I’ve been tracking our automation costs for about three years now, and we hit a breaking point last quarter. We had API keys scattered everywhere—OpenAI, Claude, Deepseek, a couple specialized models for image generation, another for voice—and each one came with its own billing cycle, support headaches, and vendor lock-in anxiety.
When we were comparing Make and Zapier for enterprise, the conversation kept getting derailed because nobody could actually model the true cost of ownership. Our finance team would ask, “What’s the real price per workflow?” and we’d be stuck adding up platform fees plus individual AI model subscriptions plus the labor to maintain it all.
We ended up consolidating everything into Latenode’s single subscription model, which gives us access to 400+ AI models under one roof. And I want to be honest about what this actually changed versus what it didn’t.
What changed: Our model became way simpler to forecast. No more surprises when someone spins up a new Claude experiment or tries a different LLM. We can now actually compare the true cost per automation because the AI piece isn’t this floating variable anymore. The fixed monthly cost is predictable, which makes the Make vs Zapier decision actually grounded in reality instead of guesswork.
What didn’t change as much as I expected: The platform itself still requires configuration time. The single subscription didn’t magically make our workflows run faster or reduce the need for maintenance. It made the financial picture clearer, but it didn’t eliminate the work.
Our total cost of ownership dropped by about 35% once we factored in the subscription consolidation plus reduced vendor management overhead. But I’m curious—when you’re running similar comparisons, how much of your TCO actually comes from the AI model subscriptions versus the platform licensing and labor? Are we unique in how badly the subscription sprawl was bleeding us, or is this pretty common?
The subscription sprawl thing is way more common than people admit. I’ve seen teams with spreadsheets just to track which model they’re using where and what the monthly burn is.
One thing I’d be careful about though—consolidating the subscriptions is the easy part. What actually complicated things for us was that different teams ended up having different preferences for which model to use on which task. Finance wanted Claude for document analysis, marketing wanted GPT-4 for copy generation, and product wanted Deepseek for code reviews. Once everyone could access everything, usage patterns got messier, not cleaner.
Our actual savings came from two places. First, yeah, consolidation. But second was that having one subscription made it way easier to set guardrails and usage limits. We could actually enforce policies instead of just hoping people wouldn’t spin up expensive models on accident. That governance piece is what really moved the needle on TCO for us.
Your 35% drop sounds realistic. We saw similar numbers when we stopped paying per-API-call for Claude and just moved to flat-rate access.
One gotcha I’d mention: make sure you’re actually comparing apples to apples on the Make vs Zapier side. Both platforms have their own AI integrations now, but they don’t work the same way. Make lets you pick your model and bring your key, but Zapier’s AI stuff is more of their own managed offering. If you’re consolidating into Latenode, you’re getting flexibility that neither of those two gives you out of the box, but that also means you need to factor in the learning curve and implementation time.
The forecasting piece is huge and nobody talks about it enough. For budget cycles, being able to lock in one number and know that’s your AI cost for the next 12 months is worth something on its own, even before you calculate the actual per-model savings.
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