A lot of the automations or workflow experiments on digitising events have been orchestrated using make.com. And it works well; however, one of the things that goes against it, especially when you are building with AI (where you will more often than not have to run through many loops and iterations), is its pricing model. Make.com charges one credit for each module that's executed, and we were burning through our credits at an alarming rate, so we wanted to look for a different alternative.
We'd always been aware of n8n and resisted using that in our work because it is much more developer-centric, not just in how it works but in how it's presented. You hit code pretty quickly, or what looks like code.
However the credit munching behaviour of Make.com tipped us towards N8n, or at the very least using a hybrid model where we use both.
Applying it in practice, we quickly ran into some specific nuances on n8n, which is true of any platform. We felt it would be useful to share some of the best practices that we found through our own research, AI research, and building on n8n to replicate / transfer relatively complex Make.com workflows into n8n.
Why? Because following these little bits of advice will save you a tonne of time down the road.