Use Vercel AI SDK with SaladCloud

Learn how to build AI-powered JavaScript and Next.js applications using the Vercel AI SDK with a self-hosted model on SaladCloud.

Last Updated: May 18, 2026

Introduction

The Vercel AI SDK is a popular framework for building AI-powered applications in JavaScript and TypeScript. It provides a unified interface for text generation, streaming, structured output, and tool calling - and includes an @ai-sdk/openai-compatible package for connecting to any OpenAI-compatible endpoint, including SaladCloud.

The Vercel AI SDK works with SaladCloud in two ways:

• Salad AI Gateway - no infrastructure to deploy or manage. Sign up for access, point your app at a single shared endpoint, and use your Salad API key directly. Currently in closed beta with monthly flat-rate access.
• Self-hosted model - deploy your own SaladCloud container group for full control over the model, hardware, and configuration. Still very easy to set up and use.

Prerequisites

Before getting started, make sure you have:

• A SaladCloud account
• Node.js 18+ installed

Step-by-Step Setup

Step 1: Choose Your Backend

Salad AI Gateway

Salad AI Gateway is the fastest way to get started - no container groups to deploy, no cold starts to wait for.

1. Sign up for early access at salad.com/ai-gateway.
2. Once approved, find your Salad API key in the portal.

Available models:

Model	Description
qwen3.6-35b-a3b	Qwen 3.6 35B-A3B - best for agentic tasks, coding, and complex reasoning
qwen3.6-27b	Qwen 3.6 27B - strong balance of capability and speed
qwen3.5-9b	Qwen 3.5 9B - fastest response times, suited for lighter tasks

Self-Hosted on SaladCloud

First, deploy an OpenAI-compatible LLM server on SaladCloud.

• Go to the SaladCloud portal and create an account if you do not already have one.
• Create an organization or choose an existing one, then click “Deploy a container group”.
• Select an LLM recipe. The Qwen3.6-35B-A3B (llama.cpp) recipe is a strong choice for general-purpose app development. On the recipe page, provide a name and deploy - the rest is preconfigured with recommended settings.
• Once deployed, your endpoint will be live and serving an OpenAI-compatible API.

Available recipes:

Ready-to-deploy recipes (best for less technical users):

• qwen3.6-35B-A3B - A powerful Mixture of Experts model optimized for instruction-following tasks.

Recipes for custom deployments (best for advanced users):

• llama.cpp - Supports GGUF models
• sglang - High-performance inference
• vllm - Popular LLM serving framework
• ollama - Simple model management
• tgi - Hugging Face Text Generation Inference server

After deployment, note your API endpoint URL (e.g., https://your-endpoint.salad.cloud).

Step 2: Install the Dependencies

In your project directory, install the Vercel AI SDK and the OpenAI-compatible provider:

npm install ai @ai-sdk/openai-compatible

The Vercel AI SDK uses ES modules. Make sure your package.json has "type": "module" set:

npm pkg set type=module

To use environment variables for your API key and endpoint, also install dotenv:

npm install dotenv

Step 3: Configure the SaladCloud Provider

Salad AI Gateway

Store your credentials in a .env file:

SALAD_API_KEY=your-salad-api-key

Create a provider instance pointing to the AI Gateway endpoint:

import { createOpenAICompatible } from '@ai-sdk/openai-compatible'
import dotenv from 'dotenv'
dotenv.config()

const saladcloud = createOpenAICompatible({
  name: 'saladcloud',
  baseURL: 'https://ai.salad.cloud/v1',
  apiKey: process.env.SALAD_API_KEY,
})

No custom headers are needed - your Salad API key in apiKey is all that’s required.

Self-Hosted on SaladCloud

Create a provider instance pointing to your SaladCloud endpoint. If you set SaladCloud to authenticate, you need to do that via the Salad-Api-Key request header - pass it through the headers option rather than apiKey:

import { createOpenAICompatible } from '@ai-sdk/openai-compatible'
import dotenv from 'dotenv'
dotenv.config()

const saladcloud = createOpenAICompatible({
  name: 'saladcloud',
  baseURL: process.env.SALAD_BASE_URL,
  headers: {
    'Salad-Api-Key': process.env.SALAD_API_KEY,
  },
})

Store your credentials in a .env file (never hardcode secrets):

SALAD_API_KEY=your-salad-api-key
SALAD_BASE_URL=https://your-endpoint.salad.cloud/v1

Step 4: Generate Text and Test the Connection

Use generateText to test that your setup works:

import { createOpenAICompatible } from '@ai-sdk/openai-compatible'
import { generateText } from 'ai'
import dotenv from 'dotenv'
dotenv.config()

const saladcloud = createOpenAICompatible({
  name: 'saladcloud',
  baseURL: process.env.SALAD_BASE_URL,
  headers: {
    'Salad-Api-Key': process.env.SALAD_API_KEY,
  },
})

const { text } = await generateText({
  model: saladcloud('qwen3.6-35b-a3b'),
  prompt: 'Explain distributed GPU computing in one paragraph.',
})

console.log(text)

Run it with:

node test.js

If you see a response, your setup is complete.

Streaming Chat Responses

Use streamText for chat interfaces that display responses as they are generated:

import { createOpenAICompatible } from '@ai-sdk/openai-compatible'
import { streamText } from 'ai'
import dotenv from 'dotenv'
dotenv.config()
const saladcloud = createOpenAICompatible({
  name: 'saladcloud',
  baseURL: process.env.SALAD_BASE_URL,
  headers: {
    'Salad-Api-Key': process.env.SALAD_API_KEY,
  },
})

const result = streamText({
  model: saladcloud('qwen3.6-35b-a3b'),
  messages: [
    { role: 'system', content: 'You are a helpful coding assistant.' },
    { role: 'user', content: 'How do I read a file in Python?' },
  ],
})

for await (const chunk of result.textStream) {
  process.stdout.write(chunk)
}