Building Cross-Framework AI Dashboards Using Next.js 14, Svelte, and Edge Computing for Low-Latency Applications

In today’s era of data-driven decision-making, AI dashboards have become indispensable for visualizing and interacting with machine learning models. However, building these dashboards for low-latency applications introduces unique challenges, particularly when optimizing for speed and scalability. Leveraging modern frameworks like Next.js, Svelte, and edge computing technologies can provide the perfect trifecta for achieving high-performance AI dashboards.

This blog post will explore how you can combine these tools to build cross-framework AI dashboards that deliver low-latency performance and seamless user experiences.

Next.js is a React-based framework that is perfect for server-side rendering (SSR) and static site generation (SSG). Version 14 introduces enhanced middleware and edge computing integrations, making it ideal for real-time AI dashboard applications. It also supports full-stack development, allowing you to handle backend API routes and frontend rendering in one framework.

Svelte is a revolutionary frontend framework that compiles your code to highly efficient vanilla JavaScript during build time. Unlike React, Svelte does not use a virtual DOM, making it faster and better suited for performance-critical applications.

Edge computing enables data processing closer to the user, minimizing latency caused by server requests. By deploying AI computations or data preprocessing at edge locations, you can achieve near-instantaneous response times for your dashboards.

The architecture of a cross-framework AI dashboard powered by Next.js, Svelte, and edge computing involves three main components:

1. **Frontend:** Svelte components handle dynamic and interactive UI elements. 2. **Middle Layer:** Next.js manages routes, SSR, and API integration. 3. **Edge Computing:** AI models and data preprocessing are deployed at edge locations using platforms like AWS Lambda@Edge or Cloudflare Workers.

First, create a Next.js project. Install dependencies using the following commands:

npx create-next-app@latest ai-dashboard
cd ai-dashboard
npm install

Next, configure your `next.config.js` file to enable middleware for edge computing.

module.exports = {
  experimental: {
    middleware: true,
  },
};

Svelte components can be used alongside Next.js using precompiled JavaScript. Create a Svelte file (`Chart.svelte`) for rendering dynamic charts.

<pre class="wp-block-syntaxhighlighter-code">
<script>
  import { onMount } from "svelte";
  let data = [];

  onMount(async () => {
    const response = await fetch('/api/data');
    data = await response.json();
  });
</script>

<style>
  .chart {
    width: 100%;
    height: 400px;
  }
</style>

<div class="chart">
  {#each data as item}
    <div style="height: {item.value}px; background-color: blue;"></div>
  {/each}
</div>
</pre>

Deploy your machine learning model using AWS Lambda@Edge or Cloudflare Workers. Below is an example of deploying a Python-based AI model using AWS Lambda:

import json
import boto3

def lambda_handler(event, context):
    # Simulate AI model prediction
    input_data = json.loads(event['body'])
    prediction = sum(input_data['features']) / len(input_data['features'])

    return {
        "statusCode": 200,
        "body": json.dumps({"prediction": prediction})
    }

Create an API route in Next.js to fetch predictions from the edge.

export default async function handler(req, res) {
  const response = await fetch('https://your-edge-location.com/predict', {
    method: 'POST',
    body: JSON.stringify(req.body),
  });

  const prediction = await response.json();
  res.status(200).json(prediction);
}

Combine Next.js and Svelte components to render the final dashboard. Below is an example of how the `index.js` file in Next.js integrates Svelte’s charts:

<pre class="wp-block-syntaxhighlighter-code">
import dynamic from 'next/dynamic';

// Dynamically import the Svelte component
const Chart = dynamic(() => import('../components/Chart.svelte'), { ssr: false });

export default function Dashboard() {
  return (
    <div>
      <h1>AI Dashboard</h1>
      <Chart />
    </div>
  );
}
</pre>

Finally, deploy your application using a platform that supports edge computing, such as Vercel for Next.js or Netlify. Ensure your Svelte components are precompiled and your edge functions are correctly configured.

By combining Next.js 14, Svelte, and edge computing, you can build high-performance AI dashboards optimized for low-latency applications. This approach leverages the strengths of each technology, including Next.js for routing and SSR, Svelte for UI efficiency, and edge computing for real-time data processing.