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DEPLOYMENT_CHECKLIST.md

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Deployment Checklist

Follow this checklist to deploy your Equai AI application to Render (backend) and Vercel (frontend).

Pre-deployment Checklist

  • Ensure all code is committed and pushed to GitHub
  • Verify MongoDB Atlas connection works (test locally first)
  • Check that all environment variables are properly configured
  • Test the application locally in both development and production modes

Backend Deployment (Render)

  • Create a new Web Service on Render
  • Connect your GitHub repository
  • Set the following configuration:
    • Name: equai-ai-backend
    • Region: Select the closest region
    • Branch: main
    • Root Directory: backend
    • Environment: Python 3
    • Build Command: pip install -r requirements.txt
    • Start Command: gunicorn app:app (for production) or python app.py
    • Plan: Free (or select a paid plan)
  • Configure Environment Variables:
    • MONGODB_URI (your MongoDB connection string)
    • DATABASE_NAME (equai_db)
    • FLASK_ENV (production)
    • PORT (10000)
  • Deploy the service
  • Note the deployed URL (e.g., https://equai-ai-backend.onrender.com)

Frontend Deployment (Vercel)

  • Build the frontend locally to test: 
    cd frontend
npm run build
  • Commit and push any changes to GitHub
  • Import project to Vercel:
    • Go to Vercel Dashboard
    • Click "New Project"
    • Import your GitHub repository
    • Set configuration:
      • Framework Preset: Vite
      • Root Directory: frontend
      • Build Command: npm run build
      • Output Directory: dist
  • Deploy the project
  • Note the deployed URL (e.g., https://equai-ai-frontend.vercel.app)

Post-Deployment Configuration

  • Update the frontend to use the production backend URL:
    • Modify frontend/src/services/api.js to point to your Render backend URL
    • Rebuild and redeploy the frontend
  • Test the complete application:
    • Solve a math problem
    • Check that graphs are generated
    • Verify history and analytics work
  • Set up custom domains if needed (optional)

Monitoring and Maintenance

  • Set up monitoring on Render for the backend
  • Configure error tracking if needed
  • Set up auto-scaling if expecting high traffic
  • Regularly update dependencies for security

Troubleshooting Common Issues

  • CORS errors: Ensure CORS is properly configured in Flask (already done)
  • API connection issues: Verify the frontend is pointing to the correct backend URL
  • MongoDB connection issues: Check the MONGODB_URI environment variable
  • Build failures: Check build logs on both platforms
  • Performance issues: Consider upgrading from free tier to paid plans

Useful Commands

# Build frontend and move to backend static folder
cd frontend
npm run build

# Test backend locally with production settings
cd backend
FLASK_ENV=production python app.py

# Commit and push changes
git add .
git commit -m "Prepare for deployment"
git push origin main