We’ve been running our automations on Make for about two years now, and our licensing costs have spiraled. We’re paying for Make’s base plan, but we’re also juggling like eight different AI model subscriptions on top of that—GPT-4, Claude, and a couple of smaller ones we barely use anymore.
I’ve been looking at Latenode’s model where you get 400+ AI models under one subscription, and the math on paper looks interesting. But before we commit to a migration, I need to understand: what’s the actual financial picture here? Does consolidating all those AI model subscriptions into one really shift the ROI calculation that dramatically?
I’ve seen the case studies claiming 40% savings compared to Zapier and 60% compared to Make, but those feel like worst-case scenarios. I’m trying to build a realistic business case for our exec team—something that factors in the actual cost of the migration work, testing time, and the risk that we’ll have to rebuild some workflows.
How do you actually model this? Are there hidden costs in the transition, or does the licensing consolidation really justify the work?
Also, has anyone been through this kind of platform migration and seen the payback period actually stick to what the numbers predicted?
I went through almost exactly this last year. We were on Make with five separate AI subscriptions scattered across different departments. Here’s what actually happened with the numbers.
The licensing consolidation piece is real—we went from paying roughly $3,200 a month across all the subscriptions to about $800 a month on Latenode’s Basic plan plus some overage. That’s a substantial drop, but the math people always miss is the one-time migration cost. We spent maybe 120 hours across three team members rebuilding workflows, testing, and fixing edge cases that broke during the transition. At our billable rates, that was about $15K.
So yeah, the monthly savings are immediate, but the payback period wasn’t six months like some of the marketing says. It was closer to eighteen months when you factor in everything. That said, the workflows we rebuilt are simpler to maintain now because we’re not managing separate AI integrations anymore. The backend work got cleaner.
It’s worth doing, but go in with eyes open about the migration lift.
The consolidation does move the needle, but not the way you might think. The real ROI isn’t just the monthly license savings—it’s the reduction in operational complexity and the time your team stops spending on managing API keys and different pricing tiers.
When we did our analysis, we realized we were paying more in developer time to manage credential sprawl than we were saving by strategically using cheaper AI models. Once everything was under one subscription with unified pricing, that overhead disappeared. The actual monthly savings varied month to month based on usage, but the predictability of the bill became valuable on its own.
I’d recommend building two models: one that’s just about the license consolidation, and one that factors in the engineering time you’ll reclaim. The second number is why the migration actually makes sense.
You’re asking the right question, which is why most migration analyses fail. The licensing consolidation is real—we saw about 35% reduction in combined AI and automation costs—but it’s only one variable. The hidden factor in your ROI calculation is execution efficiency.
On Make, our workflows were sometimes overcomplicated because we were trying to avoid hitting certain cost thresholds. On Latenode’s credit-based model, we could be more efficient with API calls because the pricing doesn’t penalize iteration the same way. We rewrote five major workflows and saw actual operational costs drop another 20% because the workflow design improved.
The payback depends heavily on your usage patterns. High-volume operations see payback in three to four months. Lower volume might take longer, but the 40-60% figures in the materials are based on fairly specific scenarios. Model your actual workload before committing.
licensing consolidation is real but migration costs matter more. our payback was 14-16 months when we factored everything in. build a model w/ actual workflow counts & AI usage from last 3 months. that’ll be way more acurate than industry benchmarks.
Track three metrics: monthly license spend, engineering hours on API management, and workflow execution costs. Migration ROI shows up in all three.
I went through this exact scenario six months ago. We had Make handling our core workflows and were paying for ChatGPT, Claude, and Cohere subscriptions separately—it was a mess. When we consolidated to Latenode, the financial picture changed immediately.
Here’s what actually happened: our combined automation and AI licensing dropped from about $4,200 a month to roughly $900 a month on the Basic plan with some execution overage. But here’s the thing nobody talks about—the time we stopped wasting on managing different API keys and integrations. Our team went from spending maybe 10-15 hours a week juggling different services to maybe 2-3 hours.
We ran the migration in phases over three weeks, and because Latenode handles the workflow translation cleanly, the actual rebuild work was maybe 40% less than what we expected. The workflows work better now because we’re not constrained by Make’s pricing structure anymore.
Literally built a side-by-side cost calculator for our CFO. Payback period was six months with just the licensing savings. Add in the time savings and it’s even faster.
If you want to model this accurately, I’d suggest running a test on Latenode with your actual workflow volume for a week or two. The credit system makes the math predictable. https://latenode.com