Most businesses have someone, or several people, copying data from one tool into another every day. Automating that is one of the fastest wins available. The hard part is choosing how.
Choose badly and you get a fragile workflow nobody understands, or a monthly bill that grows every time your business does. Here's how we decide, in plain English. We build with all four options, so we don't have a favourite.
The four options
1Zapier: the quickest way to connect two tools
Zapier has the largest library of ready-made connections, and it's the easiest for a non-technical team to read and edit. It shines at simple, straight-line jobs: when a form is submitted, add a row to a sheet and send an email.
It charges by the number of tasks it runs, so costs climb with volume. Once a workflow runs thousands of times a day, it's worth looking at the other options.
2Make: more control, still no code
Make shows each workflow as a visual map, which makes branches, loops and reshaping data much easier to build and follow. It's a good middle ground for processes with several steps, and for many of them it costs less than Zapier at the same volume.
The trade-off is a steeper learning curve if your team wants to edit workflows themselves.
3n8n: power, and your data can stay with you
n8n can run on your own server, so customer data doesn't pass through another company, and you aren't paying for every run. It has proper building blocks for AI agents, and when a no-code step isn't enough you can drop into a code step written in JavaScript or Python.
The catch: running it yourself means someone has to handle updates, backups and monitoring. There's also a hosted version if you'd rather not.
4Custom Python: when it's really part of your product
Write code when the logic is complex, when volume is high or speed matters, when the workflow is part of what your customers pay for, or when you need a tool that none of the platforms connect to well.
It's the most flexible option and usually the cheapest to run at scale. The trade-off is that changing it needs a developer.
A quick way to decide
| If this sounds like you | Start with |
|---|---|
| Simple "when this happens, do that" between popular apps | Zapier |
| Several steps, branches, or data that needs reshaping | Make or n8n |
| Sensitive customer data, or thousands of runs a day | n8n on your own server |
| AI steps that read, sort or draft messages | n8n, or custom code for heavy use |
| Core product logic, or tools with poor integrations | Custom Python |
| Your team must be able to edit it without help | Zapier or Make |
Mixing is normal. A common setup is n8n or Make for the day-to-day flow, with a small piece of custom code for the one step no tool handles well.
Whatever you choose
- Start with the task that eats the most hours, not the most interesting one.
- Log every run and get an alert when one fails. A silent failure is worse than no automation, because everyone assumes it worked.
- Keep a human approval step for anything that moves money or messages customers, at least at first.
- Write down what each workflow does and who owns it, so it survives the person who built it leaving.
- Put the accounts in your company's name, not a freelancer's.
Not sure where to start? In our 48-hour automation audit you walk us through your week on a short call. We come back with the workflows worth automating first, the tool we'd use for each and roughly how much time each one saves.