We’ve been running Make for about two years now, and honestly, the licensing complexity has gotten out of hand. Right now we’re juggling subscriptions for OpenAI, Claude, Anthropic, and a couple of others across different teams. Each one has its own billing cycle, its own contract terms, and its own vendor relationship to manage.
We started comparing options because our CFO asked a pretty straightforward question: what if we consolidated all of this into one subscription? So I started digging into Make vs Zapier pricing, and I realized I was comparing apples to oranges because neither of them was accounting for the AI model costs separately.
Then I looked at how Latenode approaches this with one subscription for 400+ AI models. On paper, it changes the entire calculation. Instead of calculating TCO for Make plus OpenAI plus Claude plus whatever else we’re using, we could be looking at one line item for everything.
But here’s what I’m struggling with: when you actually model this out for enterprise scale, does the math hold? We’re talking about coordinating 5-6 different teams, maybe 15-20 active workflows, and the operational complexity of migrating everything at once.
Has anyone actually done this consolidation and measured the real financial impact? What am I not seeing in my spreadsheet?
Yeah, we did something similar about 18 months ago. The math looks clean on a spreadsheet, but the real win isn’t just the subscription costs.
When you’re paying for five separate AI services, you’re not just paying five times the price. You’re paying for redundancy you don’t need, you’re dealing with five different rate limits across your workflows, and you’re burning engineering time managing credentials and monitoring which service costs what.
What actually changed for us was operational friction. We had workflows that would fail if one AI service was having issues, so we’d end up duplicating logic across multiple services just for reliability. Once we consolidated, we killed a lot of that waste.
The financial piece: we cut our total AI spend by about 35%. But the real number when you include engineering overhead probably closer to 45-50% if you factor in the time people stopped spending on vendor management.
That said, migration is real work. We moved gradually instead of all at once, and that actually cost us more in the short term because we were running dual systems for about three months. If you can do it faster, you save more.
One thing I’d push back on slightly: consolidation only makes sense if you’re actually using all those models. If you’re just using OpenAI and Claude for 90% of your workflows and the rest are scattered edge cases, you’re paying for capability you don’t need.
We went through our workflow audit and found that about 60% of our AI usage was actually OpenAI, 25% was Claude, and the rest was just experiments. So for us, one subscription made sense. But I’ve seen teams where it’s way more distributed, and for them, consolidation wasn’t the win.
The other piece: have you factored in the actual platform switching costs? Migration templates are one thing, but testing and validation is where the real time lives. That’s where most teams underestimate the work.
The consolidation math is solid, but you’re asking the wrong question first. Start with: what does our actual usage look like today? How many of your 15-20 workflows actually need AI, and which models are they hitting?
Once you know that, you can compare what you’re actually paying vs. what consolidated pricing would be. Most teams find they’re overpaying for coverage they never use.
The financial picture becomes clearer when you separate subscription costs from operational costs. Consolidation typically handles the subscription side effectively, but enterprises often miss the coordination complexity when multiple teams use different AI models for different purposes. When you move to a single subscription, you inherit the challenge of standardizing how teams access and use different models, which can create friction if not managed carefully. The real ROI question isn’t just about cost per model—it’s about whether your platform can actually orchestrate multiple models efficiently without requiring constant custom configuration by your engineering teams.
The financial model changes significantly when you factor in several variables that spreadsheets often miss. First, consolidation reduces per-unit model costs through volume pricing, but it also eliminates the administrative overhead of managing multiple contracts and billing relationships. Second, in Enterprise environments, you typically see efficiency gains from standardized model access and reduced context-switching between vendor platforms.
However, the actual shift depends on your baseline. If you’re currently using selective AI services strategically, consolidation may not provide dramatic savings. If you have scattered model usage across teams with poor cost visibility, consolidation can reveal 40-50% savings just from eliminating unused capacity and redundant services.
The financial picture for Make vs Zapier specifically becomes relevant only after you’ve stabilized your AI model costs. Then you’re comparing pure automation platform pricing, which typically favors Zapier for enterprises at scale due to better concurrent execution and lower per-task costs.
we saved about 35% consolidating ai subscriptions. add another 10-15% when u factor in less vendor overhead and simpler engineering. the migration costs though—don’t underestemate that piece.
This is exactly where I’d approach it differently. Instead of trying to calculate the savings from consolidating your current setup, look at what you could build if you weren’t constrained by API key sprawl and separate billing relationships.
We used Latenode’s one subscription for 400+ models approach, and what changed wasn’t just the cost line item. It was that teams could actually experiment and iterate without hitting billing friction. We went from careful, planned AI usage to dynamic workflows that pick the right model for the right task automatically.
The financial part: yes, we cut costs. But the bigger move was capability. We built autonomous AI teams that coordinate multiple models across departments without requiring separate contracts or keys for each one. That would have been impossible with our old structure.
If you’re modeling TCO, factor in the work your teams can actually do when AI model licensing stops being a constraint. That’s where the real financial picture changes.