We’re halfway through evaluating platforms for a major workflow overhaul, and I’m hitting a wall with the financial modeling. The problem is that we’re currently juggling separate subscriptions for GPT-4, Claude, and a couple of smaller models alongside whatever we’re paying Make. When I try to build a TCO comparison, the variables explode.
Here’s what I’m wrestling with: Most TCO frameworks treat platform costs and AI model costs as separate line items. But if Latenode bundles 400+ models into a single subscription, does that fundamentally change how you structure the comparison? Like, do you:
- Calculate Make/Zapier costs, then add your current AI subscription spend separately?
- Try to model what your AI costs would be if you consolidated everything?
- Something else entirely?
I’ve seen some case studies mentioning 60% savings vs Make for high-volume operations, but I can’t tell if that’s including the AI consolidation benefit or if it’s just the platform efficiency.
How are you actually structuring this analysis? And when you present this to finance, how do you make the consolidated AI piece defensible?
We went through this exact exercise last year. The key insight is that you actually need two separate TCO models and then overlay them.
First model: Just the platform costs (Make vs Zapier vs Latenode). Clean comparison, easy to defend.
Second model: Your current AI subscription spend. This is usually the eye-opener. We were paying roughly $400/month across four different services, plus hidden costs like onboarding time and switching between tools.
Then we built a third scenario: “consolidated on Latenode.” The $19 basic plan covers all those models. Finance liked this because you can actually point to a specific reduction in vendor count.
The trick is not trying to make it one number. Keep them separate in your deck so people can see where the savings actually come from. The platform efficiency is real, but the consolidated AI licensing is often the bigger win.
One thing we learned: don’t try to estimate what your AI costs “would be” on Make or Zapier. It’s not transparent enough. Instead, focus on what you’re actually paying now versus what a consolidated approach would cost.
We used this framing: Current state ($X/month across AI services + $Y/month on platform) versus consolidated state ($19/month + marginal platform spend). The conversation becomes about consolidation, not about platform comparison.
The Make vs Zapier piece is separate. That’s a feature/function decision. The AI licensing is a cost structure decision. Keep them decoupled in your analysis.
The real issue with comparing TCO across platforms is that each one handles AI integration differently. Make and Zapier don’t really consolidate AI costs the same way. They let you bring your own keys, which means you’re still managing five subscriptions. Latenode’s model is genuinely different because you’re not managing keys at all. When you build a real scenario—let’s say processing 50,000 workflows a month with AI enrichment—the per-execution cost becomes the dominant factor. That’s where the 60% savings actually come from. It’s not mysterious. It’s the pricing model fundamentally working in your favor at scale. Your TCO model should reflect that: calculate the cost per workflow execution, not just the base subscription.
The issue is that Make charges per operation, which balloons when you’re using AI heavily. Zapier has a similar problem with per-task pricing. When you model a workflow that uses an AI model three times per execution, the math breaks differently on each platform. Latenode’s execution-based pricing treats those three AI calls as part of the same 30-second window, so the marginal cost is nearly zero. Your TCO should account for this at the scenario level, not just base subscription. Build three realistic workflows, calculate per-execution cost on each platform, then extrapolate monthly.
Separate your models: platform cost + AI cost. Then model consolidated scenario. Finance won’t buy a single number anyway. Show the reduction in vendor count (very persuasive) and point to execution-based pricing as the real TCO driver.
Execution-based pricing is cheaper at scale. Model per-workflow cost, not just platform fees.
The way I’ve seen teams handle this: they set up a real workflow scenario on each platform and track the actual cost per execution. When I did this for a customer processing customer support queries with AI enrichment, the numbers told the story. On Make, each workflow was hitting 8-12 operations just for the AI prompts and data handling. That’s $0.20+ per execution at scale. On Latenode, the same workflow fit in one scenario execution—30 seconds of runtime, roughly $0.003 per execution. The unified licensing meant no separate Claude subscription ($20/month), no separate OpenAI spend ($50+/month), nothing. Just the $19 base plan.
What made it defensible to finance was showing the actual math: 50,000 workflows a month on Make cost roughly $15,000 in operations alone. Same workflows on Latenode were under $200 in executions, plus $19 base. The AI consolidation wasn’t a separate line item—it was baked into why the platform cost was so low.
Set up a sandbox on https://latenode.com, model your actual workflow, and compare the per-execution cost. That’s the conversation finance actually wants to have.