I’m new to automation tools and want to experiment with n8n without breaking the bank. I’ve been considering setting it up on my own server, but I’m concerned about security risks when making it accessible online. What are the best practices for securing a self-hosted n8n instance?
Another worry is the cost of API integrations. Since I’ll be testing different workflows, I’m afraid of accidentally running up expensive bills if my API credentials get compromised. Are there ways to set spending limits or use free tiers effectively?
I’ve also heard about different pricing for AI models within n8n. Is it possible to run local AI models instead of relying on cloud services? This could help control costs while learning the platform.
docker compose is probably ur cheapest bet honestly. i run mine on a $5 digital ocean droplet and its been solid for months. just dont expose it directly to the internet - use nginx proxy manager or traefik with ssl certs. for apis, github and discord have generous free tiers that are perfect for learning workflows without worrying about costs.
I’ve been running n8n self-hosted for about 8 months now and can share some practical insights. For security, I put it behind Cloudflare Tunnel which eliminates the need to open ports directly on your router - this has been a game changer for peace of mind. Also make sure to enable two-factor authentication and use strong passwords. Regarding API costs, most services let you set up billing alerts in their dashboards. I learned this the hard way after a runaway workflow hit my OpenAI credits pretty hard in the first month. Now I always set conservative daily limits on any paid APIs before connecting them to n8n. For AI models, you can definitely run local ones using Ollama integration. The performance isn’t quite as good as GPT-4 but it’s perfectly adequate for learning and basic automation tasks. I use a mix of both depending on the workflow complexity. Start with the free tiers and local models while you’re learning, then gradually add paid services as you identify genuine use cases.
One approach that worked well for me was starting with Railway or Render for hosting since they handle the security aspects automatically and have decent free tiers. You avoid the complexity of securing your own server while still getting hands-on experience with n8n workflows. For API management, I recommend creating separate developer accounts or sandbox environments wherever possible - services like Stripe, Twilio, and most payment processors offer test modes that behave exactly like production but won’t charge you real money. This lets you build realistic workflows without financial risk. Regarding local AI models, the hardware requirements can be surprising. I initially tried running everything on an old laptop but found that even smaller models like Llama 7B need substantial RAM to perform reasonably. Consider the electricity costs too if you plan to run models locally 24/7. Sometimes the cloud pricing actually comes out cheaper when you factor in power consumption and hardware depreciation.