Local development udah beres? Sekarang saatnya deploy ke production biar bisa dipake orang lain.

Kenapa Railway?

  • Free tier tersedia
  • Auto-deploy dari GitHub
  • No infra management
  • Support Docker

Step 1: Dockerize AI Agent

Buat Dockerfile:

FROM python:3.11-slim

WORKDIR /app

RUN apt-get update && apt-get install -y gcc && rm -rf /var/lib/apt/lists/*

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

EXPOSE 8080

CMD ["python", "main.py"]

Buat requirements.txt:

openai==1.3.0
flask==3.0.0
python-dotenv==1.0.0
gunicorn==21.2.0

Step 2: Web API Wrapper

from flask import Flask, request, jsonify
import os

app = Flask(__name__)

@app.route('/health')
def health():
    return jsonify({'status': 'ok'})

@app.route('/chat', methods=['POST'])
def chat():
    data = request.json
    message = data.get('message')
    
    if not message:
        return jsonify({'error': 'Message required'}), 400
    
    try:
        response = generate_response(message)
        return jsonify({'response': response})
    except Exception as e:
        return jsonify({'error': str(e)}), 500

if __name__ == '__main__':
    port = int(os.getenv('PORT', 8080))
    app.run(host='0.0.0.0', port=port)

Step 3: Deploy ke Railway

  1. Push ke GitHub
  2. Login ke railway.app
  3. New Project > Deploy from GitHub repo
  4. Set environment variables
  5. Auto-deploy!

Cost Breakdown

Railway Free Tier:

  • 500 hours/month
  • 1GB RAM
  • Sufficient untuk testing

Railway Pro: $5/month

  • Unlimited hours
  • 8GB RAM

Conclusion

Deploy AI agent ke production gak serumit yang dibayangkan. Dengan Docker + Railway, kamu bisa live dalam 30 menit.

Butuh bantuan deploy? DM di Telegram!