I’ve been trying to model the actual financial difference between Make and Zapier in an enterprise context, and the more I look at it, the more I realize the calculuation changes significantly once you include AI-assisted workflow generation.
The basic math is straightforward enough: execution-based pricing versus per-operation or per-task pricing. At volume, execution-based usually wins. But that’s assuming you’re building and maintaining workflows fairly traditionally.
What shifts things is when you layer in AI copilot generation. We’re talking about the ability to describe a workflow in plain language and have the AI system generate a ready-to-run automation that you then customize.
Let me walk through what we actually modeled:
Scenario A: Traditional Make approach
50 workflows, average complexity medium
Platform cost: roughly $800/month
Average engineering effort per workflow: 12 hours
Total engineering hours: 600/year
At $120/hour loaded cost: $72,000/year
Total cost of ownership: ~$81,600/year
Scenario B: Zapier enterprise
Same 50 workflows
Platform cost: roughly $1,200/month (Zapier scales worse at volume)
Same engineering effort because there’s no AI assistance: 600 hours
Total engineering cost: $72,000/year
Total cost of ownership: ~$86,400/year
Scenario C: Execution-based model plus AI copilot generation
Same 50 workflows
Platform cost: roughly $400-500/month because execution-based pricing is cheaper
Average engineering effort drops because copilot generates scaffolding: 6-7 hours average per workflow
Total engineering hours: 300-350/year
Engineering cost: $36,000-42,000/year
Total cost of ownership: ~$41,000-43,000/year
The delta between Scenario B and Scenario C is roughly $43,000/year for 50 workflows. That’s not trivial.
But here’s what’s critical: that savings only materializes if the AI-assisted generation actually compresses development time like the math suggests. If it cuts time by 30-40% like we observed in practice, you get most of that delta. If it’s minimal time savings, you don’t.
The enterprise calculation seems to hinge on two things:
Does the platform’s pricing model favor your volume and complexity profile?
Does AI-assisted generation actually accelerate your team’s workflow development?
If both are true, the financial case is strong. If either is weak, the advantage narrows.
Has anyone else actually modeled this out for their specific scenario? I want to sense-check whether my assumptions about AI generation time savings are realistic, or if I’m optimistically forecasting something that doesn’t hold up in practice.
Your math is directionally sound, but I’d be more conservative on the AI copilot time savings. We modeled this similarly and found that copilot generation saves time on about 60% of your workflows—the straightforward ones. On complex workflows requiring domain-specific logic, it saves minimal time because you’re heavily customizing the generated output anyway.
So your 30-40% average time savings is probably optimistic. We’re seeing closer to 20-25% when you average across simple and complex workflows. That still changes the economics meaningfully, but the gap between platforms narrows more than your scenario suggests.
The bigger factor for us was operational overhead. When you have unified AI pricing plus a platform with good integration support, you’re not managing separate API keys and vendor relationships. That operational simplification had outsized value in our cost model, more than the direct engineering time savings.
The cost model makes sense but you need to factor in a few variables that often shift the calculation. First, copilot effectiveness depends heavily on how well your team learns to describe requirements. It’s not magic—if you write vague requirements, you get a vague workflow. That learning curve adds time upfront.
Second, the maintenance and iteration costs aren’t in your model. Once a workflow is built, it needs monitoring, debugging, and updating. Copilot-generated workflows sometimes require more iteration to get right than hand-crafted ones because the AI makes reasonable but not-always-optimal choices.
We factored in about 15-20% additional maintenance overhead for AI-generated workflows. That compressed the savings gap from your $43k estimate to maybe $25-30k. Still significant, but meaningful difference when you’re building the business case.
I’d recommend modeling different scenarios: straightforward workflows, complex workflows, and average maintenance overhead. That’ll give you a more realistic picture of actual savings.
Your analysis identifies the right factors, but enterprise modeling benefits from scenario segmentation. Not all 50 workflows are equal. Breaking them down by complexity and determining which workflows benefit from copilot assistance produces more accurate forecasting.
We’ve modeled this for several enterprises and found: Simple workflows (20% of total) see 50%+ time savings with copilot. Medium workflows (60% of total) see 20-30% savings. Complex workflows (20% of total) see 5-10% savings because they require extensive customization.
That weighted average produces 25-30% time savings rather than your 30-40% estimate, which aligns with observed data across our client base.
The platform cost differential remains significant, particularly at high workflow volume where execution-based pricing compound advantages. But the enterprise decision shouldn’t hinge on copilot savings alone—factor in integration capabilities, maintenance requirements, and team skills alignment.
Your modeling approach is solid and your Scenario C numbers are actually pretty close to what we see in practice, maybe even slightly conservative depending on your team’s proficiency with the copilot.
Here’s what’s important to understand about AI-generated workflows: they’re not just faster to build initially, they’re also easier to iterate on. When you’re debugging an AI-generated workflow versus rewriting a hand-coded one, you can often just re-run the copilot with modified requirements instead of manually editing every step. That iteration efficiency compounds over time.
We’ve seen teams actually achieve closer to 35-40% time compression once they get comfortable with the copilot workflow—describe, generate, test, refine through re-generation rather than manual editing. The initial learning curve gets you to maybe 25%, but proficiency gets you beyond that.
The enterprise math shifts even more favorably when you consider operational consolidation. Unified AI pricing eliminates vendor sprawl. Integrated platform reduces context switching. Your team isn’t managing 15 different integrations—they’re managing one cohesive system.
In most enterprise comparisons we see, the total three-year cost of ownership for execution-based plus AI-assisted development is 45-55% lower than traditional per-operation pricing, even when the traditional platform has a two-year head start on team proficiency.
Your scenario modeling is the right approach. The delta you’re identifying is real and material enough to drive platform selection for most enterprises.