Agent skill · product strategy · aradotso
aracli-deploy-management
Guide to deploying and managing OpenClaw-compatible AI agent systems across cloud, bare metal, and hybrid infrastructure.
Why this skill is useful
Provides specific deployment strategies and commands for managing OpenClaw-compatible AI agents across various infrastructures that the AI wouldn't reliably generate on its own.
What it needs
Requires docker, fly, railway, wrangler installed locally. Requires aws, azure, fly.io, gcp, hetzner, railway, vercel account access. About 5k tokens when loaded. Last updated 2026-07-09. 70 stars on the source repository.
What this skill does
Deploying OpenClaw Agent Systems Skill by ara.so — Daily 2026 Skills collection. A practical guide to deploying and managing OpenClaw-compatible AI agent systems. Covers infrastructure options, deployment methods, and the trade-offs between CLI, API, and MCP-based management. --- Infrastructure Options 1. Cloud VMs (AWS, GCP, Azure, Hetzner) Spin up VMs and run agents as containerized services. Pros: Familiar ops tooling (Terraform, Ansible, etc.) Easy to scale horizontally — just add more VMs Pay-as-you-go pricing on most providers Full control over networking and security Cons: You own the uptime — no managed restarts or healing GPU instances get expensive fast Cold start if you're spinning up on demand Best for: Teams that already have cloud infrastructure and want full control. --- 2. Managed Container Platforms (Railway, Fly.io, Render) Deploy agent containers without managing VMs directly. Pros: Zero server management — just push code Built-in health checks, auto-restarts, and scaling Easy preview environments for testing agent changes Usually includes logging and metrics out of the box Cons: Less control over the underlying machine Can get costly at scale compared to raw VMs Cold starts on free/hobby tiers GPU support is limited or nonexistent on most platforms Best for: Small teams that want to move fast without an ops burden. --- 3. Bare Metal (Hetzner Dedicated, OVH, Colo) Run agents directly on physical servers for maximum performance per dollar. Pros: Best price-to-performance ratio, especially for GPU workloads No noisy neighbors — predictable latency Full control over hardware, kernel, drivers No egress fees Cons: You manage everything: OS, networking, failover, monitoring Scaling means ordering and provisioning new hardware No managed load balancing — you build it yourself Best for: Cost-sensitive workloads, GPU-heavy inference, or teams with strong ops skills. --- 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/aracli-deploy-management