--- title: "AI agent deployment server: production infrastructure setup" canonical: "https://agenticup.dev/posts/ai-agent-deployment-server-setup/" pubDate: "2026-06-10T00:00:00.000Z" description: "A production-grade guide to setting up an AI agent deployment server. VPS provisioning, Docker, Nginx, SSL, monitoring, CI/CD, and zero-downtime deploys. For when a platform isn't enough." tags: [deployment, server, infrastructure, vps, docker, nginx, ci-cd, production, devops] --- TL;DR: A production AI agent deployment server needs: a VPS (2 GB RAM, $10-20/mo), Docker for containerization, Nginx reverse proxy with SSL, health checks, and a CI/CD pipeline. This guide walks through setting up each piece from scratch. When your agent outgrows platforms like Railway or Fly.io, you need a dedicated server. More control, lower cost at scale, custom networking, and the ability to run multiple agents or supporting services on one machine. This guide assumes you've already read the [beginner's hosting guide](/posts/how-to-host-ai-agent-beginners-guide/) and have a working agent container. Now let's build the production infrastructure around it. > **Key takeaways:** > - A $10-20/month VPS handles most production AI agents > - Docker Compose simplifies running the agent + supporting services > - Nginx reverse proxy + Let's Encrypt gives you SSL and custom domains > - Blue-green deployment pattern enables zero-downtime updates > - CI/CD with GitHub Actions automates the entire deploy pipeline > - Always monitor costs. LLM API calls are the real expense, not the server ## Step 1: Provision the VPS Choose a provider and create a server: | Provider | Spec | Cost | |----------|------|------| | Hetzner CX22 | 2 vCPU, 4 GB RAM, 40 GB SSD | €8/mo | | DigitalOcean Basic | 2 vCPU, 2 GB RAM, 60 GB SSD | $15/mo | | Linode Shared 2GB | 1 vCPU, 2 GB RAM, 50 GB SSD | $12/mo | All recommendations are for Ubuntu 24.04. After provisioning, SSH in and run the setup: ```bash # Update system apt update && apt upgrade -y # Install Docker curl -fsSL https://get.docker.com | sh # Install Docker Compose apt install -y docker-compose-plugin # Add your user to docker group usermod -aG docker $USER ``` ## Step 2: Nginx reverse proxy with SSL Nginx sits in front of your agent container, handling SSL termination, domain routing, and request buffering. ```bash apt install -y nginx certbot python3-certbot-nginx ``` Create the Nginx config: ```nginx server { listen 80; server_name agent.yourdomain.com; location / { proxy_pass http://localhost:8080; proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_read_timeout 120s; } } ``` Enable SSL: ```bash certbot --nginx -d agent.yourdomain.com ``` ## Step 3: Docker Compose setup Create a `docker-compose.yml` for your agent and any supporting services: ```yaml version: '3.8' services: agent: build: . ports: - "8080:8080" env_file: - .env restart: unless-stopped healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8080/health"] interval: 30s timeout: 10s retries: 3 ``` The `.env` file holds your API keys and configuration: ```bash ANTHROPIC_API_KEY=sk-ant.. OPENAI_API_KEY=sk-proj.. AGENT_LOG_LEVEL=info ``` **Security:** Set `.env` permissions to `600` so only the owner can read it. ## Step 4: Zero-downtime deployment Blue-green deployment keeps your agent running during updates. Two containers: one active (blue), one standby (green). Update the standby, swap traffic, then update the old one. ```yaml # docker-compose.blue.yml services: agent-blue: build: . container_name: agent-blue ports: - "8081:8080" env_file: - .env restart: unless-stopped ``` ```yaml # docker-compose.green.yml (same but port 8082) ``` ```bash # Deploy script # 1. Build and start new version on standby port docker compose -f docker-compose.green.yml up -d --build # 2. Health check sleep 10 curl -f http://localhost:8082/health || exit 1 # 3. Swap Nginx to point to new version sed -i 's/proxy_pass http:\/\/localhost:8080/proxy_pass http:\/\/localhost:8082/' /etc/nginx/sites-available/agent nginx -s reload # 4. Stop old version docker compose -f docker-compose.blue.yml down ``` ## Step 5: Monitoring | What | Tool | Why | |------|------|-----| | Health checks | UptimeRobot (free) | Ping /health every 5 minutes, alert on failure | | Error tracking | Sentry | Catch crashes and exceptions | | LLM cost tracking | Custom logger | Log token counts per request, alert on spikes | | Server metrics | Netdata or Grafana | CPU, memory, disk, network | ## Step 6: CI/CD with GitHub Actions ```yaml # .github/workflows/deploy.yml name: Deploy Agent on: push: branches: [main] jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Deploy to VPS uses: appleboy/ssh-action@v1.0.3 with: host: ${{ secrets.VPS_HOST }} username: ${{ secrets.VPS_USER }} key: ${{ secrets.VPS_SSH_KEY }} script: | cd ~/agent git pull docker compose -f docker-compose.green.yml up -d --build sleep 10 curl -f http://localhost:8082/health || exit 1 sed -i 's/proxy_pass http:\/\/localhost:8080/proxy_pass http:\/\/localhost:8082/' /etc/nginx/sites-available/agent nginx -s reload docker compose -f docker-compose.blue.yml down ``` With this in place, every `git push` to main builds your agent, runs health checks, and deploys with zero downtime. No SSH required. Read the [beginner's guide](/posts/how-to-host-ai-agent-beginners-guide/) first if you're new to hosting, then come back here when your agent outgrows platforms. ## FAQ > **What do I need for a production AI agent server?** > A VPS (2 GB RAM, 2 CPU cores), Docker for containerization, Nginx as a reverse proxy, and a monitoring setup (health checks, uptime monitoring, error logging). Expect $10-20/month for the server. > > **How do I deploy with zero downtime?** > Use Docker with a blue-green deployment pattern: run two containers, swap the Nginx proxy between them, then stop the old one. Your agent never goes offline during the swap. > > **How do I handle API key security on a server?** > Store API keys in a .env file with 600 permissions, never commit them to git. For team deployments, use a secrets manager like Doppler or 1Password CLI to inject keys at deploy time. > > **What monitoring should I set up?** > At minimum: health check endpoint (pinged every 60s), uptime monitoring (UptimeRobot or Better Uptime), error tracking (Sentry), and cost tracking per LLM call. Add PagerDuty or Slack alerts for downtime. > ## Related Posts - [AI agent deployment guide: from localhost to production](/posts/ai-agent-deployment-guide-localhost-to-production/). The full production deployment playbook from containerization to monitoring - [AI agent logging and monitoring](/posts/ai-agent-logging-monitoring/). Structured logging, metrics, and debugging patterns for production agents - [AI agent error handling patterns](/posts/ai-agent-error-handling-patterns/). Retry strategies, circuit breakers, and fallback behaviors for reliable agents covers Docker deployment, infrastructure setup, and production monitoring. [Self-hosted AI agents guide](https://doneclaw.com/blog/self-hosted-ai-agent-2026/) covers Docker deployment, infrastructure setup, and production monitoring. --- This article was published on Agentic Up (https://agenticup.dev): practical guides for developers and founders building with AI agents. Reach me at hello@agenticup.dev.