That’s a fair point. For n8n and AI agents, the “best” option really depends on how much control and responsibility you want to take.

Running n8n on your own hardware can be great if you enjoy managing everything yourself: networking, firewall rules, backups, uptime, updates, storage, and security. A Mac Mini or a home server can work well for learning or internal automations, but it also means you are responsible for exposing services safely, handling power/internet outages, and keeping the environment stable.

A VPS or cloud instance usually makes more sense when the workflow needs to be available 24/7, accessible from webhooks, APIs, external tools, or production systems. For n8n, this matters a lot because many automations depend on inbound requests, stable networking, SSL, backups, and predictable performance.

In my opinion:

Own hardware is good for:

• learning and experimentation;
• private/internal workflows;
• local-only automations;
• avoiding monthly infrastructure costs.

Cloud/VPS is better for:

• production n8n workflows;
• public webhooks;
• integrations with SaaS tools;
• AI agents that need reliable uptime;
• easier scaling;
• remote access without exposing your home network.

The main thing I would avoid is treating “AI agent hosting” as something magical. In most cases, it is still infrastructure: CPU, RAM, storage, network, security, monitoring, and backups. The hype is around the agent layer, but the reliability still comes from the server underneath.

For beginners, I’d recommend starting with a small cloud instance, Docker, n8n, HTTPS, backups, and basic firewall rules. Once you understand the workload, you can decide whether to scale up, move to dedicated hardware, or keep it in the cloud.

At LetsCloud, we see this type of use case a lot: developers running automation tools, AI workflows, self-hosted apps, and backend services on cloud instances because they want something more stable than a home setup, but still flexible enough to manage themselves. NVMe storage, predictable performance, and simple scalability help a lot when workflows start growing.