Why I Stopped Using Vercel Edge Functions for AI Apps (And What I Use Instead)

Three months ago, I was all-in on Vercel Edge Functions for my AI projects. The promise was irresistible: globally distributed compute, minimal cold starts, and seamless integration with my Next.js apps. Then I started hitting walls that fundamentally changed how I think about deploying AI applications.

The breaking point came during a client project - a document analysis tool that used Claude 3.5 Sonnet. Everything worked perfectly in development, but production was a different story.

The Edge Runtime Reality Check

Vercel's Edge Functions run on a stripped-down JavaScript runtime that doesn't include Node.js APIs. This sounds fine until you realize how many AI-related libraries depend on Node.js internals.

Here's what broke in my projects:

PDF processing libraries - Most popular PDF parsers like pdf-parse or pdf2pic need Node.js file system APIs. I spent days trying to find edge-compatible alternatives before giving up.

Complex tokenization - Libraries like tiktoken for OpenAI token counting had issues with WebAssembly modules in the edge runtime.

File handling - Any meaningful file processing beyond basic text became a headache. Want to process images before sending to GPT-4 Vision? Good luck.

I found myself writing increasingly complex workarounds, pushing logic to middleware, or falling back to serverless functions anyway. The "edge-first" architecture was becoming "edge-sometimes" - which defeats the purpose.

Timeout Troubles with Long-Running AI Tasks

Edge Functions have a 30-second execution limit (25 seconds on hobby plans). For simple chatbot responses, that's plenty. For document analysis, content generation, or batch processing? Not even close.

I learned this the hard way when Claude took 40+ seconds to analyze a complex legal document. The function would timeout, the user would see an error, but I'd still get charged for the API call. Worse, there's no graceful way to handle partial results or resume processing.

Here's the timeout pattern I kept hitting:

// This would work fine locally but timeout in production
export default async function handler(req: Request) {
  const documents = await parseMultiplePDFs(req.body.files); // 10s
  const analysis = await claude.analyze(documents); // 35s - TIMEOUT!
  return Response.json({ analysis });
}

Cost Optimization Isn't Always Edge-Shaped

Vercel's pricing model charges per function invocation plus execution time. For AI apps with unpredictable response times, this gets expensive quickly.

I was paying premium prices for edge distribution I didn't need. Most of my users were in specific regions, and AI API calls to OpenAI or Anthropic already introduced latency that dwarfed any edge benefits.

The math didn't add up:

• Edge Function: $0.60 per million invocations + $0.20 per million GB-seconds
• Traditional serverless: Often 2-3x cheaper for the same workload
• Self-hosted: Even better economics at scale

What I Use Instead: A Hybrid Approach

I've settled on a three-tier architecture that plays to each platform's strengths:

Railway for Heavy AI Processing

Railway became my go-to for AI-heavy workloads. It's basically Heroku without the baggage - proper Node.js runtime, no arbitrary timeouts, and predictable pricing.

// Full Node.js APIs available
import fs from 'fs';
import { PDFDocument } from 'pdf-lib';

app.post('/analyze-document', async (req, res) => { const pdf = await PDFDocument.load(req.file.buffer); const text = await extractText(pdf); // Works reliably const analysis = await openai.chat.completions.create({ model: 'gpt-4-turbo', messages: [{ role: 'user', content: Analyze: ${text} }], timeout: 120000 // Can handle long responses }); res.json({ analysis }); }); `

Supabase Edge Functions for Simple AI Tasks

For straightforward AI calls - chat responses, text completion, simple analysis - Supabase Edge Functions work great. They have better Node.js compatibility than Vercel Edge, and the pricing is more predictable.

// supabase/functions/chat/index.ts
import { serve } from 'https://deno.land/std@0.168.0/http/server.ts'

serve(async (req) => { const { message } = await req.json() const completion = await openai.chat.completions.create({ model: 'gpt-3.5-turbo', messages: [{ role: 'user', content: message }], max_tokens: 150 }) return new Response(JSON.stringify({ response: completion.choices[0].message.content })) }) `

Next.js API Routes for Integration Logic

I still use Vercel for my frontend and simple API endpoints. The developer experience is unmatched, and it excels at what it's designed for - serving React apps and handling straightforward API logic.

When Edge Functions Still Make Sense

I'm not completely anti-edge. They're excellent for:

• Authentication middleware - JWT validation, session checks
• Simple AI chat - Quick OpenAI calls under 30 seconds
• A/B testing - Request routing and feature flags
• Analytics - Event collection and forwarding

Basically, anything that's fast, lightweight, and doesn't need the full Node.js ecosystem.

The Migration Process

Moving away from edge-first wasn't trivial, but it was straightforward:

1. 1Audit your functions - Identify which ones actually benefit from edge distribution
2. 2Move heavy processing - Migrate long-running AI tasks to traditional serverless or Railway
3. 3Keep the frontend - Vercel still excels at hosting Next.js apps
4. 4Update your client code - Point heavy operations to your new endpoints

The result? More reliable deployments, lower costs for my use cases, and way less debugging of runtime compatibility issues.

Practical Takeaways

• Audit before you commit - Not every API endpoint needs edge distribution
• Consider your actual latency bottlenecks - AI API calls often dwarf edge benefits
• Factor in runtime limitations - If you need Node.js APIs, edge isn't the answer
• Watch your timeout limits - 30 seconds goes by quickly with AI processing
• Do the cost math - Edge premium might not pay off for your usage patterns
• Use the right tool - Hybrid approaches often work better than all-in strategies

I still love Vercel for what it does best - hosting React apps with incredible DX. But for AI applications, I've learned that the edge isn't always the answer. Sometimes the best architecture is the boring one that just works reliably.

What's your experience been with edge functions for AI workloads? I'd love to hear if you've found better patterns or hit similar walls.