Automation is no longer a luxury – but choosing the wrong tool costs you time, money and headaches. If you run a trade business, an agency or a service company in Upper Bavaria, you don't need glossy marketing copy; you need straight answers: What can the tool actually do? What does it cost? And what happens when things don't go according to plan?
We at Softstorm, based in Bad Tölz-Wolfratshausen, use both tools in production – here's our honest take.
According to DADAKAEV LABS (2026), Make with its $9 Core plan is the cheapest option if you want to get started in the cloud. n8n starts at $20 per month in cloud mode according to eesel AI (2026) – but it has one decisive advantage: you can self-host it, completely free of charge.
That sounds technical, but it's highly relevant for many businesses in Upper Bavaria – GDPR being the key word. Anyone who automatically processes customer data, quotes or CRM entries doesn't want that data sitting on American servers. n8n on your own server or a German hosting provider solves this problem cleanly. Make, on the other hand, runs exclusively in the cloud – you have no say in where your data ends up.
Our verdict here: For quick starters or businesses without an IT background, Make is the easier entry point. If you take GDPR compliance seriously or want to save costs in the medium term, n8n is hard to beat.
This is where the two tools start to diverge. Both can connect to external AI services such as the OpenAI API or the Claude API from Anthropic. The difference lies in how deep that integration goes and how flexibly you can shape it.
In Make, adding an AI model is often a pre-built step inside a module – quick to set up, but not very customisable. In n8n you can build AI nodes as full-blown agents: with memory, with their own tools, with decision logic. This isn't marketing – it's a concrete difference when you want, for example, to automatically categorise incoming enquiries, reply to them and log them in your CRM.
Real-world example: A plumbing company from Holzkirchen built a solution with n8n and the OpenAI API in which incoming email enquiries are automatically read, assigned to the right technician and converted into a quote document – without anyone touching the email manually. According to niklasbern.de (2026), repetitive tasks can be automated by 60–80%, at a cost of €500 to €3,000 per workflow and an implementation time of 2 to 5 days. This project landed squarely within that range.
If, on the other hand, you only need simple connections – for example "When a new enquiry comes in via the contact form, add it to the Google Sheet and send me a Slack message" – Make is perfectly fine for that. No overhead, no complexity that no SME needs.
Our strong opinion: Most automation tools are overbought. Businesses in Upper Bavaria pay for features they never use, because nobody asked them the honest question upfront: what do you actually want to automate?
n8n is more powerful – but it demands more from you or your service provider. If you self-host it, you need to apply updates, respond to server outages and occasionally debug complex workflows. That's no big deal when someone has experience with it. But it is a real disadvantage if you want to handle it alone without a technical background.
The way we see it at Softstorm: In 7 out of 10 of our automation projects, the tool wasn't the first bottleneck – it was the lack of clarity about which process should actually be automated. If you start with a fuzzy process, n8n just gives you a fuzzy workflow faster.
Make has less of this problem, because its visual structure forces you to think step by step. For beginners, that's a genuine advantage. For complex scenarios involving loops, error handling and AI integration, Make hits its limits – especially when you're combining multiple data sources, conditional logic and external APIs.
The topic of AI agents also comes into play here: if you want not just simple if-then rules but real decision logic – for instance an agent that independently evaluates and prioritises quotes – then n8n combined with a model like Claude or GPT-4o is the clearly better choice. Make can theoretically do this too, but the implementation is more cumbersome and less stable.
Here's an honest quick summary:
Both tools can deliver meaningful automation for SMEs in Upper Bavaria – but for different levels of maturity and complexity.
If you know what you want to automate but aren't sure which tool fits – or whether AI should play a role at all – a short conversation is often worth more than any comparison article.
We'll take a look at your process, tell you honestly what makes sense and what doesn't – and if we think you can solve it without us, we'll say that too.
We analyze your processes, identify automation potential and show you which digital systems really make sense.
