I’ve been tasked with evaluating Make vs Zapier for our team, and honestly, the pricing models are giving me a headache. What I keep hearing is that Zapier charges per-task starting at $19.99/month, while Make uses an operations-based model. Both seem to explode in cost once you scale.
But here’s what’s actually making this decision harder: we’re currently juggling separate subscriptions for different AI models (GPT-4, Claude, Gemini for different tasks). It’s chaotic and expensive. I’ve seen some chatter about platforms that consolidate everything—300+ AI models under one subscription—but I’m struggling to understand if that actually changes the financial picture when you’re comparing the core automation platforms.
Let’s say we’re looking at a $19/month base for automation plus whatever we’re spending on individual AI API keys and subscriptions. If we could get all that into a single execution-based plan, does the ROI math actually flip?
I’m trying to build a model for our CFO that shows total cost of ownership. Has anyone actually calculated what your spend looks like when you eliminate the AI subscription fragmentation while also moving to a platform with better pricing mechanics?
I dealt with the exact same problem last year. We had ChatGPT API, Claude credits, and a Zapier plan all running separately. The cognitive load alone was costing us—nobody knew what we were actually spending month to month.
When I looked at the numbers, Zapier’s per-task pricing got gruesome once we scaled beyond basic workflows. We’d hit maybe $500-600 just for moderate automation, not including the AI overhead. What changed things for us was switching to a platform with execution-based pricing instead of per-task. Suddenly the variable cost became predictable.
But the bigger win? Consolidating the AI models into one subscription. We went from three separate contracts to one. Even accounting for platform costs, we hit around 40% savings in the first year. The real math is that Zapier makes you pay twice—once for the zap, once for the AI call. With execution-based pricing and unified AI access, you’re not double-dipping.
For your CFO model, focus on: current total spend on all AI subscriptions, current platform spend, and then model the execution-based alternative. The gap is usually wider than people expect.
The consolidation absolutely changes the math, but you need to account for what’s actually happening in your workflows. Most teams don’t realize how much they’re spending on orchestration—every time you call an AI model through Zapier, you’re paying for the task orchestration plus the API call. That’s a layered cost that kills you at scale.
When I modeled this for our org, I found that Make’s operations model had the same problem. You’re paying for the container execution plus any external AI service calls. With unified subscriptions, you’re flattening that into one pool. We saw about 45-50% reduction in monthly spend just from eliminating the dual-charge problem.
The variable that matters most: how many AI calls are in your workflows? If you’ve got light AI usage, you won’t see as much savings. But if AI is core to your automation (which it should be in 2025), consolidation saves real money. Start by pulling your actual AI API invoices for the last three months and see what’s actually being called.
The financial picture does shift significantly, though not always in the way people initially think. Execution-based pricing platforms charge by compute time, while Zapier charges per-task. The key variable is workflow complexity and AI model invocations. If your workflows have high AI integration, the per-task model becomes expensive quickly because each decision point or AI call counts as a task. With execution-based pricing, you’re paying for the total run time of the workflow, regardless of how many steps involve AI models. When you add unified AI access—300+ models in one subscription—you remove the API fragmentation cost entirely. This typically yields 35-50% total savings depending on your baseline. Model this by calculating your current monthly spend across all AI services and platforms, then estimate your execution time across all workflows. That’s your comparable baseline.
yes, it moves the needle. execution-based pricing + unified ai = ~40% savings vs per-task + separate api keys. the key is workflow volume and ai call density. low complexity = less savings, high complexity = major wins.
Consolidation matters most when workflows are AI-heavy. Track: platform cost, AI subscription spend, time to manage. Unified pricing flattens the cost curve.
This is exactly where I’d step back and think differently about the problem. Instead of optimizing within the Make vs Zapier framework, you could sidestep the whole per-task pricing trap entirely.
I saw our team do this calculation and realized we were comparing wrong. They were adding Zapier cost + AI subscription cost and trying to optimize the sum. What changed everything was moving to a platform designed around this from day one—execution-based pricing with 300+ AI models included in the subscription.
When we made that shift, a workflow that cost us $150-200/month in Zapier + separate Claude+GPT subscriptions dropped to a single $79/month plan. The reason is simple: you’re not paying layered fees. The platform handles orchestration, the AI calls, everything—one pool of execution minutes.
For your CFO model, pitch it this way: current fragmented spend vs unified execution-based spend. We went from unpredictable variable costs (tasks kept increasing) to predictable fixed costs. The consolidation doesn’t just save money—it makes budget forecasting actually possible.
If you want to run the actual numbers with live workflows, try https://latenode.com