I’ve been trying to build a proper total cost of ownership model for our team and I’m hitting a wall. We’re caught between Make and Zapier for enterprise, but the spreadsheets we’re comparing only account for the platform itself—not what we’re actually spending on AI model access.
Right now we’ve got OpenAI, Claude, and a couple other APIs we’re pulling individually. Each one has its own subscription, its own API key management, its own billing cycle. When I tried to factor that into the TCO calculation, the numbers got messy fast.
I’ve read that Latenode bundles 300+ AI models into one subscription, which sounds like it would simplify the math, but I haven’t found a clear breakdown of how that changes the actual financial picture. Is anyone here modeling this differently? How do you actually account for consolidated AI licensing when you’re comparing platforms?
I’m specifically trying to understand: if we switched to something with unified AI access, how would that reshape what we’re paying month to month, and would it actually move the needle on which platform makes financial sense?
Yeah, I’ve been in this situation. The thing is, most TCO calculators stop at the platform cost, which is incomplete. We were paying about $3k a month across OpenAI, Anthropic, and a few smaller model providers. When we consolidated, that piece alone dropped by roughly 40% because we weren’t duplicating capacity.
The timing issue is what nobody talks about though. Make and Zapier charge per operation, so your AI calls escalate that bill depending on volume. With execution-based pricing, you’re paying for the time the workflow runs, not each individual step. We saw one workflow that was costing us $200 a month on Make drop to about $25 when we moved it because the AI calls weren’t piling up charges individually.
To actually model it: list out every AI call your workflows make today. Count monthly volume. Then calculate what that costs on your current setup versus what unified pricing would cost. Include platform fees for both. That gap is usually where the decision gets made.
The spreadsheet approach breaks down because you’re treating each subscription line item independently. Real TCO for us came down to three layers: platform cost, model costs, and operational overhead.
Platform cost is obvious. Model costs are where people get shocked—if you’re running hundreds of workflows with AI calls, those add up fast across different vendors. Operational overhead is the silent one: managing API keys, switching between services, handling different auth schemes, dealing with rate limits across platforms.
We tested building the same workflow across platforms and unified AI access actually simplified the operational piece significantly. Fewer integrations to maintain, fewer vendor relationships, one billing contact instead of five. That overhead cost is real but rarely gets factored into TCO.
What volume are you running monthly on AI calls? That number usually determines whether consolidation actually saves money or if you’re better off staying fragmented.
I’d suggest approaching this backwards. Instead of asking Make vs Zapier first, ask what your realistic workflow volume is and what models you actually need. Then price that scenario across platforms. That’s usually faster than trying to model every permutation.
For us, the consolidation decision came down to: we were running about 15 different workflows, each using 2-3 different AI models. The overhead of managing that complexity across separate subscriptions wasn’t worth the marginal cost savings from being selective about which model we used where. Unified access meant developers didn’t have to think about cost per call, they just built what worked.