render.yaml

services:
  # Backend API - HACKATHON MAX PERFORMANCE CONFIG
  - type: web
    name: saarthi-backend
    env: python
    region: singapore
    plan: starter  # $7/month - 512MB RAM + better CPU (upgrade to "standard" for 1GB if needed)
    rootDir: mas_scholar_app/backend
    buildCommand: pip install --no-cache-dir -r requirements.txt
    startCommand: uvicorn main:app --host 0.0.0.0 --port $PORT --workers 1
    healthCheckPath: /
    envVars:
      - key: PYTHON_VERSION
        value: "3.11.0"
      - key: ENABLE_VECTOR_SEARCH
        value: "true"  # Full hybrid search enabled!
      - key: QDRANT_HOST
        sync: false
      - key: QDRANT_API_KEY
        sync: false
      - key: GEMINI_API_KEY
        sync: false
      - key: ALLOWED_ORIGINS
        value: "https://saarthi-ai.vercel.app"
    autoDeploy: true

# HACKATHON DEPLOYMENT OPTIONS:
#
# Option 1: Render Starter ($7/month) - Current config
#   - 512MB RAM, better CPU than free
#   - May still OOM with full model, try "standard" plan if so
#
# Option 2: Render Standard ($25/month)
#   - 1GB RAM - guaranteed to work with full hybrid search
#
# Option 3: Railway.app (Free tier has 512MB but more lenient)
#   - Often works better than Render free for ML workloads
#
# Option 4: Fly.io (256MB-1GB free tier)
#   - Good alternative with generous limits
#
# For Qdrant Cloud (offload vector storage): https://cloud.qdrant.io