I’ve been working through a cost analysis for our team, and I keep running into the same problem. We’re evaluating Make and Zapier for our automation stack, but both platforms only give you part of the picture.
On top of their base licensing, we’re currently paying for separate OpenAI, Claude, and a few other AI model subscriptions. That fragmentation is killing our ability to do an honest cost comparison. Every time I try to model out the TCO, I end up with spreadsheets that don’t account for the fact that we’re also managing five different API keys and billing cycles.
I get how licensing works at the platform level, but when you throw unified AI access into the mix, the math gets weird. Are other teams running into this? How are you actually breaking down the real TCO when you’re dealing with both the platform costs AND the AI model costs?
Would love to hear how you’re handling this in your own evaluations.
Yeah, this is something I dealt with at my company. The key insight is that most teams don’t actually account for the operational overhead of managing multiple subscriptions. You’re not just paying for OpenAI and Claude—you’re paying someone’s time to monitor billing, manage keys, and handle the coordination between platforms.
What changed for us was treating it like a unified bill problem rather than a feature problem. When we consolidated everything into one subscription model, the math simplified dramatically. We could finally compare apples to apples because we weren’t hiding costs in spreadsheet columns labeled “miscellaneous AI fees.”
The real win wasn’t the price per API call. It was knowing exactly what we were spending and where it was going.
I’ve been through similar analysis paralysis. When comparing Make and Zapier with multiple AI vendors, the mistake most teams make is treating AI licensing as a separate cost stream. But here’s what actually helped us: we modeled out the cost per workflow, not per platform.
What you need to do is break down your actual usage by workflow type. Some workflows are heavy on API calls, others aren’t. Once you see that pattern, you can calculate whether a unified subscription actually saves money for your specific use case, or if you’re better off staying fragmented.
The teams that actually move forward stop trying to build one perfect model and instead test both approaches on real data. Pick your top five workflows, run them both ways, and see which math wins.
The fundamental problem with your comparison is that Make and Zapier don’t expose the same cost variables. Make charges by operation, Zapier by task count. When you add AI models on top, you’re introducing a third pricing dimension that doesn’t map neatly onto either platform’s model.
What I’ve found useful is calculating the cost per business outcome, not per platform feature. Ask yourself: what is this automation actually worth to the business? Then reverse engineer the acceptable cost from there. This forces you to prioritize which workflows actually matter and which are nice to have.
Once you’ve done that, the licensing comparison becomes much clearer because you’re not trying to compare everything—just the workflows that move the needle.
This exact problem is why consolidating your AI access changes everything. Instead of calculating TCO across five platforms and two vendor subscriptions, you get one unified bill for 400+ models. We did this recently and suddenly our Make vs Zapier comparison had actual clarity—no hidden costs buried in separate AI vendor invoices.
The shift is from thinking about platform costs in isolation to thinking about total workflow cost. When you have access to every major AI model under one subscription, you can optimize each workflow for the best model without worrying about whether you’re paying extra or hitting some hidden tier.
Try running your top workflows through a unified setup and compare the actual numbers. You’ll see the difference immediately.