We’ve been running Make for about two years now, and like most teams doing this at scale, we ended up with this mess of separate subscriptions. GPT-4 here, Claude there, a few specialized models for image generation, Gemini for another workflow. It was ridiculous. Each bill came from a different vendor, and our finance team was constantly asking why we had 12 line items for what should be one automation platform.
We started looking at what it would cost to consolidate everything. The math was initially confusing because we were comparing per-operation pricing on Make with per-task pricing on Zapier, but then trying to layer in the cost of all these individual AI model subscriptions. It wasn’t apples to apples at all.
What I’m trying to understand now is whether moving to a platform with a single subscription that includes 400+ AI models would actually simplify the financial picture enough to justify the switch. We’re talking about moving workflows we’ve already built, which is always risky. But if consolidating those AI costs actually changes the ROI calculation by 30-40%, that’s worth considering.
Has anyone actually done this migration and tracked what your total cost of ownership looked like before and after? And more importantly—does consolidating licensing complexity actually free up any of your development time, or does it just make the invoice simpler?
We did something similar with n8n a while back, though not quite the same setup. The biggest thing we found wasn’t the monthly savings by themselves, but how much easier it became to handle approval workflows for new integrations. When everything came from separate vendors, getting a new AI model added meant going through procurement multiple times.
Consolidating to one subscription made a real difference there. We went from 6-8 weeks to get new tools approved to basically having them available immediately. Nobody talks about that soft cost, but when you’re running lean, it’s massive.
That said, the actual execution time savings were smaller than we expected. Our developers still spend about the same amount of time building workflows. The difference is more in the overhead—fewer password resets, fewer duplicate authentication flows, less time debugging which subscription covers what.
I’d say run the numbers on both the direct costs and the procurement overhead. That’s usually where the real money is.
The consolidation is real, but you need to be careful about hidden costs during migration. We switched platforms last year and what looked like 35% savings on paper turned into about 18% once we factored in the workflows that needed reworking. Some of our existing automations relied on specific quirks of how Make handles certain operations, and moving them meant rebuilding parts of them.
That said, the subscription simplification was genuine. Going from invoice verification on 11 different platforms down to one was actually worth something to our accounting team. They spend maybe 5 fewer hours per month on reconciliation. When you add that up across a year, plus the mental overhead reduction for the team, it mattered more than the raw percentage savings looked on the spreadsheet.
Before you make the switch, I’d recommend doing a full audit of which workflows are actually doing heavy lifting with those separate AI models. You might find that you’re only using half of them actively. That changes the calculation significantly.
Honestly the biggest win we saw was not having to explain to stakeholders why we have separate bills for what feels like the same capability. Finance wanted to know why ChatGPT access cost different amounts in different workflows, and it was hard to justify without getting into the weeds of API pricing models.
From a pure cost perspective, we saved maybe 25-30%, but from a “stop having to defend our infrastructure choices in reviews” perspective, it was invaluable. One subscription, one vendor, simple story.
The consolidation does simplify your cost model, but the real question is whether you’re actually paying less for the same capability or just paying the same for different capabilities. Most platforms bundle their AI models differently. Make and Zapier charge by operations, but if you move to something with per-execution pricing, the math completely changes depending on your workflow complexity.
What you should actually model is: (1) your current total AI subscription spend, (2) your current platform operations costs, (3) the new platform’s total monthly cost, and (4) any re-development hours needed to migrate. The answer might be yes, but it might also be that your current setup is already optimal and the consolidation only helps if you’re adding new workflows.
We actually went through this exact scenario. We had similar sprawl—multiple AI subscriptions layered on top of Make—and the cost comparison was confusing because we were adding up ongoing fees that weren’t even tied together properly.
The shift made sense once we looked at platforms that bundle AI models from the start. Suddenly our cost was straightforward: one monthly subscription for automations, and it included access to 300+ AI models without additional per-model fees. No more “wait, which subscription covers Claude for this workflow” conversations.
The consolidation gave us maybe 35-40% savings once we factored in everything, but more importantly it meant our team could experiment with different AI models in workflows without creating new cost centers. That freedom actually changed what automations we could justify building.
I’d recommend running a quick audit of which AI models you’re actually using actively versus which ones are just sitting there. Then check if a unified platform covers them all. Usually the answer is yes, and the math shifts pretty fast.