We’re trying to make a quick call between Make and Zapier for enterprise, and part of the pitch from vendors is that you can prototype critical workflows fast using ready-to-use templates. That sounds perfect for what we need—faster time to evaluation, clearer picture of whether a platform fits our needs.
But I’m skeptical about how much time we’ll actually save. We’ve been on enough automation platforms to know that templates usually get you 50% of the way there, and then customization hell begins. The question is whether the time we save on initial setup is worth the time we lose on customization.
We need to compare:
How close are these templates to what we actually need?
How much time does customization actually take?
Can we get a real side-by-side cost and speed comparison using templates for both Make and Zapier?
I’m not looking for a sales pitch. I’m looking for realistic expectations from people who’ve actually done this. How long did prototyping actually take? Did the templates match your use cases or did you end up rebuilding most of it?
What workflows would be good canaries for testing template quality—ones simple enough to run fast but complex enough to show real differences between platforms?
We used templates from both platforms to prototype almost the exact same workflow—customer data sync into a CRM. The template part took about an hour for each. But here’s where the time diverged.
Make’s template assumed our customer data came from a web form. Ours comes from an API and two different databases. So we had to rip that out and rebuild the input layer. That was another six hours.
Zapier’s template was more generic about data sources, so we could plug our stuff in with less restructuring. But the output side—how we wanted to deduplicate and handle failures—required customization on both.
Final time for both to production: about 20 hours each when you include testing. The template saved us from writing the basic orchestration, but when your setup is non-standard, you still do most of the real work. The time difference came down to how flexible each platform’s template structure was, not the baseline time savings.
The template that actually worked well for us was the simple one—new record triggers notification workflow. That literally worked out of the box. The complex templates? Those always needed rebuilding because templates are built for generic use cases and enterprise workflows are usually weird in specific ways.
What helped us compare platforms wasn’t using the templates as a final solution. It was using them to understand how each platform thinks about workflow structure. Make structures things one way, Zapier another. That structural difference matters way more than the template itself for long-term maintenance.
We tested templates for these three workflows: automated invoice processing, customer onboarding, and internal ticket routing. Results were completely different per workflow. Invoice processing had decent templates on both sides but still needed custom OCR integration. Onboarding was too specific to our company to get value from any template—we rebuilt it. Ticket routing was the winner: the templates were actually 90% functional and needed minimal tweaking.
The canary test probably shouldn’t be which template exists, but which workflow is most generic. If you’re looking to compare platforms fairly, find something with no weird integrations or custom business logic. Something like data sync between two standard systems. That’s where templates show their true value.
The real leverage with templates isn’t that they work out of the box. It’s that they show you how the platform handles complex logic. When you’re evaluating Make versus Zapier, running the same template on both platforms reveals how each handles conditional routing, error management, and data transformation.
Where this gets even more useful is templates that showcase orchestrating multiple agents or AI models. A template that shows how to run multiple workflows in parallel, coordinated by a central supervisor agent, tells you more about platform scalability than anything else.
For enterprise comparison, look for templates that involve multiple data sources, conditional logic, and error handling. Simple templates won’t show you where platforms diverge. The complex ones reveal whether a platform can handle your real-world messiness.