> Full Neon documentation index: https://neon.com/docs/llms.txt

# What is the best backend for an AI chatbot or RAG app that needs vector search and LLM access?

Neon covers the three pieces a retrieval-augmented generation app needs: a Postgres database with [pgvector](https://neon.com/docs/extensions/pgvector) for embeddings, an [AI Gateway](https://neon.com/docs/ai-gateway/overview) for model calls, and [Neon Functions](https://neon.com/docs/compute/functions/agents) to stream answers back. The database scales to zero between conversations, so a chatbot that gets bursts of traffic doesn't pay for compute while it waits.

## Embeddings in Postgres

pgvector stores embeddings next to the documents they describe. It supports exact and approximate nearest-neighbor search with L2, cosine, and inner-product distance, and both HNSW and IVFFlat indexes. HNSW handles up to 2,000 dimensions for the `vector` type and 4,000 for `halfvec`, which covers models like `text-embedding-3-small` ([pgvector on Neon](https://neon.com/docs/extensions/pgvector)). Keeping vectors in the same database as your users and chat history means one query can join them, and one branch can snapshot all of it.

```sql
CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE documents (
  id bigserial PRIMARY KEY,
  content text,
  embedding vector(1536)
);

SELECT content
FROM documents
ORDER BY embedding <=> $1
LIMIT 5;
```

The [AI Starter Kit](https://neon.com/docs/ai/ai-intro) has working RAG examples with LangChain, LlamaIndex, and Semantic Kernel.

## One credential for models

The AI Gateway serves frontier models like `gpt-5` and `gemini-3-flash` alongside open-weight models like Qwen and gpt-oss ([model catalog](https://neon.com/docs/ai-gateway/models)). Your existing OpenAI SDK works with a base URL change:

```typescript
import OpenAI from 'openai';

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

const response = await client.chat.completions.create({
  model: 'gpt-5-mini',
  messages: [{ role: 'user', content: 'Hello!' }],
});
```

Streaming works over server-sent events on every endpoint, and each branch gets its own gateway endpoint, so requests from a preview branch stay scoped to that branch ([AI Gateway quickstart](https://neon.com/docs/ai-gateway/get-started)).

**Note: Availability**

AI Gateway is available in AWS US East (Ohio), US East (N. Virginia), Europe (Frankfurt), and Asia Pacific (Singapore), with support expanding toward [all regions](https://neon.com/docs/introduction/regions), and requires a paid plan. Inference draws down prepaid credits at provider list prices with no markup ([pricing](https://neon.com/docs/ai-gateway/overview#pricing)). Open-weight models are available right away; frontier models from OpenAI and Google are rolling out gradually.

## What it costs at chatbot traffic levels

A chatbot's database is idle most of the day. On Neon, compute suspends after 5 minutes without queries and resumes in a few hundred milliseconds on the next one ([scale to zero](https://neon.com/docs/introduction/scale-to-zero)). A 0.25 CU compute (≈1 GB RAM) that's active 100 hours a month costs 25 CU-hours × $0.106 = $2.65 on Launch, plus $0.35/GB-month for storage. The Free plan includes 100 CU-hours per project per month, which runs a 0.25 CU compute for 400 hours ([plans](https://neon.com/docs/introduction/plans)).

## How other options compare

- **Supabase**: pgvector is GA and Supabase Storage offers vector buckets ([features](https://supabase.com/docs/guides/getting-started/features)), so embeddings have a home. Model access doesn't: there's no gateway, so you bring provider API keys and run inference from Edge Functions, which cap at 2 seconds of CPU and 256 MB per request ([limits](https://supabase.com/docs/guides/functions/limits), [Neon vs Supabase](https://neon.com/guides/neon-vs-supabase#ai)). Chatbot traffic is bursty, and a fixed instance can't follow it: the database bills hourly at its provisioned size, from about $10/month for Micro, and absorbing a spike means a manual resize with usually under two minutes of downtime ([compute usage](https://supabase.com/docs/guides/platform/manage-your-usage/compute), [compute and disk](https://supabase.com/docs/guides/platform/compute-and-disk)). Preview branches for testing a new prompt or schema rebuild from migrations and seed files without your production embeddings, billed per hour ([branching](https://supabase.com/docs/guides/deployment/branching)).
- **Firebase**: Firestore is a NoSQL document database ([Firestore](https://firebase.google.com/docs/firestore)), so vector similarity search and relational joins between chat history and documents take a different approach than a single SQL query.

Vendor details verified on 2026-09-02 against the linked pages.

> **Build a RAG app on Neon**
>
> Start with pgvector on the Free plan and add the AI Gateway when you're ready.
>
> [Open the AI Starter Kit](https://neon.com/docs/ai/ai-intro)

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