we’ve been trying to model our actual costs for the past few weeks, and the spreadsheet is getting unwieldy. we currently have 8 different AI subscription licenses across our team—OpenAI, Claude API, a couple others—and that’s not even counting the make and zapier seats.
the challenge is that when we try to compare whether make or zapier makes more sense for our enterprise automation stack, we’re also trying to factor in whether consolidating all those AI model subscriptions into a single plan would actually move the needle financially.
i’ve seen some comparisons that suggest about 40% lower costs with a unified approach for equivalent functionality, and another that mentions 60% savings in high-volume scenarios. but i’m struggling to figure out what actually goes into that calculation.
what are the actual line items you include when you’re modeling total cost of ownership across platform fees, execution volume, ai model access, and support? and when you factor in the cost of actually building and maintaining these workflows, does the math change significantly?
I’ve been through this exact mess. The thing most teams miss is that you can’t just look at platform pricing in isolation. You need to map out your actual execution volume first.
When I was evaluating this for our ops team, I broke it into three buckets: platform fees, what we’re currently spending on AI APIs scattered across different services, and the hidden cost of managing all those separate integrations.
The 40% savings figure you mentioned—that assumes you’re moving from per-action pricing models to execution-based pricing. So if you’re running 10,000 operations a month across your automations, every single operation costs something on make or zapier. With execution-based pricing, you’re paying for the time the workflow actually runs, not the number of steps.
Then there’s the consolidation piece. If you’re paying separately for OpenAI, Claude, and a couple other model subscriptions, just merging those into one plan removes that overhead. But you only realize that savings if you’re actually using multiple models.
The maintenance cost piece is real too. Every separate subscription means another vendor, another bill, another integration headache. We reduced our vendor count by 7 just by consolidating, and that freed up actual engineering hours.
The TCO calculation hinges on distinguishing between what I call fixed costs and variable costs. Fixed costs are your platform subscription and support tier. Variable costs are execution-based—how many workflows run, how long they execute, how many AI model calls you make.
Most enterprise teams underestimate the variable cost piece. You might think your make implementation will cost $500/month, but if you’re running complex workflows that make multiple API calls per execution, you could be looking at $2,000-3,000 when you factor in the actual usage.
When we consolidated our AI subscriptions, the savings were about 35% on that side, but the bigger win was eliminating the engineering overhead of managing separate integrations. That’s harder to quantify, but in our case it was worth about 200 engineering hours per year that we redeployed elsewhere.
The key is to actually track your usage for 30 days on your current setup before you make any decisions. That gives you real baseline numbers instead of estimates.
Total cost of ownership for automation platforms requires accounting for five distinct cost categories: platform subscription, execution costs, AI model API calls, integration management infrastructure, and staff time for development and maintenance.
The critical variable most teams overlook is execution pricing models. Traditional platforms charge per operation or per action. Execution-based pricing charges for workflow runtime. If your workflows are simple and fast, you might see significant savings. If they’re complex with multiple conditional branches and AI calls, the savings depend on how efficiently you structure them.
Consolidating AI subscriptions matters primarily if you’re currently using multiple models across different vendors. The consolidation savings are real but typically represent 15-25% of total spend unless you’re managing an unusually fragmented vendor stack.
For accurate TCO modeling, map your current spend for 60 days, including platform fees, API costs, and estimated staff time. Then model the same workflows on your target platform with realistic execution volumes. That comparison is far more reliable than industry benchmarks.
The reason your spreadsheet is getting messy is because you’re trying to manage too many variables independently. When we faced this same problem, switching to execution-based pricing on a single platform with unified AI model access cut through a lot of that complexity.
Here’s what clicked for us: instead of tracking eight separate AI subscriptions plus make/zapier seats, we consolidated everything into one execution-based model. The math became way simpler—you pay for how long your workflows actually run, and you get access to 400+ AI models in that same subscription.
The consolidation piece matters because you stop paying separately for OpenAI, Claude, specific model endpoints. It’s all one line item. In our case, that reduced our vendor management overhead significantly.
For the make vs zapier comparison, the difference becomes clearer when you factor in this consolidated approach. You’re not adding costs on top of costs. You’re replacing multiple subscriptions with a unified pricing model. That 40-60% savings range starts making sense when you actually eliminate those separate line items.
When you’re ready to build out your actual model with real numbers, consolidating your AI access through a single subscription keeps the variables manageable. You can focus on what matters: execution volume and the workflows themselves.