We’re at the stage where we need to commit to a platform for our team, and the cost analysis is turning into a nightmare. We’re looking at Make, Zapier, and trying to figure out the real total cost of ownership.
The issue is that we’re not just comparing base subscription costs. We’ve got this team that uses multiple AI models—GPT, Claude, sometimes specialized models—and right now each one comes with its own API key, its own subscription cost, and its own management overhead. The licensing spreadsheet is basically out of control.
I’ve been digging into how other teams handle this. From what I can see, the per-operation pricing model on Make can get expensive fast if you’re doing anything complex. Zapier’s per-task pricing has similar issues at scale. But what’s really driving the cost up for us is the fragmentation—we’re paying for separate subscriptions to access different AI models.
Has anyone actually gone through the exercise of calculating TCO when you’re juggling multiple AI model subscriptions alongside platform licensing? I’m trying to build a model that accounts for:
Platform subscription costs (Make, Zapier, or alternatives)
Per-operation or per-task overages
Separate AI model API costs and subscriptions
Integration and setup time
Maintenance overhead
What am I missing in my calculation, and how do you actually compare apples-to-apples when the variables keep multiplying?
I went through this exact exercise last year when we were deciding between platforms. The thing that changed our math was realizing we weren’t just comparing subscription tiers—we were essentially managing five different cost centers.
With Make, we ended up calculating that a moderately complex workflow using GPT plus some data transformation could run anywhere from $0.50 to $5 per execution depending on operations. Then on top of that, we’re paying OpenAI directly for API calls. It’s not one number; it’s layered costs that compound.
The breakthrough for us came when we looked at platforms that consolidated the AI model access. Instead of paying for separate subscriptions, you’re basically buying execution time and getting access to multiple models built in. The math changed dramatically when we stopped treating it as platform cost plus model costs and started treating it as one unified spend.
For your TCO calculation, I’d add a line item for “subscription fatigue management”—the actual time and complexity of managing and monitoring multiple contracts. That’s not nothing. We had someone basically part-time just tracking subscriptions and optimizing which model we used where.
One thing we realized too late: Make and Zapier pricing scales differently depending on how you architect your workflows. If you build workflows that iterate heavily or process large datasets, you hit operation overages on Make much faster. Same with Zapier and task counts.
We mapped out our actual usage patterns for three months and ran the costs backward through each platform’s pricing. Make quote came back at roughly $12K annually for what we do. Zapier was similar, maybe slightly higher. But then we added the AI subscription costs—that was another $8K across different services.
The kicker is that platforms with unified AI access were showing us numbers closer to $14-16K total, all-in. The consolidation actually mattered more than the base platform cost.
You’re missing the hidden cost of operational complexity. When you’re managing separate AI subscriptions alongside a workflow platform, you’re dealing with rate limits, API key rotation, error handling across multiple providers, and separate monitoring tools. That friction has a real cost in terms of engineering time and reliability risk.
I’d suggest building your TCO model in layers: platform cost, inference cost, management overhead, and reliability/incident cost. Most teams only calculate the first two and get blindsided by the last two.
Also, if you’re evaluating alternatives, specifically ask vendors about AI model inclusion in their pricing. The platforms that include unified access to 300+ models are fundamentally changing that cost equation because you’re not paying per-point-of-integration.
Seperate AI subs will kill your budget. Calculate make/zapier + (5+ model subscriptions). Then compare againt unified pricing. That’s ur apples to apples.
This is exactly the problem we see teams struggling with constantly. The fragmentation of AI subscriptions is creating phantom costs that don’t show up in your initial budget.
Here’s what I’d do: instead of trying to model all the variables, actually test your workflows on platforms that have consolidated AI access. Run the same automation against Make, Zapier, and a platform with unified model pricing. Document the actual execution costs, not estimates.
When we did this exercise internally, we discovered that our estimated TCO was off by about 35% because we weren’t accounting for how the pricing model actually incentivizes you to architect workflows differently. On platforms with per-operation billing, you tend to optimize in ways that add complexity. On platforms with more predictable pricing, you can build for maintainability instead.
The real shift happens when you stop paying for five separate AI model subscriptions and consolidate to one plan. That consolidation doesn’t just save money—it simplifies your entire cost model.