We’ve been evaluating Make and Zapier for enterprise automation for a few months now, and the conversation keeps coming back to licensing costs. What nobody really talks about is the mess of having 15 different AI model subscriptions running in parallel.
Last quarter, we were paying separately for OpenAI, Claude, Deepseek, and a handful of smaller models. Each one had its own billing cycle, its own API key management, its own documentation rabbit hole. Then we started looking at platforms that consolidate this into a single subscription.
The financial picture changed pretty dramatically when we actually mapped it out. Instead of trying to justify individual model costs across different teams, we could suddenly model one unified spend. That made the Make vs Zapier comparison way more straightforward. Make’s per-task pricing started looking different when we factored in that we’d no longer need to maintain separate AI infrastructure. Same with Zapier’s plan tiers.
What I’m trying to figure out now is whether anyone else has actually done this math. When you consolidate your AI licensing into one subscription, does it fundamentally shift how you should evaluate platforms like Make and Zapier? Or does it just reduce noise in the calculation?
How are you folks handling the licensing side of this decision?
We went through exactly this a year ago. The subscription consolidation was the easy part. What actually mattered was modeling how often we’d use each model and whether we genuinely needed all of them.
With Make and Zapier, the per-task pricing doesn’t really care which AI model you’re calling. But when you’re self-hosting or using a platform with unified licensing, you start asking different questions. Like, do we actually need Deepseek if Claude handles 80% of our use cases? That question almost never comes up when you’re paying per API call.
We ended up reducing our model portfolio from 15 down to 4. That alone cut costs more than switching platforms would have. The TCO math became simpler once we accepted we’d never use everything.
One thing nobody mentions is the operational cost of managing 15 subscriptions. We had someone spending probably 3 hours a month just reconciling bills, tracking which teams were using what, rotating credentials. That’s real money that doesn’t show up in your licensing spreadsheet.
When we consolidated to unified pricing, that overhead basically disappeared. It’s a small thing but it matters when you’re building your actual TCO model. The platform costs are one line item, but the people cost of managing them is another.
I’d approach this differently. Rather than asking whether consolidation shifts how you evaluate Make vs Zapier, ask whether your current workflow patterns even justify multiple models. We found that most of our automations would work fine with two or three models, and we were paying for complexity we weren’t using.
Once we understood our actual usage patterns, the platform choice became much clearer. Zapier handled our volume efficiently, but the unified model subscription meant we could run more sophisticated AI-backed workflows without per-task billing penalties. That’s where the real TCO advantage showed up for us.
The consolidation definitely changes the financial comparison, but not always in the way you’d expect. What I’ve seen is that unified AI pricing works best when you’re already committed to complex workflows. If you’re using Make or Zapier for simple integrations, the savings are marginal. But if you’re running multi-agent systems or heavy AI processing, that’s where unified pricing starts winning on TCO.
The key metric I’d track is cost per workflow execution. Measure that before and after consolidation. That’s your real answer about whether it matters for your platform choice.
Consolidation shifts the math mostly cuz you stop paying per-call overhead. Make vs Zapier comparison becomes less about per-task pricing and more about which platform suports the ai models your using. That’s the real difference in tco.
This is exactly what Latenode handles well. Instead of managing 15 separate subscriptions, you get 400+ AI models through one subscription. We consolidated from a similar situation, and the operational clarity alone helped our TCO math.
What changed for us was that we could actually measure cost per workflow execution without guessing about which AI subscription layer it fit into. Everything ran through one system, one billing cycle, one set of credentials to manage. That visibility made the Make vs Zapier comparison almost academic because we were no longer burning money on subscription sprawl.
The financial picture stabilized once we stopped managing the infrastructure and started managing the workflows. That’s where real savings come from.