Cloudways gives you a managed server with SSH access — perfect for AI apps that need system-level packages, background workers, or custom Python environments. Here’s how to deploy a FastAPI AI app from scratch.

Why Cloudways for AI apps

Unlike Railway (PaaS, no SSH) or Vercel (serverless, time limits), Cloudways gives you a full server where you can:

• Install Ollama for local inference
• Run long-running background tasks
• Install any Python package or system dependency
• Keep full SSH access for debugging
• Get managed SSL, backups, and monitoring included

Step 1: Create a Cloudways server

1. Sign up at Cloudways
2. Click “Add Server”
3. Choose your cloud provider:
   • DigitalOcean — best value ($14/mo for 1GB)
   • Vultr — good performance
   • AWS — if you need specific regions
   • GCP — if you’re in the Google ecosystem
4. Select server size (2GB+ recommended for AI apps)
5. Choose region closest to your users
6. Launch server (takes 2-3 minutes)

Step 2: Set up Python environment

SSH into your server (credentials in Cloudways dashboard):

ssh master@your-server-ip

# Install Python 3.11+ (if not already available)
sudo apt update
sudo apt install python3.11 python3.11-venv python3-pip -y

# Create project directory
mkdir -p /home/master/ai-app
cd /home/master/ai-app

# Create virtual environment
python3.11 -m venv venv
source venv/bin/activate

Step 3: Deploy your FastAPI app

# main.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import httpx
import os

app = FastAPI()

class Query(BaseModel):
    prompt: str
    max_tokens: int = 500

@app.post("/chat")
async def chat(query: Query):
    api_key = os.environ.get("ANTHROPIC_API_KEY")
    if not api_key:
        raise HTTPException(500, "API key not configured")
    
    async with httpx.AsyncClient() as client:
        response = await client.post(
            "https://api.anthropic.com/v1/messages",
            headers={
                "x-api-key": api_key,
                "anthropic-version": "2023-06-01",
                "content-type": "application/json",
            },
            json={
                "model": "claude-sonnet-4-5-20250514",
                "max_tokens": query.max_tokens,
                "messages": [{"role": "user", "content": query.prompt}],
            },
            timeout=30.0,
        )
    
    data = response.json()
    return {"response": data["content"][0]["text"]}

@app.get("/health")
async def health():
    return {"status": "ok"}

Upload via Git or SFTP:

# Option 1: Git (recommended)
cd /home/master/ai-app
git clone https://github.com/your-repo/ai-app.git .

# Option 2: SFTP
# Use FileZilla or scp to upload files

# Install dependencies
pip install fastapi uvicorn httpx pydantic

Step 4: Set environment variables

# Create .env file
cat > /home/master/ai-app/.env << 'EOF'
ANTHROPIC_API_KEY=sk-ant-your-key-here
PORT=8000
EOF

# Or export directly
export ANTHROPIC_API_KEY=sk-ant-your-key-here

For production, store secrets securely. See our password managers guide for best practices.

Step 5: Run with systemd (production)

Create a systemd service so your app starts automatically and restarts on crash:

sudo cat > /etc/systemd/system/ai-app.service << 'EOF'
[Unit]
Description=AI App FastAPI
After=network.target

[Service]
User=master
WorkingDirectory=/home/master/ai-app
Environment="PATH=/home/master/ai-app/venv/bin"
EnvironmentFile=/home/master/ai-app/.env
ExecStart=/home/master/ai-app/venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
Restart=always
RestartSec=3

[Install]
WantedBy=multi-user.target
EOF

sudo systemctl daemon-reload
sudo systemctl enable ai-app
sudo systemctl start ai-app

# Check status
sudo systemctl status ai-app

Step 6: Configure Nginx reverse proxy

Cloudways includes Nginx. Add a reverse proxy to route your domain to the FastAPI app:

# Add to your Nginx server block
location /api/ {
    proxy_pass http://127.0.0.1:8000/;
    proxy_set_header Host $host;
    proxy_set_header X-Real-IP $remote_addr;
    proxy_read_timeout 60s;
}

Cloudways handles SSL automatically through their dashboard.

Step 7: Set up auto-deploy with Git

For automatic deploys on git push:

# On the server, create a bare repo
mkdir -p /home/master/ai-app.git
cd /home/master/ai-app.git
git init --bare

# Create post-receive hook
cat > hooks/post-receive << 'EOF'
#!/bin/bash
GIT_WORK_TREE=/home/master/ai-app git checkout -f
cd /home/master/ai-app
source venv/bin/activate
pip install -r requirements.txt
sudo systemctl restart ai-app
EOF

chmod +x hooks/post-receive

Now push from your local machine:

git remote add production master@your-server-ip:/home/master/ai-app.git
git push production main

Cost breakdown

Component	Cost
Cloudways DO 2GB server	$28/mo
Domain (Cloudflare)	~$1/mo
LLM API (Claude, DeepSeek)	$5-50/mo
SSL, backups, monitoring	Included
Total	$34-79/mo

Compare with Railway ($5-30/mo but no SSH) or Hetzner (€4.50/mo but no managed services).

When to choose Cloudways over Railway

Need	Cloudways	Railway
SSH access	✅	❌
Install system packages	✅	Limited
Background workers	✅	✅
Managed SSL/backups	✅	✅
One-click deploy	❌ (Git/SFTP)	✅
Auto-scaling	❌ (vertical only)	✅
Multiple apps on one server	✅	❌ (separate services)
Predictable pricing	✅	❌ (usage-based)

Choose Cloudways when you need server access and predictable costs. Choose Railway when you want the simplest possible deploy.

Related: Cloudways vs Railway vs Hetzner · Deploy AI App on Railway · AI App Deployment Checklist · Best Hosting for AI Side Projects · Best Password Managers for Developers