We’ve been running the numbers on consolidating our AI model subscriptions, and it’s getting messy. Right now we’re paying for separate OpenAI, Claude, and a few other model accounts—all on top of what we’re already spending on Make for our workflows. The licensing spreadsheet is basically unmanageable at this point.
I found some case studies showing that consolidation can cut costs significantly, but I’m struggling to model what the actual savings would be if we moved everything to a single subscription. Does consolidating 300+ AI models into one plan actually change the financial equation when you’re already comparing Make’s pricing against Zapier’s?
We’re trying to figure out if the TCO math is actually different when you factor in unified AI licensing as a separate variable. The numbers suggest maybe 40-60% savings depending on execution volume, but I want to understand how that actually translates to our specific situation.
Has anyone here actually done this calculation? What changed in your cost model when you consolidated, and how did it affect your platform decision?
I dealt with this exact problem last year. We had seven different AI subscriptions bleeding money, and the consolidation math wasn’t straightforward because we were also evaluating platforms.
What actually happened: we mapped out our execution patterns first—how often we ran each workflow, what models we were actually using. Turns out we were paying for capacity we never touched. Once we consolidated, the real savings came from execution-based pricing instead of per-operation charges. Make’s model would’ve cost us about 60% more for the same volume.
The key is breaking down your actual usage. Don’t just look at plan prices. Track what you’re running and for how long. That’s where the math clicked for us.
The consolidation absolutely changes the equation, but not in a simple way. I’ve seen teams save 40-60% as mentioned, but that number only matters if your current spend is actually high. Start by calculating your true execution costs across all platforms. Most teams underestimate usage when comparing apples to apples. When we did this analysis, we found that switching to time-based pricing rather than operation-based made the biggest difference. The unified AI subscription became almost secondary to that shift.
Your approach is solid, but consider this: the consolidation calculation needs to account for switching costs and learning overhead. Moving all your AI integrations at once can introduce errors that cost time and money. What I’ve observed is that teams who tailor their model selection per workflow actually see better ROI than those who optimize for a single subscription. Consolidation is valuable when it’s paired with workflow optimization, not just as a licensing decision.
The consolidation math becomes clear when you stop treating AI licensing as separate from platform costs. We ran this exact scenario, and the breakthrough came when we mapped execution patterns across both platforms.
Here’s what changed: instead of paying per operation (Make’s model) or per task (Zapier’s model), execution-based pricing meant we could run complex workflows with multiple AI models for a fraction of the cost. A workflow that cost us $200+ monthly on Make dropped to around $40 with consolidated licensing because we paid for actual runtime, not individual operations.
The key insight is that consolidation works best when the platform itself supports it natively. We have 300+ AI models accessible through one subscription now, and the financial picture shifted completely. No more juggling keys, no API management overhead, and the cost per workflow execution became predictable.
If you’re serious about modeling this, calculate your current per-operation costs across all your AI subscriptions, then compare that to execution-based pricing. The difference is usually significant enough to justify migration.