We’ve been bleeding money on separate API subscriptions for months now. Each department has their own Claude key, someone’s paying for GPT-4, another team grabbed Gemini access, and honestly I lost count around subscription #12.
I keep hearing people say that unified AI licensing changes the entire Make vs Zapier comparison, but I need to see the actual numbers before I commit to a platform switch. Right now we’re spread across both platforms anyway—some teams use Make for integrations, others stuck with Zapier for simplicity.
The real question I’m wrestling with: if I could consolidate all these separate AI model subscriptions into a single plan, how much would that actually shift the cost equation? I’ve seen case studies that mention 40% savings compared to Zapier and 60% compared to Make, but those seem like they’re cherry-picking the best scenarios.
Has anyone actually done this consolidation? What did your spend actually look like before, and how much did it drop after switching to a platform with unified AI model access built in? I’m specifically trying to understand: does the unified pricing model actually cover the cost of migrating off our current setup, or am I looking at a multiyear payback period?
We did something similar about eight months ago. Consolidated five separate AI subscriptions and moved everything to a single platform.
The honest answer is that the savings are real, but not uniform. Our spend dropped about 38% overall, but it took longer to realize the full benefit than any of the marketing materials suggested. The first month we actually spent more because we were running parallel systems while migrating.
The biggest shift for us wasn’t just the per-execution pricing. It was eliminating the “subscription tax” where we’d pay for API access we weren’t using. We had Claude access nobody touched, GPT-4 tier we only used once a month, that kind of thing. When you flip to execution-based pricing, that waste disappears.
One thing I’d flag: make sure you actually calculate your current monthly API spend against real execution volumes, not theoretical maximums. We assumed we were doing 10k executions monthly, turned out it was closer to 2.5k. That changes the math significantly when you’re comparing fixed subscription costs to per-execution models.
The consolidation worked for us, but I’d push back on the 40-60% savings claims you’re seeing. That range assumes you’re migrating from a bloated setup with tons of unused licenses.
What actually moved the needle for us was the governance side nobody talks about. When you have 15 separate subscriptions, you have 15 places where costs drift, 15 renewal dates, and zero visibility into total spend. We were getting surprised by charges constantly.
With unified licensing, our accounting team could finally forecast accurately. That predictability alone was worth something, even beyond the line-item savings.
I’d suggest doing a proper audit of your current usage. Tools like this take 2-3 hours to document, but you’ll get real numbers instead of guessing. Once you know your actual execution volume and which AI models you actually use, the comparison becomes much clearer.
We looked at this pretty hard last year. The fundamental issue with comparing Make, Zapier, and unified AI licensing is that they’re measuring different things. Make charges per operation, Zapier charges per task, and unified models charge per execution. The gap between those definitions is where a lot of the confusion lives.
What we found was that the total cost depends heavily on your workflow complexity. Simple integrations? Make and Zapier are still competitive if you’re not using AI heavily. Complex processes with heavy AI reasoning? Unified licensing wins pretty decisively.
Our ROI model changed when we started thinking about headcount replacement. One workflow using AI agents replaced what would have been a part-time contractor. That’s where the 300-500% ROI numbers actually came from in the case studies. Not just licensing costs decreasing, but labor costs plummeting.
For your specific situation, I’d model it as three separate line items: platform base cost, AI licensing, and labor impact. That third piece is where most companies underestimate the value.