Skip to content

Chapter 9: Initial Deployment Strategy

After building a functional chatbot with a modular backend, smart prompt logic, vector search, and a clean frontend interface, it’s time to put it all together and deploy it to the cloud.

This chapter walks you through deploying both frontend and backend components using Render and Netlify, setting up environment variables, and optionally containerizing your backend using Docker for better portability and scalability. You’ll walk away with a fully working MVP chatbot, accessible via the web.

9.1 Deployment Architecture Overview

Your deployment setup will follow a frontend-backend split:

┌────────────┐      HTTPS       ┌────────────┐       HTTPS/API       ┌──────────────┐
│  React UI  │ ───────────────▶ │ FastAPI App│ ─────────────────────▶│ OpenAI, DB   │
└────────────┘                 └────────────┘                        └──────────────┘
     (Netlify)                    (Render)                              (OpenAI API, Supabase)

9.2 Backend Deployment with Render

Why Render?

  • Free tier available.
  • Easy FastAPI support.
  • Built-in environment variable management.
  • Automatic redeployment on push.

Steps:

  1. Create a GitHub repo with your FastAPI backend code. Folder structure:
   chatbot-backend/
     ├── app/
     │   ├── main.py
     │   ├── routes.py
     │   ├── openai_utils.py
     │   ├── vectorstore.py
     │   └── ...
     ├── requirements.txt
     └── Dockerfile (optional)

  1. Set up Render service:

  2. Go to render.com

  3. Create a new Web Service
  4. Connect your GitHub repo
  5. Set build command: pip install -r requirements.txt

  6. Set start command: uvicorn app.main:app --host=0.0.0.0 --port=10000

  7. Add environment variables in Render’s dashboard:

  8. OPENAI_API_KEY

  9. SUPABASE_URL
  10. SUPABASE_KEY

  11. (any others your backend uses)

  12. Verify API endpoint: You should be able to test:

https://your-backend-name.onrender.com/chat

9.3 Frontend Deployment with Netlify

Why Netlify?

  • React-friendly and automatic from GitHub.
  • Free tier with HTTPS.
  • Supports custom domains, environment variables, redirects.

Steps:

  1. Create a GitHub repo with your React frontend code. Typical structure:
   chatbot-frontend/
     ├── public/
     ├── src/
     │   └── App.jsx
     ├── .env
     └── package.json
  1. Configure .env file (optional):
   VITE_API_URL=https://your-backend-name.onrender.com

  1. Deploy to Netlify:

  2. Go to netlify.com

  3. Connect your GitHub frontend repo
  4. Set build command: npm run build
  5. Set publish directory: dist (for Vite) or build (for CRA)

  6. Add environment variables if needed

  7. Verify chatbot UI: You should now be able to visit:

   https://your-chatbot-site.netlify.app

9.4 Optional: Backend Dockerization

Containerization ensures your backend works consistently across local, cloud, or production environments.

Sample Dockerfile:

FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY app/ ./app
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

Run Locally:

docker build -t claybot-backend .
docker run -p 8000:8000 --env-file .env claybot-backend

You can later deploy this container to:

  • Render (via Docker tab)
  • Railway
  • AWS ECS / GCP Cloud Run / Azure Container Apps
  • Kubernetes (for scaling Part 4)

9.5 Deployment Checklist

Task Status ✅
FastAPI app works locally ✅
.env secrets are configured ✅
Vector DB (Supabase) is live ✅
OpenAI API tested successfully ✅
Render service up ✅
Frontend connects to backend ✅
Netlify deployed UI ✅
CORS configured properly ✅
Test: end-to-end conversation ✅

9.6 Tips for MVP Rollout

  • Collect Logs: Use Render’s dashboard or integrate a logging service.
  • Add Feedback Button: Let users report bugs or suggestions.
  • Soft Launch: Share with a few testers before public launch.
  • Monitor Usage: Track API token usage, cost, and errors.

Remember: MVP doesn't mean minimal functionality. It means delivering value as early as possible with the least complexity.

Conclusion: Your Chatbot Is Live 🎉

With your MVP chatbot deployed to the cloud, you’ve completed the full loop:

  • Designed architecture
  • Engineered prompts
  • Embedded external knowledge
  • Built frontend interactions
  • Deployed a live chatbot

This makes you ready to scale, extend, and customize. In Part 3, we shift gears to gain full control over chatbot infrastructure—by learning how to host your own LLM models, optimize performance, and cut costs dramatically.