressed by their catalog ID (e.g. claude-sonnet-4-6, gpt-5-mini, gemini-3-flash).
Standard SDKs, one URL change — OpenAI SDK and AI SDK (OpenAI-compatible MLflow/Responses routes), Anthropic SDK (native Messages), google-genai (native Gemini).
Branch-scoped — Each branch gets its own gateway host; the Neon credential authorizes requests for that branch and its descendants.
Streaming — Chat generation supports server-sent events with no extra configuration.
Embeddings — Generate vectors for semantic search.

Availability

Check these preconditions before setting anything up:

The AI Gateway is currently available in aws-us-east-2, aws-us-east-1, aws-eu-central-1, and aws-ap-southeast-1. Confirm the user's project is in one of these regions.

The AI Gateway is available on every Neon plan, including Free, and draws down prepaid AI Gateway credits. On the Free plan, the user adds a payment card and buys credits on the Billing page; the organization stays on Free. Any account with prepaid credits can call every model in the catalog, with no difference in pricing or model access between plans. See https://neon.com/docs/ai-gateway/prepaid-credits.md.

Enabling the gateway: credits and model access

The AI Gateway is credential-gated rather than a provisioning step. neon config apply / deploy, neon checkout, and neon env pull mint the branch credential and write NEON_AI_GATEWAY_BASE_URL and NEON_AI_GATEWAY_TOKEN on every plan. Whether the account can serve a model is decided per request:

No card and credits yet → no model is enabled. An account that hasn't added a card and bought AI Gateway credits, which includes every new Free account, gets every catalog model from GET /v1/models with enabled: false, unavailable_reason: "paid_plan_required", and a recovery_url pointing at the organization's Billing page, and a request returns 403 with the same unavailable_reason. Despite the name, the fix is adding a card and buying credits, not upgrading the plan. neon env pull (and the env pull bundled into apply / deploy / checkout) warns when no model is enabled, names the reason, and links the recovery_url.
Locked models. neon env pull also warns when some models are enabled but no flagship model (e.g. Anthropic Opus, OpenAI Codex / *-pro) is among them, and links to the branch's AI Gateway page in the Neon Console (https://console.neon.tech/app/projects/<project-id>/branches/<branch-id>/ai-gateway). That page lists locked models under Some models are restricted, grouped by reason, each with its fix: Add a card and buy credits, Add credits for prepaid credits, or Support for an account restriction or a model not available to the account. After the user buys credits, models unlock once Neon confirms access. No CLI warning does not mean every model is enabled; check the model's enabled value. See https://neon.com/docs/ai-gateway/troubleshooting.md.
Older CLI. neon 8.23.3 and earlier refuse aiGateway on a Free plan with "isn't available on the Free plan". Update with npm i -g neon@latest (8.23.4 or later) and re-run.

Setup

The gateway is part of neon.ts (see the neon skill for the branch-first workflow and neon.ts basics). Enable it with aiGateway:

// neon.ts
import { defineConfig } from "@neon/config/v1";

export default defineConfig({
  aiGateway: true,
});

neon deploy   # provisions the gateway on the linked branch

Neon Infrastructure as Code (`neon.ts`)

The aiGateway toggle above is part of neon.ts, Neon's infrastructure-as-code file — one TypeScript file declares the gateway alongside every other branch service, in version control (see the neon skill for the full reference). Reconcile it against a branch the Terraform way:

neon config status   # print the branch's live config (is the gateway on?)
neon config plan     # dry-run diff of what apply would change
neon config apply    # enable the gateway on the branch  (neon deploy is an alias)

The gateway is branch-scoped: each branch gets its own gateway host. When a neon.ts is present, neon checkout applies the policy as it creates a branch, so a fresh preview/CI branch comes up with the gateway already enabled. Checking out an existing branch doesn't reconcile it — run neon deploy to apply changes. Provisioning (config apply / deploy), link, and checkout also pull the branch's gateway credentials into your local .env.local, so local runs hit the same branch gateway as the deployed function (no manual env pull needed).

Environment Variables

When aiGateway is enabled, Neon injects the gateway credentials as Neon-branded env vars. Inside a deployed Neon Function these are injected automatically; locally, neon env pull writes them to .env/.env.local (or use neon-env run -- <cmd> to inject at runtime without a file):

Variable	Meaning
`NEON_AI_GATEWAY_TOKEN`	Gateway bearer token (a Neon credential, `nt_live_...`)
`NEON_AI_GATEWAY_BASE_URL`	Bare branch gateway host (`scheme://host`, no path — no `/ai-gateway`): `https://<branch-id>-api.ai.<region>.aws.neon.tech`

Neon injects only these two vars — it does not set OPENAI_API_KEY / OPENAI_BASE_URL. The @neon/ai-sdk-provider and Mastra's neon/<model> read NEON_AI_GATEWAY_* directly (zero config); for the plain OpenAI SDK / @ai-sdk/openai, build the client's apiKey + baseURL from them (shown below), or set your own OPENAI_* by hand (env pull leaves user-set vars untouched).

NEON_AI_GATEWAY_BASE_URL is the bare host — you append the dialect path yourself (which is exactly what the @neon/ai-sdk-provider does for you). The routes under the host are:

/v1 — unified, OpenAI Chat Completions-compatible; recommended default, works with every provider (/v1/chat/completions).
/openai/v1 — OpenAI Responses API (required for gpt-5-…-codex variants and gpt-5-5-pro); the @ai-sdk/openai provider uses the Responses API by default (/openai/v1/responses).
/anthropic — native Anthropic Messages (extended thinking, prompt caching). Give the Anthropic SDK this as its base URL and it appends /v1/messages itself, so the full request path is /anthropic/v1/messages.
/gemini — native Gemini generateContent. Give google-genai this as its base URL and it appends /v1beta/models/<model>:generateContent itself, so the full request path is /gemini/v1beta/models/<model>:generateContent.

So ${NEON_AI_GATEWAY_BASE_URL}/v1 is the chat-completions endpoint and ${NEON_AI_GATEWAY_BASE_URL}/openai/v1 the OpenAI Responses endpoint (both appended by you); for the native Anthropic and Gemini dialects you hand the SDK the shorter /anthropic or /gemini base and it appends the rest. See Use with Plain SDKs below.

For typed, validated access to the injected credentials, pass the same neon.ts config object to parseEnv from @neon/env — it returns an env.aiGateway namespace (apiKey, baseUrl) derived from your config.

Build Agents with the Vercel AI SDK (Recommended)

The Vercel AI SDK is the recommended way to call the gateway and build agents from TypeScript: one set of primitives (generateText, streamText, tool calling, structured output) over every catalog model, with first-class streaming for the long agent responses Neon Functions are built to host.

The dedicated @neon/ai-sdk-provider reads NEON_AI_GATEWAY_BASE_URL + NEON_AI_GATEWAY_TOKEN from the injected env with zero config and routes each model to the best endpoint (Anthropic → Messages, OpenAI/Codex → Responses, everything else → MLflow). On a Neon Function that streams text and generates images, pick an OpenAI catalog model, since the image tool runs on the Responses route:

import { neon } from "@neon/ai-sdk-provider";
import { streamText } from "ai";

const result = streamText({
  model: neon("gpt-5-mini"),
  messages,
  tools: {
    image_generation: neon.tools.imageGeneration({
      model: "gpt-image-2",
      outputFormat: "jpeg",
      size: "1024x1024",
    }),
  },
});
return result.toUIMessageStreamResponse();

A single completion is the same provider with generateText:

import { neon } from "@neon/ai-sdk-provider";
import { generateText } from "ai";

const { text } = await generateText({
  model: neon("claude-haiku-4-5"), // or gpt-5-3-codex, gemini-3-flash, ...
  prompt: "Summarize Postgres for me.",
});

Prefer @neon/ai-sdk-provider over the bare @ai-sdk/openai openai(): Neon injects only NEON_AI_GATEWAY_*, not OPENAI_*, so openai() won't pick up the gateway from the env on its own. If you do use @ai-sdk/openai, configure it explicitly with createOpenAI({ apiKey: process.env.NEON_AI_GATEWAY_TOKEN, baseURL: ${process.env.NEON_AI_GATEWAY_BASE_URL}/openai/v1 }).

To build an agent — a model that calls tools in a loop and then answers — add tools and a stopWhen budget. The loop runs in-process, so on a Neon Function it isn't cut off by lambda-style timeouts:

import { neon } from "@neon/ai-sdk-provider";
import { generateText, tool, stepCountIs } from "ai";
import { z } from "zod";

const { text } = await generateText({
  model: neon("claude-sonnet-4-6"),
  prompt: "How many open todos do I have, and what's the oldest one?",
  tools: {
    listTodos: tool({
      description: "List the user's open todos.",
      inputSchema: z.object({}), // AI SDK v5+: `inputSchema`, not `parameters`
      execute: async () => db.select().from(todos),
    }),
  },
  stopWhen: stepCountIs(5), // let the model call tools, then summarize
});

For a full AI SDK agent deployed as a Neon Function (streaming, tool calling, image generation, persistence), see the neon-functions skill's references/ai-sdk.md.

Generate Images

Images come from the OpenAI Responses image_generation built-in tool on an OpenAI model, on the /openai/v1/responses route. The gateway has no image model endpoint, so the AI SDK's generateImage() throws NoSuchModelError. Every image request follows two rules:

Use a supported image model. The gateway supports gpt-image-2, gpt-image-1.5, and gpt-image-1-mini. Set model explicitly on every tool definition, and use gpt-image-2 unless the user asks for another.
Always stream. A non-streaming response whose image is over 655,360 bytes fails with 400: The response content length cannot be greater than 655360 bytes long, and most images are larger than that. Use streamText, never generateText, when the image tool is attached. With the plain OpenAI SDK, pass stream: true to client.responses.create().

import { neon } from "@neon/ai-sdk-provider";
import { streamText } from "ai";

const result = streamText({
  model: neon("gpt-5-mini"),
  prompt: "Generate an image of a red apple on a wooden table",
  tools: {
    image_generation: neon.tools.imageGeneration({ model: "gpt-image-2" }),
  },
});

for await (const part of result.fullStream) {
  if (
    part.type === "tool-result" &&
    !part.dynamic &&
    "result" in part.output &&
    typeof part.output.result === "string"
  ) {
    const png = Buffer.from(part.output.result, "base64"); // save or upload it
  }
}

Generate Embeddings with the AI SDK

Use @neon/ai-sdk-provider 1.0.0+ with AI SDK 6+ for embedding calls. neon.embeddingModel() reads the same injected gateway variables as the language models:

import { neon } from "@neon/ai-sdk-provider";
import { embed, embedMany } from "ai";

const model = neon.embeddingModel("qwen3-embedding-0-6b");
const providerOptions = { neon: { dimensions: 1024 } };
const { embeddings } = await embedMany({
  model,
  values: documentBodies, // unprefixed document text
  providerOptions,
  maxParallelCalls: 1,
});
const { embedding } = await embed({
  model,
  value: `Instruct: Given a question about Neon, retrieve documentation passages that answer the question\nQuery: ${queryText}`,
  providerOptions,
});

The query instruction is specific to Qwen3 and Neon documentation search; adapt it to the corpus. Documents stay unprefixed. Keep the same model and dimensions for documents and queries, and keep the original query text for BM25. For schema, storage, batch limits, and an OpenAI SDK alternative, follow the neon-postgres skill's vector search guide.

Build Agents with Mastra (Recommended)

Mastra is the recommended framework when you want batteries-included agents — built-in memory, tools, workflows, and tracing — with the model still pointed at the gateway. With @mastra/core 1.47+, use a neon/<model> magic string; Mastra reads NEON_AI_GATEWAY_BASE_URL and NEON_AI_GATEWAY_TOKEN from the environment (injected by neon deploy when aiGateway is enabled). Use parseEnv only for other declared services (e.g. env.postgres.databaseUrl for @mastra/pg memory):

import { Agent } from "@mastra/core/agent";
import { parseEnv } from "@neon/env";
import config from "../neon";

const env = parseEnv(config);

export const personalAssistant = new Agent({
  id: "personal-assistant",
  name: "personal-assistant",
  instructions:
    "You are a warm, concise personal assistant with long-term memory.",
  model: "neon/claude-haiku-4-5",
  memory, // your Mastra memory store, e.g. @mastra/pg on env.postgres.databaseUrl
});

Use with Plain SDKs (Lower-Level)

When you don't need an agent framework — a single completion, an existing provider-SDK integration, or native provider features — call the gateway with the plain SDKs. Neon injects the NEON_AI_GATEWAY_* vars (not OPENAI_*), so set the client's apiKey + baseURL from them. For the OpenAI Responses dialect (/openai/v1):

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.NEON_AI_GATEWAY_TOKEN,
  baseURL: `${process.env.NEON_AI_GATEWAY_BASE_URL}/openai/v1`,
});

const res = await client.responses.create({
  model: "gpt-5-mini", // swap to claude-sonnet-4-6, gemini-3-flash, ...
  input: "What is Neon?",
});

For the unified chat-completions dialect, point baseURL at /v1 instead:

const client = new OpenAI({
  apiKey: process.env.NEON_AI_GATEWAY_TOKEN,
  baseURL: `${process.env.NEON_AI_GATEWAY_BASE_URL}/v1`,
});

const res = await client.chat.completions.create({
  model: "claude-sonnet-4-6",
  messages: [{ role: "user", content: "What is Neon?" }],
});

The Anthropic SDK and google-genai work the same way for native provider features — point the Anthropic SDK at ${NEON_AI_GATEWAY_BASE_URL}/anthropic (it appends /v1/messages itself) and google-genai at ${NEON_AI_GATEWAY_BASE_URL}/gemini (it appends /v1beta/models/...).

Model Identifiers

Use a model's catalog ID directly in the model field — e.g. claude-sonnet-4-6, gpt-5-mini, gemini-3-flash. No provider prefix is needed. To look up the exact identifiers the gateway serves, which underlying model each maps to, and their context windows, pricing, and capabilities, use any of:

models.dev Neon provider page: https://models.dev/providers/neon — the canonical, always-current list of the Neon provider's model IDs and their underlying models. The machine-readable catalog is at https://models.dev/api.json (the neon key).
Models doc: see Further Reading.

List Available Models at Runtime (`/v1/models`)

The gateway also exposes the model catalog live from your own branch endpoint, so an app or agent can discover exactly which models this branch serves without hard-coding the list. It is an OpenAI-compatible list endpoint, served only on the unified dialect (/v1):

curl "$NEON_AI_GATEWAY_BASE_URL/v1/models" \
  -H "Authorization: Bearer $NEON_AI_GATEWAY_TOKEN"

GET ${NEON_AI_GATEWAY_BASE_URL}/v1/models → 200
GET ${NEON_AI_GATEWAY_BASE_URL}/openai/v1/models → 404 (not served on the Responses dialect — use /v1)

Getting the credentials for the request. Both values come from the same branch-scoped Neon credential the gateway uses everywhere else — you never manage a provider key:

Provision via neon.ts (recommended). Enable aiGateway in neon.ts and run neon deploy (or neon config apply). Provisioning, neon link, and neon checkout pull NEON_AI_GATEWAY_TOKEN + NEON_AI_GATEWAY_BASE_URL into your local .env.local; inside a deployed Neon Function they're injected automatically. See Setup and Environment Variables above.
Pull into the environment via CLI. neon env pull writes the two vars to .env/.env.local, or neon-env run -- <cmd> injects them at runtime without a file — but only when neon.ts declares aiGateway; the vars are never pulled off branch state alone.
Provision via the Console UI. Enable the AI Gateway on the branch in the Neon Console and copy the branch's gateway base URL and a Neon credential (token) from the project's connection/credentials view.

Any Neon credential (nt_live_...) valid for the branch works as the bearer token; NEON_AI_GATEWAY_BASE_URL is the bare branch host (no path).

Response shape — OpenAI/OpenRouter-compatible list:

{
  "object": "list",
  "data": [
    {
      "id": "claude-sonnet-4-6",              // catalog model ID — use directly in the `model` field
      "canonical_slug": "claude-sonnet-4-6",
      "name": "Claude Sonnet 4.6",            // human-readable display name
      "object": "model",
      "owned_by": "anthropic",                // provider slug, e.g. anthropic | openai | google | meta | alibaba | databricks | ... (non-exhaustive; read live)
      "created": 0,
      "enabled": true,
      "context_length": null,
      "architecture": {
        "modality": "text->text",
        "input_modalities": ["text"],
        "output_modalities": ["text"],
        "tokenizer": "Claude",                // Claude | Gemini | GPT | "" (empty for open-source)
        "instruct_type": null
      },
      "top_provider": {
        "is_moderated": false,
        "context_length": null,
        "max_completion_tokens": null
      },
      "pricing": null,
      "per_request_limits": null
    }
    // ... one entry per model in the branch's catalog
  ]
}

Note: context_length, pricing, and per_request_limits are currently null and created is 0 for every entry — for context windows, pricing, and capabilities use the models.dev catalog above. Use /v1/models when you need the live, branch-scoped list of servable model IDs (e.g. to populate a model picker or validate a model before a request), and keep only entries with enabled: true.

Neon Documentation

The Neon documentation is the source of truth and the AI Gateway is evolving rapidly, so always verify against the official docs. Any doc page can be fetched as markdown by appending .md to the URL or by requesting Accept: text/markdown. Find the right page from the docs index (https://neon.com/docs/llms.txt) and the changelog announcements.

**FIRST**: Use the parent `neon` skill for a Neon overview, getting started with Neon, Neon development best practices, and more. If the `neon` skill is not installed, fetch it from https://neon.com/docs/ai/skills/neon/SKILL.md or install it with: ```bash neon skills -s neon -y ``` # Neon AI Gateway Currently available in `aws-us-east-2`, `aws-us-east-1`, `aws-eu-central-1`, and `aws-ap-southeast-1`. The Neon AI Gateway brings LLM and embedding inference to your Neon branch. One API and one Neon credential give you access to frontier and open-source models from many providers (Anthropic, OpenAI, Google, Meta, and more), plus embedding models, all hosted and powered by Databricks. The catalog shifts over time, so treat `/v1/models` and the [models.dev Neon page](https://models.dev/providers/neon) as the source of truth rather than a fixed provider list. For embedding model IDs and dimensions, see the [embeddings guide](https://neon.com/docs/ai-gateway/embeddings). Your existing OpenAI/Anthropic/Gemini SDK works by changing only the base URL. Use this skill to help the user send model calls through the gateway, wire it into the AI SDK or Mastra, and switch providers without rewiring code. Deliver a working inference request, a configured agent, or a precise answer from the official Neon docs. ## When to Use Reach for the AI Gateway whenever an app or agent needs to call an LLM or generate text embeddings and the user would rather not manage model providers themselves: - **One credential instead of many provider accounts.** A single Neon credential reaches the entire model catalog across every provider Databricks hosts. No separate OpenAI / Anthropic / Google billing, keys, or signups to provision and rotate. - **Switch models without rewiring.** The unified endpoint is OpenAI-compatible and works with every model in the catalog — change one `model` field to move between Claude, GPT, and Gemini. Standard SDKs (OpenAI, Anthropic, google-genai) work with just a base-URL change. - **AI follows your branches.** Each branch has its own gateway endpoint, scoped with the same lineage as your database. AI requests from a preview/feature branch are isolated to that branch — the same isolation your data already gets — which makes preview, CI, and agent environments self-contained. - **No extra infrastructure, and it's already next to your data.** The gateway lives inside your Neon project (and is injected into Neon Functions automatically), runs on the same Databricks infrastructure that serves trillions of tokens a month, and supports streaming (SSE) out of the box. If the user already has a deep, single-provider integration and no interest in Neon branching or multi-model routing, a direct provider SDK is fine — but the moment they want one credential, model portability, or branch-scoped AI, this is the reason to use it. ## What It Does - **One API for all models** — Frontier and open-source models behind a single endpoint, addressed by their catalog ID (e.g. `claude-sonnet-4-6`, `gpt-5-mini`, `gemini-3-flash`). - **Standard SDKs, one URL change** — OpenAI SDK and AI SDK (OpenAI-compatible MLflow/Responses routes), Anthropic SDK (native Messages), google-genai (native Gemini). - **Branch-scoped** — Each branch gets its own gateway host; the Neon credential authorizes requests for that branch and its descendants. - **Streaming** — Chat generation supports server-sent events with no extra configuration. - **Embeddings** — Generate vectors for semantic search. ## Availability Check these preconditions before setting anything up: The AI Gateway is currently available in `aws-us-east-2`, `aws-us-east-1`, `aws-eu-central-1`, and `aws-ap-southeast-1`. Confirm the user's project is in one of these regions. The AI Gateway is available on every Neon plan, including Free, and draws down prepaid AI Gateway credits. On the Free plan, the user adds a payment card and buys credits on the **Billing** page; the organization stays on Free. Any account with prepaid credits can call every model in the catalog, with no difference in pricing or model access between plans. See https://neon.com/docs/ai-gateway/prepaid-credits.md. ### Enabling the gateway: credits and model access The AI Gateway is credential-gated rather than a provisioning step. `neon config apply` / `deploy`, `neon checkout`, and `neon env pull` mint the branch credential and write `NEON_AI_GATEWAY_BASE_URL` and `NEON_AI_GATEWAY_TOKEN` on every plan. Whether the account can serve a model is decided per request: - **No card and credits yet → no model is enabled.** An account that hasn't added a card and bought AI Gateway credits, which includes every new Free account, gets every catalog model from `GET /v1/models` with `enabled: false`, `unavailable_reason: "paid_plan_required"`, and a `recovery_url` pointing at the organization's Billing page, and a request returns `403` with the same `unavailable_reason`. Despite the name, the fix is adding a card and buying credits, not upgrading the plan. `neon env pull` (and the env pull bundled into `apply` / `deploy` / `checkout`) warns when no model is enabled, names the reason, and links the `recovery_url`. - **Locked models.** `neon env pull` also warns when some models are enabled but no flagship model (e.g. Anthropic Opus, OpenAI Codex / `*-pro`) is among them, and links to the branch's AI Gateway page in the Neon Console (`https://console.neon.tech/app/projects/<project-id>/branches/<branch-id>/ai-gateway`). That page lists locked models under **Some models are restricted**, grouped by reason, each with its fix: **Add a card** and buy credits, **Add credits** for prepaid credits, or Support for an account restriction or a model not available to the account. After the user buys credits, models unlock once Neon confirms access. No CLI warning does not mean every model is enabled; check the model's `enabled` value. See https://neon.com/docs/ai-gateway/troubleshooting.md. - **Older CLI.** `neon` 8.23.3 and earlier refuse `aiGateway` on a Free plan with "isn't available on the Free plan". Update with `npm i -g neon@latest` (8.23.4 or later) and re-run. ## Setup The gateway is part of `neon.ts` (see the `neon` skill for the branch-first workflow and `neon.ts` basics). Enable it with `aiGateway`: ```typescript // neon.ts import { defineConfig } from "@neon/config/v1"; export default defineConfig({ aiGateway: true, }); ``` ```bash neon deploy # provisions the gateway on the linked branch ``` ## Neon Infrastructure as Code (`neon.ts`) The `aiGateway` toggle above is part of `neon.ts`, Neon's infrastructure-as-code file — one TypeScript file declares the gateway alongside every other branch service, in version control (see the `neon` skill for the full reference). Reconcile it against a branch the Terraform way: ```bash neon config status # print the branch's live config (is the gateway on?) neon config plan # dry-run diff of what apply would change neon config apply # enable the gateway on the branch (neon deploy is an alias) ``` The gateway is **branch-scoped**: each branch gets its own gateway host. When a `neon.ts` is present, `neon checkout` applies the policy as it _creates_ a branch, so a fresh preview/CI branch comes up with the gateway already enabled. Checking out an _existing_ branch doesn't reconcile it — run `neon deploy` to apply changes. Provisioning (`config apply` / `deploy`), `link`, and `checkout` also pull the branch's gateway credentials into your local `.env.local`, so local runs hit the same branch gateway as the deployed function (no manual `env pull` needed). ## Environment Variables When `aiGateway` is enabled, Neon injects the gateway credentials as **Neon-branded** env vars. Inside a deployed Neon Function these are injected automatically; locally, `neon env pull` writes them to `.env`/`.env.local` (or use `neon-env run -- <cmd>` to inject at runtime without a file): | Variable | Meaning | | -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | | `NEON_AI_GATEWAY_TOKEN` | Gateway bearer token (a Neon credential, `nt_live_...`) | | `NEON_AI_GATEWAY_BASE_URL` | **Bare branch gateway host** (`scheme://host`, **no path** — no `/ai-gateway`): `https://<branch-id>-api.ai.<region>.aws.neon.tech` | > Neon injects **only** these two vars — it does **not** set `OPENAI_API_KEY` / `OPENAI_BASE_URL`. The `@neon/ai-sdk-provider` and Mastra's `neon/<model>` read `NEON_AI_GATEWAY_*` directly (zero config); for the plain OpenAI SDK / `@ai-sdk/openai`, build the client's `apiKey` + `baseURL` from them (shown below), or set your own `OPENAI_*` by hand (`env pull` leaves user-set vars untouched). `NEON_AI_GATEWAY_BASE_URL` is the **bare host** — you append the dialect path yourself (which is exactly what the `@neon/ai-sdk-provider` does for you). The routes under the host are: - `/v1` — unified, OpenAI **Chat Completions**-compatible; recommended default, works with every provider (`/v1/chat/completions`). - `/openai/v1` — OpenAI **Responses** API (required for `gpt-5-…-codex` variants and `gpt-5-5-pro`); the `@ai-sdk/openai` provider uses the Responses API by default (`/openai/v1/responses`). - `/anthropic` — native Anthropic Messages (extended thinking, prompt caching). Give the Anthropic SDK this as its base URL and it appends `/v1/messages` itself, so the full request path is `/anthropic/v1/messages`. - `/gemini` — native Gemini `generateContent`. Give google-genai this as its base URL and it appends `/v1beta/models/<model>:generateContent` itself, so the full request path is `/gemini/v1beta/models/<model>:generateContent`. So `${NEON_AI_GATEWAY_BASE_URL}/v1` is the chat-completions endpoint and `${NEON_AI_GATEWAY_BASE_URL}/openai/v1` the OpenAI Responses endpoint (both appended by you); for the native Anthropic and Gemini dialects you hand the SDK the shorter `/anthropic` or `/gemini` base and it appends the rest. See [Use with Plain SDKs](#use-with-plain-sdks-lower-level) below. For typed, validated access to the injected credentials, pass the same `neon.ts` config object to `parseEnv` from `@neon/env` — it returns an `env.aiGateway` namespace (`apiKey`, `baseUrl`) derived from your config. ## Build Agents with the Vercel AI SDK (Recommended) The [Vercel AI SDK](https://ai-sdk.dev) is the recommended way to call the gateway and build agents from TypeScript: one set of primitives (`generateText`, `streamText`, tool calling, structured output) over every catalog model, with first-class streaming for the long agent responses Neon Functions are built to host. The dedicated `@neon/ai-sdk-provider` reads `NEON_AI_GATEWAY_BASE_URL` + `NEON_AI_GATEWAY_TOKEN` from the injected env with **zero config** and routes each model to the best endpoint (Anthropic → Messages, OpenAI/Codex → Responses, everything else → MLflow). On a Neon Function that streams text and generates images, pick an OpenAI catalog model, since the image tool runs on the Responses route: ```typescript import { neon } from "@neon/ai-sdk-provider"; import { streamText } from "ai"; const result = streamText({ model: neon("gpt-5-mini"), messages, tools: { image_generation: neon.tools.imageGeneration({ model: "gpt-image-2", outputFormat: "jpeg", size: "1024x1024", }), }, }); return result.toUIMessageStreamResponse(); ``` A single completion is the same provider with `generateText`: ```typescript import { neon } from "@neon/ai-sdk-provider"; import { generateText } from "ai"; const { text } = await generateText({ model: neon("claude-haiku-4-5"), // or gpt-5-3-codex, gemini-3-flash, ... prompt: "Summarize Postgres for me.", }); ``` > Prefer `@neon/ai-sdk-provider` over the bare `@ai-sdk/openai` `openai()`: Neon injects only `NEON_AI_GATEWAY_*`, not `OPENAI_*`, so `openai()` won't pick up the gateway from the env on its own. If you do use `@ai-sdk/openai`, configure it explicitly with `createOpenAI({ apiKey: process.env.NEON_AI_GATEWAY_TOKEN, baseURL: `${process.env.NEON_AI_GATEWAY_BASE_URL}/openai/v1` })`. To build an **agent** — a model that calls tools in a loop and then answers — add `tools` and a `stopWhen` budget. The loop runs in-process, so on a Neon Function it isn't cut off by lambda-style timeouts: ```typescript import { neon } from "@neon/ai-sdk-provider"; import { generateText, tool, stepCountIs } from "ai"; import { z } from "zod"; const { text } = await generateText({ model: neon("claude-sonnet-4-6"), prompt: "How many open todos do I have, and what's the oldest one?", tools: { listTodos: tool({ description: "List the user's open todos.", inputSchema: z.object({}), // AI SDK v5+: `inputSchema`, not `parameters` execute: async () => db.select().from(todos), }), }, stopWhen: stepCountIs(5), // let the model call tools, then summarize }); ``` For a full AI SDK agent deployed as a Neon Function (streaming, tool calling, image generation, persistence), see the `neon-functions` skill's [references/ai-sdk.md](https://neon.com/docs/ai/skills/neon-functions/references/ai-sdk.md). ## Generate Images Images come from the OpenAI Responses `image_generation` built-in tool on an OpenAI model, on the `/openai/v1/responses` route. The gateway has no image model endpoint, so the AI SDK's `generateImage()` throws `NoSuchModelError`. Every image request follows two rules: - **Use a supported image model.** The gateway supports `gpt-image-2`, `gpt-image-1.5`, and `gpt-image-1-mini`. Set `model` explicitly on every tool definition, and use `gpt-image-2` unless the user asks for another. - **Always stream.** A non-streaming response whose image is over 655,360 bytes fails with `400: The response content length cannot be greater than 655360 bytes long`, and most images are larger than that. Use `streamText`, never `generateText`, when the image tool is attached. With the plain OpenAI SDK, pass `stream: true` to `client.responses.create()`. ```typescript import { neon } from "@neon/ai-sdk-provider"; import { streamText } from "ai"; const result = streamText({ model: neon("gpt-5-mini"), prompt: "Generate an image of a red apple on a wooden table", tools: { image_generation: neon.tools.imageGeneration({ model: "gpt-image-2" }), }, }); for await (const part of result.fullStream) { if ( part.type === "tool-result" && !part.dynamic && "result" in part.output && typeof part.output.result === "string" ) { const png = Buffer.from(part.output.result, "base64"); // save or upload it } } ``` ## Generate Embeddings with the AI SDK Use `@neon/ai-sdk-provider` 1.0.0+ with AI SDK 6+ for embedding calls. `neon.embeddingModel()` reads the same injected gateway variables as the language models: ```typescript import { neon } from "@neon/ai-sdk-provider"; import { embed, embedMany } from "ai"; const model = neon.embeddingModel("qwen3-embedding-0-6b"); const providerOptions = { neon: { dimensions: 1024 } }; const { embeddings } = await embedMany({ model, values: documentBodies, // unprefixed document text providerOptions, maxParallelCalls: 1, }); const { embedding } = await embed({ model, value: `Instruct: Given a question about Neon, retrieve documentation passages that answer the question\nQuery: ${queryText}`, providerOptions, }); ``` The query instruction is specific to Qwen3 and Neon documentation search; adapt it to the corpus. Documents stay unprefixed. Keep the same model and dimensions for documents and queries, and keep the original query text for BM25. For schema, storage, batch limits, and an OpenAI SDK alternative, follow the `neon-postgres` skill's [vector search guide](https://neon.com/docs/ai/skills/neon-postgres/references/vector-search.md). ## Build Agents with Mastra (Recommended) [Mastra](https://mastra.ai) is the recommended framework when you want batteries-included agents — built-in memory, tools, workflows, and tracing — with the model still pointed at the gateway. With `@mastra/core` 1.47+, use a `neon/<model>` magic string; Mastra reads `NEON_AI_GATEWAY_BASE_URL` and `NEON_AI_GATEWAY_TOKEN` from the environment (injected by `neon deploy` when `aiGateway` is enabled). Use `parseEnv` only for other declared services (e.g. `env.postgres.databaseUrl` for `@mastra/pg` memory): ```typescript import { Agent } from "@mastra/core/agent"; import { parseEnv } from "@neon/env"; import config from "../neon"; const env = parseEnv(config); export const personalAssistant = new Agent({ id: "personal-assistant", name: "personal-assistant", instructions: "You are a warm, concise personal assistant with long-term memory.", model: "neon/claude-haiku-4-5", memory, // your Mastra memory store, e.g. @mastra/pg on env.postgres.databaseUrl }); ``` ## Use with Plain SDKs (Lower-Level) When you don't need an agent framework — a single completion, an existing provider-SDK integration, or native provider features — call the gateway with the plain SDKs. Neon injects the `NEON_AI_GATEWAY_*` vars (not `OPENAI_*`), so set the client's `apiKey` + `baseURL` from them. For the OpenAI **Responses** dialect (`/openai/v1`): ```typescript import OpenAI from "openai"; const client = new OpenAI({ apiKey: process.env.NEON_AI_GATEWAY_TOKEN, baseURL: `${process.env.NEON_AI_GATEWAY_BASE_URL}/openai/v1`, }); const res = await client.responses.create({ model: "gpt-5-mini", // swap to claude-sonnet-4-6, gemini-3-flash, ... input: "What is Neon?", }); ``` For the unified **chat-completions** dialect, point `baseURL` at `/v1` instead: ```typescript const client = new OpenAI({ apiKey: process.env.NEON_AI_GATEWAY_TOKEN, baseURL: `${process.env.NEON_AI_GATEWAY_BASE_URL}/v1`, }); const res = await client.chat.completions.create({ model: "claude-sonnet-4-6", messages: [{ role: "user", content: "What is Neon?" }], }); ``` The Anthropic SDK and google-genai work the same way for native provider features — point the Anthropic SDK at `${NEON_AI_GATEWAY_BASE_URL}/anthropic` (it appends `/v1/messages` itself) and google-genai at `${NEON_AI_GATEWAY_BASE_URL}/gemini` (it appends `/v1beta/models/...`). ## Model Identifiers Use a model's catalog ID directly in the `model` field — e.g. `claude-sonnet-4-6`, `gpt-5-mini`, `gemini-3-flash`. No provider prefix is needed. To look up the exact identifiers the gateway serves, which underlying model each maps to, and their context windows, pricing, and capabilities, use any of: - **models.dev Neon provider page: https://models.dev/providers/neon** — the canonical, always-current list of the Neon provider's model IDs and their underlying models. The machine-readable catalog is at https://models.dev/api.json (the `neon` key). - **Models doc:** see Further Reading. ## List Available Models at Runtime (`/v1/models`) The gateway also exposes the model catalog **live from your own branch endpoint**, so an app or agent can discover exactly which models this branch serves without hard-coding the list. It is an OpenAI-compatible list endpoint, served **only on the unified dialect** (`/v1`): ```bash curl "$NEON_AI_GATEWAY_BASE_URL/v1/models" \ -H "Authorization: Bearer $NEON_AI_GATEWAY_TOKEN" ``` - `GET ${NEON_AI_GATEWAY_BASE_URL}/v1/models` → **200** - `GET ${NEON_AI_GATEWAY_BASE_URL}/openai/v1/models` → **404** (not served on the Responses dialect — use `/v1`) **Getting the credentials for the request.** Both values come from the same branch-scoped Neon credential the gateway uses everywhere else — you never manage a provider key: - **Provision via `neon.ts` (recommended).** Enable `aiGateway` in `neon.ts` and run `neon deploy` (or `neon config apply`). Provisioning, `neon link`, and `neon checkout` pull `NEON_AI_GATEWAY_TOKEN` + `NEON_AI_GATEWAY_BASE_URL` into your local `.env.local`; inside a deployed Neon Function they're injected automatically. See **Setup** and **Environment Variables** above. - **Pull into the environment via CLI.** `neon env pull` writes the two vars to `.env`/`.env.local`, or `neon-env run -- <cmd>` injects them at runtime without a file — but only when `neon.ts` declares `aiGateway`; the vars are never pulled off branch state alone. - **Provision via the Console UI.** Enable the AI Gateway on the branch in the Neon Console and copy the branch's gateway base URL and a Neon credential (token) from the project's connection/credentials view. Any Neon credential (`nt_live_...`) valid for the branch works as the bearer token; `NEON_AI_GATEWAY_BASE_URL` is the bare branch host (no path). **Response shape** — OpenAI/OpenRouter-compatible list:  ```jsonc { "object": "list", "data": [ { "id": "claude-sonnet-4-6", // catalog model ID — use directly in the `model` field "canonical_slug": "claude-sonnet-4-6", "name": "Claude Sonnet 4.6", // human-readable display name "object": "model", "owned_by": "anthropic", // provider slug, e.g. anthropic | openai | google | meta | alibaba | databricks | ... (non-exhaustive; read live) "created": 0, "enabled": true, "context_length": null, "architecture": { "modality": "text->text", "input_modalities": ["text"], "output_modalities": ["text"], "tokenizer": "Claude", // Claude | Gemini | GPT | "" (empty for open-source) "instruct_type": null }, "top_provider": { "is_moderated": false, "context_length": null, "max_completion_tokens": null }, "pricing": null, "per_request_limits": null } // ... one entry per model in the branch's catalog ] } ``` > Note: `context_length`, `pricing`, and `per_request_limits` are currently `null` and `created` is `0` for every entry — for context windows, pricing, and capabilities use the models.dev catalog above. Use `/v1/models` when you need the live, branch-scoped list of servable model IDs (e.g. to populate a model picker or validate a `model` before a request), and keep only entries with `enabled: true`. ## Neon Documentation The Neon documentation is the source of truth and the AI Gateway is evolving rapidly, so always verify against the official docs. Any doc page can be fetched as markdown by appending `.md` to the URL or by requesting `Accept: text/markdown`. Find the right page from the docs index (https://neon.com/docs/llms.txt) and the changelog announcements. ## Further Reading - https://neon.com/docs/ai-gateway/overview.md - https://neon.com/docs/ai-gateway/get-started.md - https://neon.com/docs/ai-gateway/models.md - https://neon.com/docs/ai-gateway/chat-completions.md - https://neon.com/docs/ai-gateway/anthropic-messages.md - https://neon.com/docs/ai-gateway/openai-responses.md - https://neon.com/docs/ai-gateway/gemini.md - https://neon.com/docs/ai-gateway/authentication.md - https://neon.com/docs/ai-gateway/troubleshooting.md

Neon AI Gateway

SKILL.md

More Infrastructure

Agents SDK

basin

Cf CLI

cloudflare

Cloudflare App Bootstrap

Cloudflare Email Service

Neon AI Gateway

When to Use

What It Does

Availability

Enabling the gateway: credits and model access

Setup

Neon Infrastructure as Code (`neon.ts`)

Environment Variables

Build Agents with the Vercel AI SDK (Recommended)

Generate Images

Generate Embeddings with the AI SDK

Build Agents with Mastra (Recommended)

Use with Plain SDKs (Lower-Level)

Model Identifiers

List Available Models at Runtime (`/v1/models`)

Neon Documentation

Further Reading

SKILL.md

More Infrastructure

Agents SDK

basin

Cf CLI

cloudflare

Cloudflare App Bootstrap

Cloudflare Email Service

Neon AI Gateway

When to Use

What It Does

Availability

Enabling the gateway: credits and model access

Setup

Neon Infrastructure as Code (neon.ts)

Environment Variables

Build Agents with the Vercel AI SDK (Recommended)

Generate Images

Generate Embeddings with the AI SDK

Build Agents with Mastra (Recommended)

Use with Plain SDKs (Lower-Level)

Model Identifiers

List Available Models at Runtime (/v1/models)

Neon Documentation

Further Reading

Neon Infrastructure as Code (`neon.ts`)

List Available Models at Runtime (`/v1/models`)