Next.js + Modal.com Integration (2025)

Quick Reference

When to Use This Skill

Use for Next.js + Modal integration:

• AI inference that's too heavy for Edge/​Vercel Functions
• Video/​audio processing with FFmpeg
• Background jobs exceeding Vercel's 60s timeout
• GPU workloads (image generation, LLMs, embeddings)
• Cost-effective scaling for burst compute

Architecture principle: Next.js handles UI/​auth/​routing, Modal handles heavy compute.

---

Next.js + Modal.com Integration (2025)

Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                    Next.js (Vercel)                         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐ │
│  │   Pages/    │  │    API      │  │    Server           │ │
│  │   App       │  │   Routes    │  │    Actions          │ │
│  │   Router    │  │             │  │                     │ │
│  └──────┬──────┘  └──────┬──────┘  └──────────┬──────────┘ │
└─────────┼────────────────┼───────────────────┼─────────────┘
          │                │                   │
          └────────────────┼───────────────────┘
                           │ HTTPS
                           ▼
┌─────────────────────────────────────────────────────────────┐
│                    Modal.com                                │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐ │
│  │   FastAPI   │  │    GPU      │  │    Background       │ │
│  │   Endpoint  │  │   Functions │  │    Jobs             │ │
│  │             │  │   (A100)    │  │    (.spawn())       │ │
│  └─────────────┘  └─────────────┘  └─────────────────────┘ │
└─────────────────────────────────────────────────────────────┘

Modal Backend Setup

Basic FastAPI Endpoint

# modal_backend/​app.py
import modal
from datetime import datetime

app = modal.App("nextjs-backend")

image = (
    modal.Image.debian_slim(python_version="3.11")
    .pip_install("fastapi", "pydantic")
)

@app.function(image=image)
@modal.concurrent(max_inputs=100, target_inputs=50)
@modal.asgi_app()
def api():
    """FastAPI endpoint for Next.js frontend"""
    from fastapi import FastAPI, HTTPException, Depends
    from fastapi.middleware.cors import CORSMiddleware
    from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
    from pydantic import BaseModel, Field

    web_app = FastAPI(title="Next.js Backend API")

    # CORS configuration for Next.js
    web_app.add_middleware(
        CORSMiddleware,
        allow_origins=[
            "http://localhost:3000",           # Next.js dev
            "https://*.vercel.app",            # Vercel preview
            "https://yourdomain.com",          # Production
        ],
        allow_credentials=True,
        allow_methods=["*"],
        allow_headers=["*"],
    )

    # Simple API key auth
    security = HTTPBearer()
    API_KEY = "your-secret-key"  # Use modal.Secret in production

    def verify_token(creds: HTTPAuthorizationCredentials = Depends(security)):
        if creds.credentials != API_KEY:
            raise HTTPException(status_code=401, detail="Invalid API key")
        return creds.credentials

    # === Endpoints ===

    class ProcessRequest(BaseModel):
        data: str = Field(..., min_length=1)
        options: dict = {}

    class ProcessResponse(BaseModel):
        result: str
        processed_at: str

    @web_app.post("/​process", response_model=ProcessResponse)
    def process_endpoint(
        req: ProcessRequest,
        token: str = Depends(verify_token)
    ):
        # Your processing logic here
        result = f"Processed: {req.data}"

        return ProcessResponse(
            result=result,
            processed_at=datetime.utcnow().isoformat()
        )

    @web_app.get("/​health")
    def health():
        return {"status": "healthy", "timestamp": datetime.utcnow().isoformat()}

    return web_app

Deploy with:

modal deploy modal_backend/​app.py
# Returns: https://your-workspace--nextjs-backend-api.modal.run

---

Next.js API Route Integration

Basic API Route (App Router)

// app/​api/​process/​route.ts
import { NextRequest, NextResponse } from 'next/​server';

const MODAL_API_URL = process.env.MODAL_API_URL!;
const MODAL_API_KEY = process.env.MODAL_API_KEY!;

export async function POST(req: NextRequest) {
  try {
    const body = await req.json();

    const response = await fetch(`${MODAL_API_URL}/​process`, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/​json',
        'Authorization': `Bearer ${MODAL_API_KEY}`,
      },
      body: JSON.stringify({
        data: body.data,
        options: body.options || {},
      }),
    });

    if (!response.ok) {
      const error = await response.text();
      throw new Error(`Modal API error: ${response.status} - ${error}`);
    }

    const result = await response.json();
    return NextResponse.json(result);

  } catch (error) {
    console.error('Processing error:', error);
    return NextResponse.json(
      { error: 'Processing failed' },
      { status: 500 }
    );
  }
}

Environment Variables

# .env.local
MODAL_API_URL=https://your-workspace--nextjs-backend-api.modal.run
MODAL_API_KEY=your-secret-key

---

AI Image Generation Example

Modal Backend

# modal_backend/​image_gen.py
import modal

app = modal.App("image-generator")

image = (
    modal.Image.debian_slim(python_version="3.11")
    .pip_install(
        "fastapi",
        "torch",
        "diffusers",
        "transformers",
        "accelerate",
        "pydantic",
    )
)

models_volume = modal.Volume.from_name("sd-models", create_if_missing=True)

@app.cls(
    image=image,
    gpu="A100-40GB",
    volumes={"/​models": models_volume},
    min_containers=1,  # Keep warm for fast response
    max_containers=5,
    container_idle_timeout=300,
)
class ImageGenerator:
    @modal.enter()
    def setup(self):
        import torch
        from diffusers import StableDiffusionXLPipeline

        print("Loading SDXL model...")
        self.pipe = StableDiffusionXLPipeline.from_pretrained(
            "stabilityai/​stable-diffusion-xl-base-1.0",
            torch_dtype=torch.float16,
            cache_dir="/​models",
        )
        self.pipe.to("cuda")
        print("Model ready!")

    @modal.method()
    def generate(
        self,
        prompt: str,
        negative_prompt: str = "",
        width: int = 1024,
        height: int = 1024,
        steps: int = 30,
    ) -> bytes:
        """Generate image and return as PNG bytes"""
        import io

        image = self.pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            width=width,
            height=height,
            num_inference_steps=steps,
        ).images[0]

        buffer = io.BytesIO()
        image.save(buffer, format="PNG")
        return buffer.getvalue()


@app.function(image=image)
@modal.concurrent(max_inputs=50)
@modal.asgi_app()
def api():
    from fastapi import FastAPI, HTTPException
    from fastapi.middleware.cors import CORSMiddleware
    from fastapi.responses import Response
    from pydantic import BaseModel, Field

    web_app = FastAPI()

    web_app.add_middleware(
        CORSMiddleware,
        allow_origins=["http://localhost:3000", "https://*.vercel.app"],
        allow_methods=["*"],
        allow_headers=["*"],
    )

    class GenerateRequest(BaseModel):
        prompt: str = Field(..., min_length=1, max_length=1000)
        negative_prompt: str = ""
        width: int = Field(1024, ge=512, le=2048)
        height: int = Field(1024, ge=512, le=2048)
        steps: int = Field(30, ge=20, le=50)

    @web_app.post("/​generate")
    def generate_endpoint(req: GenerateRequest):
        generator = ImageGenerator()

        try:
            image_bytes = generator.generate.remote(
                prompt=req.prompt,
                negative_prompt=req.negative_prompt,
                width=req.width,
                height=req.height,
                steps=req.steps,
            )
            return Response(content=image_bytes, media_type="image/​png")
        except Exception as e:
            raise HTTPException(status_code=500, detail=str(e))

    return web_app

Next.js API Route

// app/​api/​generate-image/​route.ts
import { NextRequest, NextResponse } from 'next/​server';

const MODAL_API_URL = process.env.MODAL_IMAGE_GEN_URL!;

export async function POST(req: NextRequest) {
  try {
    const body = await req.json();

    const response = await fetch(`${MODAL_API_URL}/​generate`, {
      method: 'POST',
      headers: { 'Content-Type': 'application/​json' },
      body: JSON.stringify({
        prompt: body.prompt,
        negative_prompt: body.negativePrompt || '',
        width: body.width || 1024,
        height: body.height || 1024,
        steps: body.steps || 30,
      }),
    });

    if (!response.ok) {
      throw new Error(`Generation failed: ${response.statusText}`);
    }

    const imageBuffer = await response.arrayBuffer();

    return new NextResponse(imageBuffer, {
      headers: {
        'Content-Type': 'image/​png',
        'Cache-Control': 'public, max-age=31536000, immutable',
      },
    });
  } catch (error) {
    console.error('Image generation error:', error);
    return NextResponse.json({ error: 'Generation failed' }, { status: 500 });
  }
}

React Component

// components/​ImageGenerator.tsx
'use client';

import { useState } from 'react';

export default function ImageGenerator() {
  const [prompt, setPrompt] = useState('');
  const [imageUrl, setImageUrl] = useState('');
  const [loading, setLoading] = useState(false);
  const [error, setError] = useState('');

  const generateImage = async () => {
    setLoading(true);
    setError('');

    try {
      const response = await fetch('/​api/​generate-image', {
        method: 'POST',
        headers: { 'Content-Type': 'application/​json' },
        body: JSON.stringify({ prompt }),
      });

      if (!response.ok) {
        throw new Error('Generation failed');
      }

      const blob = await response.blob();
      const url = URL.createObjectURL(blob);
      setImageUrl(url);
    } catch (err) {
      setError(err instanceof Error ? err.message : 'Unknown error');
    } finally {
      setLoading(false);
    }
  };

  return (
    <div className="max-w-2xl mx-auto p-6">
      <h1 className="text-3xl font-bold mb-6">AI Image Generator</​h1>

      <textarea
        className="w-full p-4 border rounded-lg mb-4 min-h-[100px]"
        placeholder="Describe the image you want to generate..."
        value={prompt}
        onChange={(e) => setPrompt(e.target.value)}
      />

      <button
        className="w-full bg-blue-600 text-white py-3 px-6 rounded-lg font-medium
                   disabled:opacity-50 disabled:cursor-not-allowed
                   hover:bg-blue-700 transition-colors"
        onClick={generateImage}
        disabled={loading || !prompt.trim()}
      >
        {loading ? 'Generating...' : 'Generate Image'}
      </​button>

      {error && (
        <div className="mt-4 p-4 bg-red-100 text-red-700 rounded-lg">
          {error}
        </​div>
      )}

      {imageUrl && (
        <div className="mt-6">
          <img
            src={imageUrl}
            alt="Generated image"
            className="w-full rounded-lg shadow-lg"
          />
        </​div>
      )}
    </​div>
  );
}

---

Webhooks, File Uploads & Streaming LLM Responses

Complete patterns for long-running Modal jobs with webhook callbacks (async job queueing, polling vs webhook, retry / failure handling), file upload processing pipelines (presigned URLs, chunked uploads, virus scanning, image processing), and streaming LLM responses to Next.js clients (Server-Sent Events, ReadableStream, abort handling) live in references/​webhooks-uploads-streaming.md. Load that reference when wiring async jobs, file pipelines, or LLM streaming endpoints between Next.js and Modal.

---

Best Practices

1. Keep Modal API Keys Server-Side

// GOOD: API key in server-side API route
// app/​api/​process/​route.ts
const MODAL_API_KEY = process.env.MODAL_API_KEY; // Server only

// BAD: Exposing API key to client
// NEXT_PUBLIC_MODAL_API_KEY=... // Never do this!

2. Use Warm Containers for Production

@app.cls(
    min_containers=1,           # Always keep 1 warm
    max_containers=10,          # Scale up to 10
    container_idle_timeout=300, # Keep warm for 5 min after use
)
class MyService:
    pass

3. Handle Vercel Timeouts

// Vercel timeout limits:
// - Hobby: 60 seconds
// - Pro: 300 seconds
// - Enterprise: 900 seconds

// For longer jobs, use webhooks instead of waiting

4. Implement Request Timeouts

const controller = new AbortController();
const timeoutId = setTimeout(() => controller.abort(), 55000); // 55s

try {
  const response = await fetch(url, { signal: controller.signal });
} finally {
  clearTimeout(timeoutId);
}

5. Cache Modal Responses

// Next.js 15 cache pattern
import { unstable_cache } from 'next/​cache';

const getCachedResult = unstable_cache(
  async (id: string) => {
    const response = await fetch(`${MODAL_API_URL}/​process/​${id}`);
    return response.json();
  },
  ['modal-result'],
  { revalidate: 3600 } // Cache for 1 hour
);

---

Common Pitfalls

1. CORS Errors

# Make sure CORS allows your Next.js domains
web_app.add_middleware(
    CORSMiddleware,
    allow_origins=[
        "http://localhost:3000",
        "https://*.vercel.app",  # All Vercel preview URLs
        "https://yourdomain.com",
    ],
    allow_methods=["*"],
    allow_headers=["*"],
)

2. Cold Start Latency

# Use min_containers=1 for production APIs
@app.function(min_containers=1)  # ~$1-2/​day idle cost
def api():
    pass

3. Large Payloads

// For large files, upload directly to Modal or use signed URLs
// Don't pass >4MB through Next.js API routes

// Better: Use Modal's CloudBucketMount

4. Error Handling

// Always handle Modal errors gracefully
try {
  const response = await fetch(MODAL_API_URL);
  if (!response.ok) {
    // Log for debugging
    console.error(`Modal error: ${response.status}`, await response.text());
    // Return user-friendly error
    return NextResponse.json(
      { error: 'Service temporarily unavailable' },
      { status: 503 }
    );
  }
} catch (error) {
  // Handle network errors
  console.error('Network error:', error);
  return NextResponse.json(
    { error: 'Could not connect to service' },
    { status: 503 }
  );
}

---

Deployment Checklist

1. Deploy Modal backend first

   modal deploy modal_backend/​app.py
   

2. Set environment variables in Vercel

   MODAL_API_URL=https://your-workspace--app-api.modal.run
   MODAL_API_KEY=your-secret-key
   WEBHOOK_SECRET=your-webhook-secret
   

3. Update CORS for production domain

4. Enable warm containers for production

   @app.function(min_containers=1)
   

5. Monitor costs

   modal app stats your-app-name
   

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Related Skills

• nextjs-server-actions - Server Actions patterns
• nextjs-caching - Caching strategies
• nextjs-deployment - Deployment configurations