One API and one credential for frontier and open-source LLMs, built into your Neon branch and powered by Databricks. Use when a user wants to call an LLM, add AI/chat/an agent to their app, route between model providers (OpenAI, Anthropic, Google/Gemini, Meta, Alibaba, DeepSeek), or...
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One API and one credential for frontier and open-source LLMs, built into your Neon branch and powered by Databricks. Use when a user wants to call an LLM, add AI/chat/an agent to their app, route between model providers (OpenAI, Anthropic, Google/Gemini, Meta, Alibaba, DeepSeek), or...
This is a preview feature and only available in us-east-2. The Neon AI Gateway is the LLM inference layer built into your Neon branch: one API and one Neon credential give you access to frontier and open-source models from Anthropic, OpenAI, Google, Meta, Alibaba, DeepSeek, and Databricks — powered by Databricks. 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 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 seven providers. 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-2-5-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 — Server-sent events work on all endpoints with no extra configuration.
Setup
The gateway is part of neon.ts (see the neon skill for the branch-first workflow and neon.ts basics). Enable it under preview.aiGateway:
neon deploy # provisions the gateway on the linked branch
Neon Infrastructure as Code (neon.ts)
The preview.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).
For typed, validated access to the injected credentials, pass the same config object to parseEnv from @neon/env — it returns an env.aiGateway namespace (apiKey, baseUrl) derived from your neon.ts.
Environment variables
When preview.aiGateway is enabled, Neon injects the gateway credentials as OpenAI-standard env vars (so the OpenAI SDK and AI SDK work from the environment with no config), plus NEON_-branded aliases. 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
OPENAI_API_KEY
Gateway bearer token (a Neon credential, nt_live_...)
OPENAI_BASE_URL
Full OpenAI-dialect route, including/ai-gateway/openai/v1: https://<branch-id>-api.ai.<region>.aws.neon.tech/ai-gateway/openai/v1
NEON_AI_GATEWAY_TOKEN
Same bearer as OPENAI_API_KEY (survives a user overriding OPENAI_* with their own keys)
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
The two base URLs are different: OPENAI_BASE_URL already includes the full /ai-gateway/openai/v1 (Responses) route, while NEON_AI_GATEWAY_BASE_URL is just the bare host, so you append /ai-gateway/<dialect> yourself (this is also what the @neon/ai-sdk-provider does for you). The routes under the host are:
/ai-gateway/mlflow/v1 — unified, OpenAI Chat Completions-compatible; recommended default, works with every provider.
/ai-gateway/openai/v1 — OpenAI Responses API (required for gpt-5-…-codex variants and gpt-5-5-pro). This is the route OPENAI_BASE_URL already points at, because the @ai-sdk/openai provider uses the Responses API by default.
So ${NEON_AI_GATEWAY_BASE_URL}/ai-gateway/mlflow/v1 is the chat-completions endpoint, ${NEON_AI_GATEWAY_BASE_URL}/ai-gateway/openai/v1 equals OPENAI_BASE_URL, and so on. If you only have OPENAI_BASE_URL and need chat completions, swap the dialect: baseUrl.replace("/openai/v1", "/mlflow/v1") (this is what the Mastra example does).
For typed access, parseEnv (from @neon/env) returns env.aiGateway (apiKey, baseUrl) derived from your neon.ts.
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.
On a Neon Function that streams text and generates images, the @ai-sdk/openai provider reads OPENAI_API_KEY and OPENAI_BASE_URL from the injected env automatically — no client config needed; just pick a catalog model:
For multi-provider routing from a single call, the dedicated @neon/ai-sdk-provider reads NEON_AI_GATEWAY_BASE_URL + NEON_AI_GATEWAY_TOKEN and routes each model to the best endpoint (Anthropic → Messages, OpenAI/Codex → Responses, everything else → MLflow):
import { neon } from"@neon/ai-sdk-provider";
import { generateText } from"ai";
const { text } = awaitgenerateText({
model: neon("claude-haiku-4-5"), // or gpt-5-3-codex, gemini-2-5-flash, ...prompt: "Summarize Postgres for me.",
});
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 } = awaitgenerateText({
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.
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. A memory-backed agent (threads/messages in Postgres via @mastra/pg) running as a Neon Function reads env.aiGateway from parseEnv and uses the chat-completions (MLflow) dialect:
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. The injected OPENAI_API_KEY and OPENAI_BASE_URL are OpenAI-standard, so new OpenAI() picks them up with zero config. Since OPENAI_BASE_URL is the OpenAI Responses dialect (/openai/v1), call the Responses API:
importOpenAIfrom"openai";
const client = newOpenAI(); // reads OPENAI_API_KEY + OPENAI_BASE_URL from the envconst res = await client.responses.create({
model: "gpt-5-mini", // swap to claude-sonnet-4-6, gemini-2-5-flash, ...input: "What is Neon?",
});
For the unified chat-completions dialect (/mlflow/v1) instead, point the client at it. The ergonomic way is to swap the dialect on the injected base URL rather than rebuild it (same move the Mastra example makes):
The Anthropic SDK and google-genai work the same way for native provider features — point them at the /anthropic and /gemini routes on the bare gateway host (${NEON_AI_GATEWAY_BASE_URL}/ai-gateway/anthropic, ${NEON_AI_GATEWAY_BASE_URL}/ai-gateway/gemini).
Model identifiers
Use a model's catalog ID directly in the model field — e.g. claude-sonnet-4-6, gpt-5-mini, gemini-2-5-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.
Availability
The AI Gateway is a preview (early access) feature available only on new projects in the us-east-2 region; it can't be enabled on existing projects. Foundation model access requires a paid Neon plan. Confirm the user's project is a new project in us-east-2. If the user does not yet have access, point them to the private beta sign-up: https://neon.com/blog/were-building-backends#access
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.
