We’re in the middle of evaluating whether to consolidate our automation stack. Right now we’ve got Make handling most workflows, but we’re also juggling separate API subscriptions for different AI models—OpenAI here, Anthropic there. It’s a mess from a cost perspective.
The thing is, when I tried modeling the total cost of ownership for a migration, I kept running into the same problem. Finance wanted to see exactly where every dollar goes. With Make, that’s straightforward—you pay per operation. But when you’re comparing that to a platform where you get 400+ AI models in one subscription, the financial picture gets fuzzy fast.
I pulled some numbers from case studies showing 40% savings compared to Zapier and 60% savings compared to Make for high-volume operations, which is interesting. But our finance team isn’t convinced those savings are real for our specific workflows. They want to see our actual costs mapped out.
Has anyone actually built a convincing cost comparison for their leadership team when you’re switching from per-operation pricing to unified AI model pricing? I’m trying to figure out if the consolidation actually delivers the ROI people claim, or if we’re just trading one set of costs for another.
We went through this exact exercise last year. The key thing that finally worked with our finance team was showing them the per-workflow cost over a year, not just the platform fees.
What we did was take five of our most-run workflows and calculated the actual cost in Make—operations, execution time, all of it. Then we modeled what those same workflows would cost on a unified subscription. The difference was actually significant once we included the AI model costs we were paying separately.
The thing that made finance believe us was when we stopped trying to make it theoretical and just showed them the spreadsheet for Q3’s actual volume. Real data from our system, not vendor case studies.
One more thing—we maintained parallel environments for two weeks before we fully migrated. That let us validate the cost assumptions before we committed.
The issue with comparing itemized costs is that you’re looking at different cost structures. Make charges per operation, but when you factor in the overhead of managing separate AI subscriptions, you need to account for the time your team spends managing those integrations. From my experience, the real savings often come from operational efficiency, not just raw pricing. We found that workflows running on a unified platform required less maintenance and fewer error handlers because the platform handled edge cases better. That labor cost reduction was actually bigger than the subscription savings. You might want to quantify what your team currently spends managing multiple API connections and subscriptions—that’s usually where the hidden cost advantage exists.
Show them real historical data. Pull Make bills for last 3 months, calc actual per-workflow costs. Then model same workflows unified. Concrete numbers beat projections every time. Finance will actually listen if its their own data.
The cleanest way to get finance aligned is with concrete benchmarking. Export your Make workflow execution history, then model those same workflows against unified AI subscription pricing. What most teams discover is the per-operation model in Make compounds—you’re paying for each API call, each transformation, each pause. With a time-based model like Latenode offers, you’re paying for 30 seconds of execution regardless of how many operations happen in that window. That’s usually where the 40-60% savings appear in real workflows.
We’ve seen it reduce costs from thousands monthly down to hundreds because complex workflows that were expensive in Make suddenly become affordable. The unified 400+ AI model subscription also eliminates the fragmentation of paying OpenAI, Anthropic, and others separately.
Build your ROI case with your actual workflows, not industry benchmarks. That’s what breaks through financial resistance.