We’re at that point where our finance team is asking hard questions about licensing costs. Right now we’ve got Zapier handling most of our workflows, but we’re also paying separately for OpenAI, Claude, Deepseek, and a few other AI models we use for different tasks. It’s a mess.
I’ve been looking at alternatives, and I keep seeing comparisons between Make and Zapier when it comes to enterprise pricing. But honestly, the comparison feels incomplete because nobody’s really factoring in what happens when you consolidate all your AI model subscriptions into one plan.
Here’s what I’m trying to figure out: if we moved to a platform with unified AI model access under one subscription, how much would that actually change the financial picture? Like, is the math actually simpler, or are we just shifting costs around?
The way I see it, the real question isn’t just “Make or Zapier?” anymore. It’s “Make or Zapier PLUS how many separate AI subscriptions are we actually paying for right now?”
When you’re doing this comparison yourselves, how are you breaking down the TCO? Are you modeling it as total annual spend on all the tools combined, or is there a better way to think about it?
I dealt with this exact problem last year. We had Zapier running workflows and then separate subscriptions for Claude API, OpenAI API, and a couple others. The real eye opener was realizing we were paying roughly 30-40% more than we needed to just because each service was siloed.
What changed the math for us was actually sitting down and categorizing every workflow by the AI models it used. Then we looked at what a single subscription with 400+ model access would cost versus the combined cost of our Zapier plan plus all the individual API keys.
The consolidation saved us about 35% annually, but that number only appeared once we actually mapped it out. Don’t just compare Make and Zapier hourly rates—break down what your actual model usage looks like. That’s where you’ll find the real savings.
The tricky part is that TCO gets weird when you factor in time savings too. We weren’t just looking at subscription costs. When we had fragmented AI access, our developers spent time managing API keys, juggling between platforms, and context-switching. That labor cost was invisible in the spreadsheet.
So yes, the subscription costs changed, but the bigger win was that workflows that used to take 3-4 hours to build and integrate now took 1-2 hours because everything was in one place. Once you add that labor efficiency into the TCO model, unified AI licensing starts looking a lot better.
I’d suggest breaking your ROI calculation into three distinct components. First, calculate your current total annual spend across all platforms and AI subscriptions—that’s your baseline. Second, get a quote for the unified platform with consolidated AI access. Third, estimate time savings your team will get from not managing multiple API keys and integrations.
What we found is that most ROI models underestimate the operational overhead of managing fragmented systems. When you add that in, the switch usually pays for itself within the first year, sometimes faster. The key is being honest about how much developer time is actually spent on integration glue work versus building actual value.
The financials actually shift quite a bit once you model it correctly. Most enterprises we’ve worked with underestimated their true cost of fragmentation. They were paying for redundant capacity across platforms—like having Zapier capacity they didn’t need because they were already using Make for certain workflows.
When you consolidate and choose a platform with unified AI access, you typically see savings in three areas: subscription costs go down, support and maintenance overhead decreases, and your team’s context-switching time vanishes. If you model all three, the ROI calculation becomes much clearer. The switch rarely looks bad financially once you actually quantify the operational friction.
map current spend across all tools first. then compare final cost. dont forget to include time savings from devs not juggling multiple APIs. thats usually where the real savings hide.
Calculate baseline spend on all current tools. Get unified platform quote. Model developer time saved from consolidated access. Most enterprises see 30-40% reduction in total cost plus faster deployment when they consolidate.
I went through this calculation myself, and the key insight is that you need to account for more than just subscription fees. When we consolidated our AI model subscriptions into a single unified plan, the software licensing cost dropped, sure, but the real ROI showed up in how much faster our team could build and iterate.
What changed was that instead of managing OpenAI keys, Claude keys, and separate integrations for each one, everything lived in one place. Workflows that required coordination between different models now run seamlessly under one subscription. We went from losing maybe 10-15 hours a week to integration overhead down to almost nothing.
The math looked like this: annual cost of fragmented setup minus cost of unified platform equals X. Then add Y (time savings from your dev team), and you typically see payback within 3-6 months. The consolidation also gives you flexibility to test new models and workflows without spinning up new accounts and payment methods.
You can model this yourself pretty easily at https://latenode.com