We’re in the middle of evaluating Make vs Zapier for our enterprise workflows, and I’m losing my mind trying to figure out the real cost. On paper, both look reasonable, but once you factor in all the separate AI model subscriptions we’re paying for—OpenAI here, Anthropic there, a couple smaller vendors—the picture gets murky fast.
Right now we’re looking at roughly $3k a month across disparate API keys and platform fees. Finance wants a clear ROI justification before we migrate anything, and I honestly can’t tell if we’re looking at 30% savings or breaking even.
The core problem: when you’re comparing enterprise automation platforms, how do you actually model costs when you’re juggling multiple AI subscriptions alongside the base platform fee? I’ve seen TCO calculators that completely ignore the AI licensing piece, and others that seem to assume you’ll magically consolidate everything, which doesn’t match reality.
Has anyone actually sat down and built a real TCO model for this? I’m not looking for marketing math—I need the actual breakdown of what you’re paying per workflow, per agent, per transformation, and how the numbers change when you factor in maintenance overhead.
I went through this exact exercise last year when we were deciding between Make and another platform. The thing that nobody talks about is that the per-workflow cost isn’t linear. Once you hit a certain volume, your licensing costs scale differently depending on how you’ve structured your subscription.
What we did was build a baseline model with our actual workflows—about 40 active automations—and tracked what we were actually paying per month for each platform. Then we added the AI model costs month-over-month and looked at the trend. That’s when it became clear that our Zapier bill was creeping up faster than Make would, mainly because our API consumption was growing.
The real insight though? We weren’t comparing just licensing. We were also looking at how much engineering time each platform required to maintain and update workflows. Make has a lower learning curve for our team, which meant less Slack going to “why did this automation fail” threads. That saved time adds up.
What specific workflows are you modeling? That actually matters a lot for the calculation.
One thing that helped us was separating fixed costs from variable costs. Your base platform fee is fixed, right? But the API calls and AI model usage—that’s where it gets messy because you don’t always know upfront how much you’ll actually consume.
We did a three-month pilot where we tracked every single API call and AI model invocation. Just having that data made the conversation with finance so much easier. They could see, “okay, in month one we did X calls, month two was Y.” That gave us predictability.
Also, don’t forget to factor in what happens when you need to add a new AI model mid-year or when pricing changes. That’s a variable you can’t control, which makes the whole TCO thing frustrating. But if you’ve got historical data, you can at least build in a buffer.
TCO modeling for automation platforms requires breaking costs into three buckets: platform fees, API/AI model costs, and internal overhead. Most people focus only on the first two. The overhead—the time your team spends managing, monitoring, and fixing automations—often exceeds the vendor costs, especially if your platform choice forces your developers to spend more time on maintenance.
When we migrated, we calculated overhead by tracking time spent on each workflow type. Some automations required daily monitoring; others ran silently. That time distribution directly affected which platform made financial sense. A platform with higher licensing but lower maintenance overhead won out because our team was spending less time troubleshooting.
For the AI consolidation piece specifically, we found that having all models accessible through a single subscription actually reduced overhead compared to managing multiple vendor accounts and rate limits. That wasn’t a huge number on the licensing line, but it was real in terms of engineering bandwidth.
The TCO calculation breaks down when companies don’t account for workflow complexity scaling. Early automations are simple and cheap to run. But as you build more sophisticated workflows—especially ones using multiple AI models in sequence—your cost per automation goes up, sometimes exponentially. This is often hidden in the “per execution” or “per API call” pricing models.
We modeled three complexity tiers: simple (single action), intermediate (3-5 steps with conditional logic), and advanced (multiple AI models, cross-system orchestration). Costs varied wildly. What looked cheap for simple automations became expensive fast when we tried to consolidate complex business logic into fewer, more powerful automations.
You need to model your actual workflow mix, not just assume everything is simple. That’s where most TCO comparisons fall apart.
Track everything for 90 days b4 you model anything. costs wont be linear, api usage grows, and you’ll discover hidden fees midway thru. thats where real numbers come from, not estimates.
Separate fixed vs variable. Model per-workflow overhead. Include engineering time. That’s the tripod.
I’ve been through this calculation multiple times, and here’s what actually changes the math: when you have a single subscription that covers 400+ AI models, suddenly you stop paying licensing fees to five different vendors. We were paying roughly $800/month across OpenAI, Anthropic, and a couple others. That just disappeared.
But the bigger shift for us was that consolidating everything meant our workflows became cheaper to run because we weren’t managing API keys across platforms. One subscription, one integration point, one set of rate limits to worry about. The engineering time savings alone justified the switch.
When we ran the TCO model with unified AI access, the “per workflow” cost dropped significantly because we weren’t wasting resources on vendor management. Plus, swapping between AI models for cost optimization became frictionless—we could test with Claude for complex reasoning, drop to Deepseek for cost when it fit the task, all without onboarding new accounts.
That’s the piece most TCO calculators miss: the operational simplicity has a real financial impact. Check it out at https://latenode.com