How to Deploy AI Models Using Vercel AI SDK

In this blog post, we will explore how to deploy AI models using the Vercel AI SDK, which allows developers to leverage powerful models directly in their Next.js applications. With Vercel's seamless integration, you can manage everything from model hosting to serverless functions, making it easier than ever to incorporate AI capabilities into your app.

Before You Get Started

To follow along with this guide, make sure you have a Next.js project already set up. If you don't, create one using the following command:

npx create-next-app@latest

You'll also need a Vercel account and a model that you want to deploy. Vercel AI SDK supports various models such as OpenAI's GPT and other custom models hosted on Vercel's platform.

Step 1: Install the Vercel AI SDK

Start by installing the Vercel AI SDK into your Next.js project. You can do this using npm or yarn:

npm install @vercel/ai

yarn add @vercel/ai

This SDK will allow you to interact with AI models through API routes in your Next.js application.

Step 2: Set Up Environment Variables

After installing the SDK, you'll need to set up environment variables to securely store your API keys and credentials. Create a .env.local file in the root of your project and add the following:

VERCEL_AI_API_KEY=your_api_key_here

Make sure to replace your_api_key_here with the actual API key for your model.

Step 3: Create an API Route for Inference

Next, you'll create an API route in Next.js to handle model inference. This API route will receive the input from the user, send it to the model for processing, and return the output.

Create a new file pages/api/inference.js:

import { VercelAI } from "@vercel/ai";

export default async function handler(req, res) {
  if (req.method === "POST") {
    const { input } = req.body;

    // Create an instance of VercelAI
    const ai = new VercelAI(process.env.VERCEL_AI_API_KEY);

    try {
      const result = await ai.infer({
        model: "openai-gpt",
        prompt: input,
      });

      res.status(200).json({ result });
    } catch (error) {
      res.status(500).json({ error: "Inference failed", details: error.message });
    }
  } else {
    res.setHeader("Allow", ["POST"]);
    res.status(405).end(`Method ${req.method} Not Allowed`);
  }
}

In this code, we create a new API route that handles a POST request. It uses the VercelAI class to interact with the model and return the result of the inference.

Step 4: Create a Frontend Form

Now, we need a form to take input from the user and send it to the API route for processing.

Create a component components/InferenceForm.js:

import { useState } from "react";

export default function InferenceForm() {
  const [input, setInput] = useState("");
  const [result, setResult] = useState(null);

  const handleSubmit = async (e) => {
    e.preventDefault();

    const res = await fetch("/api/inference", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ input }),
    });

    const data = await res.json();
    setResult(data.result);
  };

  return (
    <div>
      <form onSubmit={handleSubmit}>
        <input
          type="text"
          value={input}
          onChange={(e)=> setInput(e.target.value)}
          placeholder="Enter your prompt"
        />
        <button type="submit">Submit</button>
      </form>
      {result && <div>Result: {result}</div>}
    </div>
  );
}

This simple form captures the user input and sends it to the /api/inference endpoint we created earlier. When the response comes back, it displays the result on the page.

Optimizing Performance

When deploying AI models, performance is key. Vercel’s AI SDK is built for speed and efficiency, but you can further optimize your setup by following these best practices:

1. Use `getServerSideProps` or API Routes for Inference

If your model inference requires real-time data processing, use getServerSideProps in your Next.js pages or API routes to ensure the inference happens on the server side. This avoids exposing sensitive API keys and ensures a faster response time for the end user.

export async function getServerSideProps(context) {
  const response = await fetch('https://your-vercel-api/inference', {
    method: 'POST',
    body: JSON.stringify({ input: context.params.input }),
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${process.env.VERCEL_AI_API_KEY}`,
    },
  });

  const data = await response.json();

  return { props: { result: data.result } };
}

2. Cache Results

If your model produces outputs that can be reused, consider caching the results of the inference. This reduces repeated calls to the API and improves the overall speed. You can use Vercel’s in-built edge caching or external caching solutions like Redis.

3. Handle Large Inputs Efficiently

For models that deal with large inputs (like images or documents), ensure the data is compressed before sending it to the API for inference. This reduces the amount of data transferred and speeds up the process.

const compressImage = (file) => {
  // logic for compressing the image
};

Conclusion

By using the Vercel AI SDK, you can easily deploy AI models in your Next.js applications and take advantage of Vercel's powerful serverless infrastructure. Whether you're building a chatbot, an image recognizer, or any other AI-powered feature, this SDK simplifies the process of integrating AI into your apps.