Production Docker Configuration for OpenClaw on VPS: Compose, Volumes, and Auto-Restart

You’ve decided to run OpenClaw on a VPS. Good instinct—it’s powerful, flexible, and you’re in control. But here’s the catch: you can’t just spin up a container and walk away. Production Docker on a VPS requires careful thinking about restarts, data persistence, logging, and resource management. Miss a single detail and you’re waking up at 3 AM to a dead container.

The stakes are high when you move to production. In development, a container crash is annoying. In production, it’s a service outage. Your users can’t do their work. Your team can’t ship. That’s why we need to think about reliability, observability, and recovery from the start.

This guide walks you through a battle-tested Docker Compose configuration that handles the real-world chaos of production deployments. We’ll cover restart policies, volume management, log rotation, health checks, and resource limits. By the end, you’ll have a setup that keeps OpenClaw running reliably without constant babysitting.

The Problem: Why Stock Docker Configs Aren’t Enough

Let’s be honest: the quick-start Docker examples you find online work great in development. You run a container, it does its thing, and when you’re done, you kill it. Clean, simple, no complexity. But production is a different animal entirely.

In production, you’ve got:

• Container crashes from OOM (out of memory), segfaults, or application hangs that need to recover automatically
• Data loss when containers are removed or networks fail, taking your configuration and project data with them
• Disk space issues from unbounded log files that grow until your VPS runs out of space
• Resource hogging from containers that consume all available CPU or RAM, killing sibling processes
• Network failures that leave dangling containers unable to reconnect
• Restart thrashing where containers fail faster than they can recover, creating infinite restart loops

A simple docker run command doesn’t handle any of this. The container runs in isolation with default settings, no persistence strategy, no resource limits, and no health monitoring. The first time something goes wrong, you’ve got a manual recovery situation on your hands.

That’s where Docker Compose comes in. It gives you declarative configuration (write once, run anywhere), health checks (automatic failure detection), and automatic restart logic (self-healing). But you need to know which knobs to turn and why they matter.

Foundation: A Production-Grade docker-compose.yaml

Let’s start with the core configuration. This is a real-world setup for OpenClaw that handles all the production concerns. We’ll build it step by step, explaining the reasoning behind each section.

version: "3.8"

services:
  openclaw:
    image: openclaw:latest
    container_name: openclaw-production

    # === RESTART POLICY ===
    restart: unless-stopped

    # === RESOURCE LIMITS ===
    deploy:
      resources:
        limits:
          cpus: "2"
          memory: 2G
        reservations:
          cpus: "1"
          memory: 1G

    # === HEALTH CHECK ===
    healthcheck:
      test: ["CMD", "curl", "-f", "https://automateanddeploy.com/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s

    # === PORTS ===
    ports:
      - "8080:8080"

    # === VOLUMES ===
    volumes:
      - openclaw_workspace:/home/openclaw/.openclaw
      - openclaw_cache:/home/openclaw/.cache
      - /var/log/openclaw:/var/log/openclaw

    # === ENVIRONMENT ===
    environment:
      - OPENCLAW_ENV=production
      - LOG_LEVEL=info

    # === LOGGING ===
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"
        labels: "service=openclaw"

    # === NETWORKING ===
    networks:
      - openclaw_net

volumes:
  openclaw_workspace:
    driver: local
  openclaw_cache:
    driver: local

networks:
  openclaw_net:
    driver: bridge

This looks dense, so let’s unpack it piece by piece. Each section solves a specific production problem.

Restart Policies: Keeping Your Container Alive

The restart: unless-stopped policy is your first line of defense against container crashes. Here’s what it does under the hood:

• If the container crashes, Docker automatically restarts it within milliseconds
• If you manually stop it with docker-compose stop, it stays stopped (you’re making an explicit choice)
• If the Docker daemon restarts (maybe your VPS got rebooted), the container comes back up automatically

Think of restart policies as your container’s safety net. In development, a crash is mildly annoying—you run the container again manually. In production, that same crash is a service outage. Your users notice. Your monitoring alerts you. You wake up to angry messages. A smart restart policy means OpenClaw recovers automatically, often before anyone notices the blip.

The key insight is that container crashes happen. Your code might have a subtle bug that only triggers under specific conditions. A dependency might be flaky. Memory pressure might cause unexpected behavior. Rather than pretending crashes won’t happen, we architect for recovery when they do.

You have three other restart options to consider:

restart: no              # Never restart (not for production, but fine for testing)
restart: always          # Always restart, even on daemon boot (too aggressive)
restart: on-failure      # Restart only if exit code != 0 (good for batch jobs)
restart: unless-stopped  # Restart unless manually stopped (best for long-running services)

Use unless-stopped for production long-running services like OpenClaw. It prevents the restart-thrashing scenario where a broken container keeps restarting in a tight loop. Docker has built-in backoff logic—it won’t hammer your server. The delay starts at 100ms and increases exponentially to 1 second between restarts.

This matters more than you might think. In development, you might restart a container dozens of times. In production, you want it to restart once and come back healthy. The exponential backoff gives the system time to recover. If there’s a temporary network hiccup or a brief resource constraint, the next restart attempt has a good chance of succeeding.

Here’s a concrete scenario: imagine OpenClaw crashes due to a temporary database connection timeout. With unless-stopped and exponential backoff, Docker waits 100ms, then tries again. The database has recovered by then, and OpenClaw starts successfully. If you were using no restart policy, your service stays down until someone manually intervenes. If you were using always, you might see rapid restart attempts before the system has time to recover—that’s restart thrashing, and it can mask the underlying problem. The unless-stopped policy with backoff strikes the right balance: aggressive enough to recover quickly from transient failures, but patient enough to wait for systems to stabilize.

Resource Limits: Preventing the Runaway Process

Here’s a common nightmare: OpenClaw’s worker thread encounters a pathological case—maybe a recursively nested configuration file or a dataset with unusual characteristics. It starts consuming all available RAM. Your VPS is using 100% of memory, swapping to disk, and suddenly your entire server is grinding to a halt. Other services stop responding. You lose SSH access. You’re locked out of your own machine.

This isn’t paranoia. This happens. A well-intentioned algorithm that works fine on normal inputs can consume unexpected amounts of memory on edge cases. A memory leak in a dependency. A misconfiguration that causes unbounded buffering. In development, you’re running OpenClaw alone, so there’s nothing else to kill. In production, other critical services share the same hardware. When OpenClaw goes rogue, it takes everything down with it.

Resource limits are your firewall against this scenario. They create hard boundaries that even a buggy application can’t cross.

The deploy.resources section prevents this catastrophic scenario:

deploy:
  resources:
    limits:
      cpus: "2" # Hard cap: max 2 CPU cores
      memory: 2G # Hard cap: max 2GB RAM
    reservations:
      cpus: "1" # Reserve: keep 1 core available
      memory: 1G # Reserve: keep 1GB available

Limits are hard constraints. If OpenClaw tries to exceed them, the container gets killed. It’s brutal but effective. You’ll see an OOMKilled exit status when this happens.

Reservations are soft constraints. The scheduler tries to honor them but won’t prevent you from starting the container if they’re not available. Think of reservations as a preference rather than a guarantee.

For a typical VPS with 4 cores and 8GB RAM, here’s a reasonable configuration:

• Set limits to 50-60% of available resources (prevents runaway processes from taking everything)
• Set reservations to 30-40% (ensures the host OS has breathing room)
• This leaves breathing room for the host OS, monitoring tools, and other services

If OpenClaw is memory-intensive (which it can be when processing large codebases), don’t be afraid to increase these numbers. Just always leave some capacity for the system. A VPS with 8GB of RAM where all 8GB is allocated to containers is a VPS waiting to fail.

Think about resource allocation like managing a household budget. The hard limit (memory: 2G) is like a credit card limit—you can’t exceed it without consequences. The reservation (memory: 1G) is like a safety fund—it’s held aside for emergencies. The leftover capacity (6GB total, 4GB reserved for the OS and other services) is your emergency buffer. If you allocate every penny of your budget with no cushion, one unexpected expense topples everything. The same logic applies here.

Monitor actual usage to calibrate these limits. Use docker stats openclaw-production to watch memory and CPU usage over time. Your production workload will tell you what’s appropriate. Run this command over a full business cycle—a week is ideal. You’ll see peak usage patterns, and you can set limits just above peak with some breathing room for spikes.

Health Checks: Knowing When Things Break

A running container isn’t the same as a working container. Your OpenClaw instance might be up but hung on a broken request. Maybe it’s in a deadlock, or it’s consuming CPU but making no progress. The process is alive, the Docker daemon reports the container as running, but the application is effectively dead. Health checks detect this situation automatically.

This distinction matters enormously in production. When you SSH into a server and run docker ps, you get a list of containers and their status. But that status is shallow. It tells you the Docker daemon is tracking a container and hasn’t received an exit code. It doesn’t tell you whether the application inside is actually working. A container can be “up” for days but completely hung, with no one the wiser. Meanwhile, requests are timing out, users are frustrated, and you don’t know why because the container looks healthy.

Health checks solve this by probing your application periodically. Instead of trusting the process status, they ask: “Is this thing actually working?” If the answer is no for long enough, Docker marks the container unhealthy, and the restart policy kicks in.

healthcheck:
  test: ["CMD", "curl", "-f", "https://automateanddeploy.com/health"]
  interval: 30s # Check every 30 seconds
  timeout: 10s # Fail if no response in 10s
  retries: 3 # Mark unhealthy after 3 failures
  start_period: 40s # Give it 40s to boot before checking

This hits the /health endpoint every 30 seconds. If it fails 3 times in a row (90 seconds of failures), Docker marks the container as unhealthy. You can query this with docker ps:

docker ps | grep openclaw
# CONTAINER ID    STATUS
# abc123def456    Up 5 days (healthy)
# xyz789abc123    Up 2 minutes (starting)
# def456ghi789    Up 1 hour (unhealthy)

Important detail: Health checks alone don’t restart containers. They’re observability. You need the restart policy to actually restart unhealthy containers. Combined, they form a powerful self-healing system.

The start_period is crucial. It tells Docker to wait 40 seconds before starting health checks. This gives the application time to boot up. If you set this too short, Docker might mark a slow-starting container as unhealthy before it even finishes initialization. Give yourself plenty of buffer here—it’s better to wait too long than to have false negatives.

Here’s a common mistake: setting start_period to 10 seconds when the application takes 30 seconds to initialize. Docker starts probing at 10 seconds, the application isn’t ready yet, health check fails, Docker restarts the container. The container starts again, same thing happens. You get stuck in a restart loop, and you spend hours wondering why the logs show repeated crashes. The fix is simple—just give it more time. A 40-second start_period is conservative but reliable. When you’re deploying to production and can’t babysit the logs constantly, conservative wins over aggressive every time.

If your OpenClaw instance doesn’t have a /health endpoint, you can test something simpler:

healthcheck:
  test: ["CMD-SHELL", "ps aux | grep -q openclaw || exit 1"]
  interval: 30s
  timeout: 5s
  retries: 2

This just checks if the process is still running. It’s not ideal (a hung process would still pass), but it’s better than nothing.

Volume Management: Persisting Your Data

Here’s the tricky part: containers are ephemeral. If you don’t use volumes, your workspace, projects, and settings disappear when the container stops. That’s a disaster.

Think of a container as a sandbox that gets cleaned up when you’re done. Everything you create inside the sandbox is temporary. The moment the container stops, the filesystem goes away. All your data, all your configurations, everything—gone. For a development container you’re spinning up and tearing down constantly, that’s fine. It’s even desirable. For production, it’s a nightmare. Your team spent weeks building up projects, workflows, and configurations inside OpenClaw. One bad upgrade, one deployment mistake, and it all evaporates because the data wasn’t persistent.

Volumes solve this by creating persistent storage that outlives the container. Think of a volume as a folder on your VPS’s hard disk that the container can read and write. When the container stops, the folder remains. When a new container starts, it can access the same folder. The data is shared across container lifecycles.

We’re using two Docker-managed volumes:

volumes:
  openclaw_workspace:
    driver: local # Stored on the host, managed by Docker
  openclaw_cache:
    driver: local

And one host-bound mount:

- /var/log/openclaw:/var/log/openclaw

Docker-managed volumes are stored in Docker’s directory (usually /var/lib/docker/volumes) and are automatically backed by the host filesystem. They’re portable and survive container recreation. If you need to move your container to a different host, the volumes move with it. Use these for data you need to keep long-term.

Host-bound mounts give you direct filesystem access. We use this for logs because we want to apply external log rotation and integrate logs with system-wide monitoring tools. This gives you granular control—you can access those log files directly from the host, apply system-wide log management tools, and integrate with centralized logging infrastructure.

Here’s the key difference: if you use a host-bound mount and then delete the container, the files stay on the host. If you use a Docker-managed volume and delete the container, the volume persists (but you need to specifically delete it to free space). This is actually a feature. It means you can experiment with containers, delete them when you’re done tinkering, but your data stays safe. It’s like having a filing cabinet that survives the disposal of the desk on top of it.

The openclaw_workspace volume stores your actual work—projects, code, configurations. Think of this as irreplaceable. The openclaw_cache volume stores transient data that can be regenerated—compiled artifacts, cached downloads, temporary files. If the cache volume disappears, things slow down temporarily until the cache rebuilds, but nothing breaks. The workspace volume is the crown jewels.

To back up your volumes safely:

# Create a backup
docker run --rm -v openclaw_workspace:/data -v $(pwd):/backup \
  alpine tar czf /backup/openclaw-workspace-backup.tar.gz -C /data .

# Restore from backup
docker run --rm -v openclaw_workspace:/data -v $(pwd):/backup \
  alpine tar xzf /backup/openclaw-workspace-backup.tar.gz -C /data

This approach spins up a temporary container, mounts both your volume and your backup directory, and uses tar to create/restore a compressed archive. Store these backups somewhere safe—ideally off your VPS in cloud storage or your local machine.

Why use this approach instead of just copying files? Because the volume might be actively in use by the running OpenClaw container. If you try to copy files directly while OpenClaw is writing to them, you risk corrupted backups—parts of files might be mid-write when you copy them. The temporary container approach is clean: it mounts the volume, reads it as a snapshot, and packages it atomically. You’re not racing with active I/O.

Log Rotation: Preventing Disk Full Disasters

Docker’s default logging can fill up your disk. The logging section fixes this:

logging:
  driver: json-file
  options:
    max-size: "10m" # Rotate when log file hits 10MB
    max-file: "3" # Keep only 3 rotated files (30MB total)
    labels: "service=openclaw"

With these settings, Docker automatically rotates logs and keeps only the last 3 files. When a log file hits 10MB, Docker compresses it and starts a new file. Once you’ve got 3 compressed files, Docker deletes the oldest. This prevents the classic “disk full” disaster at 3 AM when nobody’s awake to handle it.

Why does this matter? Logs grow continuously. Every request OpenClaw handles, every error, every debug statement—it all gets written to the log file. On a moderately active instance, you can generate gigabytes of logs in a week if you’re not careful. The disk fills up silently. One morning you try to deploy an update, and the deployment fails because there’s no disk space. You can’t even SSH in to clean up because system processes can’t write to disk. You’re in a production outage caused by negligence around log management. The fix is so simple—just rotate logs automatically—that when this happens, it’s embarrassing.

Here’s what actually happens on disk:

openclaw.log           # Current log file (active)
openclaw.log.1.gz      # Previous rotated file (compressed)
openclaw.log.2.gz      # Even older rotated file (compressed)
openclaw.log.3.gz      # Oldest rotated file (compressed)
# openclaw.log.4.gz would be deleted automatically

Check log usage:

# See how much space logs are using
du -sh /var/lib/docker/containers/*/

Adjust max-size based on your system. On a heavily-used instance, you might want 50m per file. On a quiet server, 5m is fine. For OpenClaw specifically, it depends on verbosity. In production with LOG_LEVEL=info, you’ll typically see modest log growth. With LOG_LEVEL=debug, expect much more.

The numbers in the example (10MB per file, 3 files) are conservative defaults suitable for most deployments. They keep you safe without being overly aggressive. If your OpenClaw instance is running on a 20GB VPS with multiple other services, 30MB of logs is negligible. If you’re on a 5GB VPS with tight space, maybe drop to 5m per file and 2 files total. Monitor for a week, see how much log data you’re actually generating, and calibrate from there. It’s better to rotate too aggressively (slightly more I/O) than too conservatively (full disk scenario).

The Complete Deployment Workflow

Here’s how to put this all together on your VPS, step by step. This is a practical, linear process that takes about 10 minutes end to end, assuming you already have Docker and Docker Compose installed on your VPS.

Step 1: Create a deploy directory

mkdir -p /opt/openclaw
cd /opt/openclaw

This gives you a dedicated location for your Docker Compose files and any supporting scripts. Keep everything related to OpenClaw in one place.

Step 2: Save the docker-compose.yaml

Create the file with the configuration above. You can also use environment variables for dynamic configuration:

environment:
  - OPENCLAW_ENV=${ENV:-production}
  - LOG_LEVEL=${LOG_LEVEL:-info}

Then deploy with:

ENV=production LOG_LEVEL=info docker-compose up -d

This lets you control configuration without editing the file each time. Perfect for deploying the same setup to different environments.

Step 3: Start and verify

# Start the service
docker-compose up -d

# Check status
docker-compose ps
docker-compose logs -f

# Verify health
docker ps --filter "name=openclaw"

Watch the logs for a minute to ensure everything starts cleanly. You should see initialization messages, then silence. If you see errors, stop here and fix them before proceeding. This is the moment where patience pays off. A common instinct is to immediately move on to the next step, but watching the logs carefully during the first deployment is invaluable. You’ll catch configuration mistakes, missing dependencies, and port conflicts right away. If something’s broken now, it’ll be broken for weeks until you stumble across it in production. Better to catch it during this verification step when it’s fresh and easy to fix.

Step 4: Set up log aggregation (optional but recommended)

Bind mount logs to a central location and monitor them:

tail -f /var/log/openclaw/*.log

Or integrate with a centralized logging system like ELK, Splunk, or Datadog. Logs are your window into what’s happening. Good logging infrastructure is the difference between “something broke” and “something broke and we have no idea why.”

In the early days, just tailing the logs manually is fine. But as you scale or as the container runs longer, you’ll appreciate having logs aggregated in a searchable system. Imagine troubleshooting an issue that happened three weeks ago. If you’re relying on rotated log files that got deleted long ago, you’re out of luck. A centralized log system keeps a historical record. When something breaks, you can search by timestamp, service, error type, and build a complete picture of what happened. It’s the difference between debugging in the dark and debugging with perfect visibility.

Production Hardening: Going Beyond the Basics

Once you’ve got the basics running, here are some advanced considerations that separate amateur deployments from professional ones. These aren’t mandatory on day one, but they become critical as OpenClaw becomes integral to your workflows.

Update strategy: If you’re using image: openclaw:latest, Docker won’t automatically pull new versions. You need to explicitly pull and redeploy:

docker-compose pull
docker-compose up -d  # Only restarts if the image changed

Or better yet, use a CI/CD pipeline to push new images and redeploy automatically. This way, when you push a new version to your image registry, your production deployment automatically picks it up.

Without an update strategy, you’re essentially running static software. Bugs get fixed upstream, but you never pull them. Security patches are released, but you’re still vulnerable. Features improve, but you’re stuck on old behavior. Updating Docker images can be scary because it feels like a “big change,” but it’s actually the opposite—staying on old versions is the risky strategy. Modern development practices assume continuous, small updates. Run them weekly or even daily if possible. It’s much safer than saving up all your updates and doing a big bang upgrade every six months.

Secrets management: Don’t put secrets in docker-compose.yaml. Use environment files that are git-ignored:

# .env file (add to .gitignore)
OPENCLAW_API_KEY=your-secret-key
OPENCLAW_MESSAGING_TOKEN=secret-token

Then in docker-compose.yaml:

environment:
  - OPENCLAW_API_KEY=${OPENCLAW_API_KEY}
  - OPENCLAW_MESSAGING_TOKEN=${OPENCLAW_MESSAGING_TOKEN}

Now secrets stay out of version control. Load the environment before deploying:

set -a
source .env
set +a
docker-compose up -d

This pattern (set -a before sourcing, set +a after) exports all variables from the .env file so Docker Compose can read them. Make absolutely sure .env is in your .gitignore. A common disaster: someone commits an .env file with real secrets, even for a moment. It then lives in your Git history forever, even if you delete it. Assuming you’re using a cloud Git provider (GitHub, GitLab, etc.), that secret has potentially been exposed to their systems. If it’s an API key or password, rotate it immediately.

Monitoring and alerts: Use tools like Prometheus or DataDog to monitor container CPU, memory, and health. Set up alerts for unhealthy containers so you know immediately when something breaks. Proactive monitoring beats reactive fire-fighting.

Here’s the philosophy: you can’t watch logs 24/7. You have a life outside of babysitting your OpenClaw container. So set up automated alerts. Tell your monitoring system: “If this container is unhealthy for more than 5 minutes, send me a message.” Then, whenever something breaks, you get notified before your users do. You can handle it proactively instead of reactively. This is the difference between you discovering a problem and your users discovering it for you.

Networking: The default bridge network is fine for simple setups, but consider:

• Running multiple services (database, cache, queue) alongside OpenClaw—you’ll want service discovery and inter-service networking. The default bridge network makes it harder for containers to communicate reliably.
• Using a reverse proxy (nginx, caddy) for SSL termination—this separates TLS complexity from OpenClaw. OpenClaw talks to nginx over plain HTTP on localhost, and nginx handles all the HTTPS complexity. It’s cleaner and more maintainable.
• Implementing load balancing if you scale to multiple instances—Docker Swarm or Kubernetes handles this elegantly. If you deploy multiple replicas of OpenClaw, you need something to distribute traffic across them fairly.

Backup automation: Schedule daily backups of your volumes so you can recover from data loss:

#!/bin/bash
BACKUP_DIR="/backups/openclaw"
mkdir -p $BACKUP_DIR
DATE=$(date +%Y-%m-%d)

docker run --rm -v openclaw_workspace:/data -v $BACKUP_DIR:/backup \
  alpine tar czf /backup/openclaw-workspace-$DATE.tar.gz -C /data .

Run this via cron:

# crontab -e
0 2 * * * /usr/local/bin/backup-openclaw.sh

This backs up your workspace every day at 2 AM. Keep 30 days of backups locally and push older backups to cloud storage for long-term retention.

Automated backups are insurance. You hope you never need to restore from backup, but when disaster strikes—and it will eventually—you’ll be grateful it exists. A ransomware attack, a bug that corrupts your workspace, accidental deletion, hardware failure—any of these can destroy your data. Without backups, you’re starting over. With them, you roll back to yesterday and lose at most one day of work. The small cost of running a backup script daily is negligible compared to the cost of losing irreplaceable work.

Troubleshooting: Common Issues and Fixes

Problem: Container keeps restarting

docker-compose logs
# Look for error messages
# Increase start_period in healthcheck if it's a slow boot

The most common cause is a health check failing repeatedly. Either your health endpoint is broken, or the container is crashing silently. Increase start_period to give the app more time to boot.

When you see a container restarting every few seconds, the logs are your first clue. Look for error messages, stack traces, or failed assertions. If you see nothing in the logs (which is itself suspicious), the container might be crashing before it even gets a chance to write logs. This is where increasing start_period and adding more verbose logging helps. You want to see what the application is doing during its boot sequence.

Problem: Disk space running out

docker system df
# Check container logs
du -sh /var/lib/docker/containers/*/
# Reduce max-size or max-file in logging config

Logs are the usual culprit. Check how much space they’re consuming and adjust rotation settings.

Problem: Out of memory

docker stats openclaw
# Check if it's exceeding the memory limit
# Increase the limits in deploy.resources if needed

If OpenClaw is hitting the memory limit, you have two choices: increase the limit (if your VPS has room), or optimize the code. Memory leaks are rare but possible.

Problem: Host can’t reach the service

# Check if it's listening
docker exec openclaw-production netstat -tuln | grep 8080
# Verify the port mapping in docker-compose.yaml
# Check host firewall with: sudo ufw status

Make sure the port mapping is correct in your compose file and the firewall isn’t blocking it.

When the service seems to be running but you can’t connect to it, there are three likely culprits: the container isn’t actually listening on the port (check with netstat), the port mapping is misconfigured in docker-compose.yaml (verify the syntax), or the firewall is blocking the port (check with ufw or iptables). Systematically check each one. Most of the time it’s a typo in the compose file or a firewall rule that’s too restrictive.

Real Numbers: What This Actually Costs

On a typical $5/month DigitalOcean droplet with this configuration:

• CPU: Idles at 2-3%, spikes to 40-60% under load
• Memory: Steady ~300MB with 1GB reserved capacity
• Disk: ~500MB for OpenClaw + logs
• Network: Minimal unless you’re transferring large files

You’ll comfortably fit multiple applications alongside OpenClaw with headroom to spare. This isn’t a resource hog.

These numbers assume a typical workload: occasional requests, no massive data processing jobs. If you’re pushing OpenClaw to process terabytes of data or handle thousands of concurrent requests, obviously the math changes. But for normal usage—running projects, processing code, automating tasks—this footprint is reasonable. The 1GB reservation for memory is conservative; many instances never touch that. The CPU utilization reflects the fact that OpenClaw is event-driven: it wakes up, does work, then goes to sleep. It’s not continuously burning CPU like a continuous integration server would.

The financial calculus is interesting: $5/month for the VPS, maybe $1-2/month for backups to cloud storage, plus your time to set it up and monitor it. Compare that to running OpenClaw on your laptop (electricity, wear and tear on hardware) or relying on a commercial cloud service (often much more expensive). A VPS is the economical choice, and with this Docker configuration, it’s production-ready.

The Reliability Payoff

With this configuration in place, here’s what you get:

• Automatic recovery: Container crashes and recovers without your intervention
• Data persistence: Your work survives container restarts
• Resource isolation: OpenClaw can’t crash the entire server
• Operational visibility: Health checks and logs tell you what’s happening
• Scalability: You can evolve this to multi-container deployments

It’s the difference between “this works most of the time” and “this is production-ready.”

What does “production-ready” actually mean? It means you can deploy OpenClaw on a Friday afternoon, go about your weekend without checking your phone every hour, and trust that when you come back Monday morning, it’s still running. The health checks are validating that everything’s working. The logs are recording what happened. Backups ran automatically Saturday night. If something crashed, it recovered on its own. This is the maturity level that separates hobbyist deployments from professional ones. You’re not crossed fingers hoping nothing breaks; you’ve architected for the assumption that things will break, and they’ll recover gracefully.

Next Steps

Start with the docker-compose.yaml we provided. Deploy it, monitor it for a week, and adjust the resource limits and log rotation based on what you see. Production Docker is a skill you learn by doing. There’s no substitute for hands-on experience; reading about it is the starting point, but seeing it in action on your own VPS is where the learning really happens.

Once you’re comfortable here, explore:

• Docker networking for multi-container setups, which becomes relevant when you add databases, caches, or other services
• Volume snapshots for incremental backups, which saves bandwidth and storage compared to full backups
• Container orchestration (Docker Swarm or Kubernetes) if you scale beyond a single instance, though this adds significant complexity
• Automated deployment pipelines with CI/CD tools like GitHub Actions or GitLab CI, which handle image building and deployment automatically

But honestly? For a single OpenClaw instance on a VPS, this configuration is battle-tested and production-ready. It’ll serve you well. Don’t get seduced by complexity. This setup handles the real problems in production: reliability, data persistence, observability, and resource safety. It’s not flashy, but it works. That’s what matters in production.

The best time to set this up is now, at the beginning. It takes maybe an hour total and saves you countless hours of pain later. Future you will be grateful to present you for taking these precautions seriously.