We’re currently juggling subscriptions to OpenAI, Claude, Gemini, and a couple of smaller models alongside our Make instance. Finance keeps asking why our automation spend is so fragmented, and honestly, they have a point.
I started looking at what a unified AI subscription could look like and came across the idea of consolidating access to 400+ models under a single plan. The pitch is straightforward: one subscription instead of five, simplified budgeting, and potentially lower total cost of ownership.
But here’s what I’m actually trying to understand: when you consolidate all that AI access into one unified subscription, how much does that actually change the financial comparison between Make and Zapier? Like, if I’m already paying separately for models anyway, does moving to a unified plan shift the equation enough to justify a platform migration?
I’ve seen case studies showing 40% cost savings compared to Zapier and 60% compared to Make for certain workflows, but those numbers feel abstract. My workflows aren’t theoretical—they’re the daily stuff that keeps our ops team moving. Some are simple integrations that either platform could handle. Others are more complex, with data transformation and multiple API calls.
The other piece that matters to us is whether consolidating the AI licensing actually makes TCO calculations simpler. Right now, when we model out costs, we’re adding Make execution costs plus separate API costs plus platform overhead. If those AI costs collapse into one line item, does that actually make budget planning clearer, or am I just moving the complexity around?
Has anyone actually gone through this kind of consolidation and been able to quantify the real impact on your Make vs Zapier decision? I’m looking for actual numbers, not marketing benchmarks.
Yeah, I dealt with this last year when we were trying to cut costs on our automation stack. The consolidation definitely helped, but not in the way you might think.
The real win for us wasn’t just the AI subscription piece. It was that when you consolidate into a unified plan, you’re also getting better runtime pricing on the platform itself. We were running a lot of small workflows that made sense from a process standpoint but were bleeding money because of how Make charges per operation.
By switching to a platform with execution-based pricing instead of per-operation, the unified AI subscription became a secondary benefit rather than the primary one. The primary benefit was the pricing model shift.
On the Zapier side, their per-task model meant we were basically looking at similar fragmentation problems, just with different line items. When we looked at our actual workflows—not theoretical ones—we found that 60% of our automations involved either data transformation that required multiple operations in Make, or AI calls that were being charged separately anyway.
The consolidation made our budget forecast maybe 15% clearer in practice. Not revolutionary, but it meant finance stopped asking me every month why the automation allocations were all over the place. Sometimes that simplicity is worth something even if it’s not pure dollar savings.
One thing to keep in mind: consolidating the AI subscription is good, but it doesn’t solve the fundamental pricing difference between platforms. Make wants you to pay per operation. Zapier wants you to pay per task. A unified AI plan doesn’t change either of those dynamics.
What actually moved our numbers was realizing that our workflows had a lot of dead weight. Tasks that made sense logically but were expensive to execute. Once we switched to a time-based pricing model instead of per-operation, suddenly consolidating the AI made sense because we weren’t getting nickeled and dimed on every transformation step.
If you’re staying on Make or Zapier’s pricing model, the AI consolidation helps but it’s not a game changer. If you’re considering a switch to something with different pricing entirely, that’s where the real decision gets made.
The math changes when you factor in execution patterns. We found that consolidating AI subscriptions saved us about $800 per month, but the platform choice actually drove $3,000 per month in differences once we ran equivalent workflows through different systems. The AI consolidation is meaningful, but it’s not the primary lever you should be pulling on.
What matters more is understanding your actual workflow volume and complexity. If you’re doing a lot of API calls with light data transformation, Make’s operation counting kills you. If you’re doing heavy data processing with fewer external API calls, different platforms hit different pain points. The unified AI subscription is one optimization within a much larger financial picture.
The impact depends entirely on your current AI spending baseline. If you’re spending $2,000 monthly on AI APIs separately, consolidation to a unified subscription could save 40-50% there. But if you’re spending $300, the optimization matters less in percentage terms. The enterprise cost comparison between Make and Zapier involves too many variables: your task volumes, data processing needs, integration complexity, and scaling patterns. AI consolidation is one component of that analysis, not the determining factor.
Here’s the thing—consolidating AI subscriptions does move the needle, but the bigger shift happens when your platform itself uses execution-based pricing instead of counting every single operation.
We switched to a setup where we get access to 400+ AI models through one subscription, plus we’re only paying for actual execution time. That combination is what actually changed our financial picture compared to Make or Zapier.
Instead of tracking AI APIs separately, plus operation counts, plus task limits, we now just look at our execution volume. For our workflow patterns, that single consolidation lowered our total costs by around 60% compared to Make because we weren’t getting charged for every transformation step.
The unified AI access simplifies budgeting because everything flows through one pricing model. No more Excel sheets tracking five different API charges. It’s easier to forecast and easier to justify to finance.
If you want to run the numbers on your actual workflows, you can test this without a full migration. That’s what helped us make the decision: