We’re in the middle of evaluating Make and Zapier for our enterprise automation setup, and the math is getting messy. Right now we’re paying for OpenAI, Claude, Deepseek, and four other AI model subscriptions separately—each with its own contract cycle and billing quirks. On top of that, we need to factor in whether Make or Zapier makes more sense for our workflows.
The problem is every cost calculator I’ve built either simplifies things too much or gets so granular that finance stops listening. We need to compare:
Per-workflow costs in Make vs Zapier
Licensing sprawl across AI models
Maintenance overhead for each platform
How consolidating AI access changes the equation
Has anyone actually built a TCO model that accounts for all these moving parts without losing credibility with the finance team? I’m wondering if there’s a way to show the real impact of reducing subscription chaos while also comparing the platform costs fairly. What does your actual breakdown look like when you’re juggling multiple variables like this?
Yeah, we dealt with this exact mess last year. The key thing I learned is don’t try to make one massive spreadsheet that tracks everything. Instead, break it into three separate models: platform costs (Make or Zapier per workflow), AI licensing costs, and operational overhead.
For the AI side, we found that consolidating into one subscription actually revealed how much we were overpaying. We had 12 separate contracts and were getting charged differently depending on usage tiers that didn’t align. When we moved those to a single subscription, the per-model cost dropped by about 30%.
For Make vs Zapier, we just did apples-to-apples testing of three core workflows we actually run. Tracked setup time, complexity score, and ongoing maintenance needs. That gave us real numbers instead of vendor estimates.
The thing finance actually cared about was the three-year runway. Show them: if consolidation saves X, and platform switch saves Y, what’s the total benefit over 36 months minus migration costs. That’s the number that moved the needle for us.
One thing we didn’t expect: the biggest TCO component wasn’t licensing, it was training and operational overhead. When we switched platforms, we had to retrain people. When we consolidated AI models, support questions actually increased because people didn’t realize everything was available anymore.
If you’re building the model, I’d add a line item for those hidden costs. Most TCO calculators miss that entirely.
The most realistic approach I’ve seen is to build your TCO in phases rather than trying to predict everything upfront. Start with a pilot using three representative workflows on your top choice between Make and Zapier. Track actual costs for 30 days: license fees, setup time in hours, maintenance, integration costs if any.
Then extrapolate that to your full workflow volume and calculate the annual impact. This gives you real data instead of estimates. When we did this, we discovered our actual cost per workflow was 40% higher than vendor projections because we weren’t accounting for the time engineers spent debugging. That changed everything about which platform made financial sense.
Total cost of ownership in this scenario requires separating baseline platform costs from AI licensing, then modeling the operational overhead. The consolidation benefit comes from reducing contract management burden and achieving better per-model pricing through volume. I’d recommend demonstrating ROI using a 24-month window: calculate current state costs across all 15 subscriptions plus platform fees, then project the same workflows on a unified plan with either Make or Zapier. The delta is your business case.
Break it into 3 models: platform fees, ai licensing, and ops overhead. pilot 3 workflows on ur chosen platform first. get real numbers, then extrapolate. thats how we avoided making a mess of it.
Segment your costs: platform, AI, operations. Test with pilot workflows to get real data instead of estimates. This prevents overoptimistic projections.
I’ve been through this exact exercise multiple times, and here’s what actually works: instead of trying to model everything at once, start by testing your actual workflows on both Make and Zapier, then layer in the AI licensing piece separately.
What we found is that when you consolidate AI access into a single subscription, the math changes dramatically. You eliminate per-model contract overhead, get better volume pricing, and honestly, your finance team stops having to track 15 different vendor agreements. We cut our AI subscription footprint from twelve contracts down to one and saw immediate savings.
But here’s the thing—the real win isn’t just the license consolidation. It’s that you can now prototype and compare workflows much faster without tracking which AI models are available where. We built our TCO by running actual workflows side-by-side and measuring time-to-value, implementation cost, and maintenance overhead. That gave us credible numbers.
Latenode specifically helped us here because we could spin up prototypes quickly using their unified AI subscription access, which let us get real cost data without commissioning multiple proof of concepts. The platform’s AI Copilot also converted our workflow descriptions into runnable automations, which cut our evaluation time by half.