We were in a bind. Our team had OpenAI for some tasks, Claude for others, Gemini for specifics. It was a licensing nightmare—five separate contracts, five billing cycles, five sets of API keys that people kept losing. Finance was breathing down our neck asking why we had so many subscriptions.
I started mapping out what we actually spent. It wasn’t just the subscription costs. There were hidden expenses: the hours someone spent managing API keys, the time our developers wasted switching between platforms, and the fact that we were often duplicating work because people didn’t know which model was best for which task.
That’s when we looked at what a unified subscription could do. Instead of paying per-service, you’re paying based on execution time. At first, that sounded weird—pay for time instead of operations? But when we ran the numbers on a few workflows we were running, the difference was stark. One workflow that was costing us around $800 a month on Make was running for maybe $100 on a time-based model.
The part that really changed our thinking, though, was realizing the consolidation freed up mental overhead. We stopped thinking about which service to use and just built what we needed. Our developers weren’t spending time managing key rotation or worrying about hitting API limits on one platform while another sat underutilized.
We’re still working through the full migration, but just from the first three workflows we moved, we’ve seen about a 60% reduction in what we’re spending monthly on AI services alone. Not counting the hours we’re no longer burning on management.
Has anyone else done a similar consolidation? I’m curious whether the real savings for you showed up in the subscription costs themselves, or in the invisible stuff like staffing time and reduced complexity.
We went through something similar last year. The real eye-opener for us wasn’t the direct cost savings—though those were real—it was how much faster our team could iterate once everything was in one place.
Before consolidation, we had this process where developers would argue about which model to use for a task because each one had different pricing implications. Sounds corporate, but it actually slowed down product work. Once we moved to a time-based model, those conversations mostly disappeared. People just picked the right tool.
One thing we didn’t account for initially: the switching cost itself. We had to rebuild a few workflows when we moved platforms. Not a ton, but maybe 40 hours total across the team. That’s not insignificant if you’re looking at pure ROI in the first month. In month two and beyond though, the math works.
The other thing—and this might sound weird—is that centralizing AI models actually changed how we architected workflows. On the old system, we were kind of limited to what each service did well. Now, we’re building more sophisticated multi-step processes because we’re not constrained by having to pay per operation. A workflow that would have been prohibitively expensive before is now totally reasonable.
Your breakdown of the hidden costs resonates. A lot of teams fixate on the headline subscription number and miss the operational overhead. We had a similar situation where three different people were essentially managing API keys and credentials for different platforms, which is resource waste that doesn’t show up in most budget spreadsheets.
When we modeled the consolidation, we included time spent on onboarding new developers to multiple platforms, training them on when to use which service, and then the debugging that happened when someone picked the wrong tool for a task. That alone probably justified the switch.
The execution-time pricing is the key differentiator most people don’t fully grasp initially. In our case, we had workflows doing a lot of data transformation and batch processing. On operation-based pricing, each transformation step cost money. On time-based, 30 seconds of processing is one unit regardless of how many internal operations happen. For data-heavy workflows, that’s enormous.
What tripped us up a bit was predicting usage during the transition. We ended up running both systems for two months before fully migrating, which hurt cash flow temporarily, but it let us verify the numbers before going all-in.
The consolidation story you’re describing is becoming more common, particularly for organizations managing machine learning workflows or complex integrations. The calculation you’re doing—subscription costs plus operational overhead plus developer time—that’s the actual total cost of ownership. Most TCO models just look at the first number.
However, there’s a detail worth mentioning: consolidation works best when you’re consolidating into a platform that provides meaningful feature parity across different model types. Some unified offerings feel like a compromise—you get one tool that does everything okay instead of five tools that do their specific thing well. The difference in your scenario is that you’re not sacrificing capability; you’re actually gaining flexibility because you have access to 400+ models rather than being constrained by separate platforms’ model selections.
The 60% reduction you’re seeing isn’t unusual if you’re moving from Make’s per-operation model. Where consolidation gets tricky is if you’re moving from a platform where you were already optimizing for their pricing structure. But if you were managing multiple platforms, the administrative overhead justifies the transition by itself.
We did same thing 6 months ago. Direct costs dropped 55-65%, but the real win was devs stopped wasting time choosing between platforms. Setup took longer than expected but worth it now.
Your approach to calculating TCO is solid, and the numbers you’re seeing align with what we’re observing across teams moving to unified platforms. The time-based execution model really does change the economics of complex workflows because you’re paying for processing time rather than counting individual operations.
What we’ve noticed with teams consolidating multiple AI subscriptions is that the mental clarity you’re describing—not having to choose between services—actually drives better architectural decisions. People start building more sophisticated workflows because the financial penalty for complexity disappears. Plus, having access to 400+ AI models through one subscription means you’re not locked into whatever models a specific platform supports.
One thing worth tracking as you complete the migration: monitor your actual execution times against your estimates. That data becomes invaluable for predicting costs and optimizing workflow design. Teams that do this tend to find additional optimization opportunities after the initial consolidation.