I’m building an ROI model for our exec team to justify potentially switching platforms, and I’m hitting a wall trying to make the numbers comparable.
The Make vs Zapier math is straightforward on the surface—tier pricing, number of tasks, maybe some automation credits. But we also have to account for the fact that we’re currently paying for separate AI integrations that we could consolidate. That changes the financial picture, but I don’t have a standard framework for comparing it.
Here’s the problem: if we stay with Make, we keep paying for OpenAI and Claude subscriptions separately, plus Make’s subscription. If we switch to Zapier, similar situation. But if we move to a platform with unified AI pricing, the calculation is completely different—one subscription covers both the automation platform and the AI models.
I know the total cost difference, but I’m struggling to present this in a way that isolates how much of the savings is from the platform switch versus how much is from consolidating AI licensing. My CFO wants to see it broken down.
How have other people handled this? Is there a standard way to model this that actually holds up to scrutiny?
I just went through this exercise with our finance team, and the way that actually landed was separating it into three distinct line items:
First, platform costs (Make tier or Zapier tier or whatever you’re comparing). Second, AI model subscriptions as their own category. Third, operational overhead—the labor time spent managing multiple subscriptions and API keys.
Then we calculated what each would be under the current setup and under the proposed setup. The breakout shows your CFO exactly where the savings are coming from, not just total savings.
The key is being honest about the operational piece. If you’re spending two hours a month managing different API keys and billing systems, that’s real cost. We valued it at our average engineer hourly rate. It made the case clearer because it wasn’t just ‘we save money on subscriptions,’ it was ‘here’s where each dollar comes from.’
We broke it down by cost category and modeled it year-over-year. Year 1 had migration costs, so it looked worse. Years 2-3 showed the compound benefit. That actually mattered for our decision because it showed the break-even point clearly.
The unified AI licensing didn’t change the comparison as much as people thought—maybe 15-20% of the total savings. The bigger factor was just choosing a more efficient platform. But you need to separate the lease two to make the numbers credible.
Create a matrix with each platform as a column (Make, Zapier, proposed platform) and cost categories as rows. Include platform subscriptions, per-model AI costs, integration fees if any, and estimated labor overhead. Use the same calculation for labor—it’s usually underestimated. We found that when you actually account for time spent on API key management, debugging cross-platform issues, and maintenance, the labor component is often 20-30% of total cost. That made the case for consolidation much stronger.
Effective TCO modeling requires identifying three distinct cost vectors: platform licensing, AI model subscriptions, and operational overhead. Assign each to separate budget categories in your model. This enables CFO-level scrutiny and isolates the financial impact of each decision. Platform costs are easy to compare. AI licensing consolidation typically yields 15-25% savings. Operational overhead is often overlooked but frequently represents 15-20% of total automation costs. Model conservatively on the labor component—it’s the easiest number to challenge.
split into 3: platform cost, AI subscriptions, ops overhead. use hourly rates for labor time. shows CFO exactly where savings come from. made our model credible
Three line items: platform tier, AI subscriptions, labor overhead. Calculate each separately for current vs. proposed setup. Year-over-year shows break-even point.
We simplified this by showing what our current state actually costs end-to-end. We had Make at $300/month, separate OpenAI at $50/month, Claude at $40/month, plus about 6 hours per month of ops time managing all of it. That’s roughly $500-600 total when you value the labor.
With Latenode’s unified subscription, we moved to one line item covering everything. The subscription was $400/month, which on paper is cheaper. But more importantly, we eliminated the API key juggling and the cross-platform debugging. That ops time dropped to maybe 30 minutes per month.
The case we presented to our CFO was: ‘Same functionality, cleaner cost structure, less operational complexity.’ The math was straightforward because we weren’t trying to separate platform benefits from AI consolidation benefits—we just showed what the whole system costs to maintain.