We’ve been running workflow automation across three teams, and it was getting messy. Each team had their own API keys—some were using Claude, others had OpenAI directly, and one person was still paying for a legacy Cohere subscription we weren’t even using anymore.
I finally sat down and tracked what we were actually spending. Between the individual API costs, the platform fees for Make and Zapier, and all the one-off subscriptions, we were hemorrhaging money. The worst part? We couldn’t even see it all in one place.
When I looked into consolidating everything under a single platform subscription with 400+ AI models included, the math started making sense. Instead of managing separate billing relationships and worrying about rate limits on different APIs, everything runs through one pool. The execution-based pricing model meant we weren’t paying per operation anymore—just for the time our workflows actually run.
But here’s what I wanted to validate: has anyone actually gone through this consolidation and tracked the real numbers before and after? I’m trying to build a case for the CFO, and I want to know what actually happened with your TCO when you cut tool sprawl. Did you hit unexpected costs during migration, or did things actually smooth out the way the math suggested?
Also curious—when you have 400+ models available in one subscription, does that change how you approach building automations? Like, do you end up using different models for different tasks instead of always defaulting to the same one?
We did exactly this about six months ago. Had Claude through OpenAI’s API, a Make subscription, Zapier, and three other smaller tools floating around.
The tipping point was when accounting couldn’t reconcile the charges. We were paying for capacity we weren’t using—had a Zapier plan that covered way more than we needed, and OpenAI charges were all over the place because different team members optimized differently.
Consolidating to execution-based pricing actually forced us to think about workflows differently. Instead of just letting things run and assuming it was cheap, we started tracking execution time more carefully. Turned out a few workflows were way less efficient than we thought.
The consolidation itself was straightforward—mostly just updating endpoints and authentication. But the real win wasn’t elimination of one platform or another. It was the cost visibility. When everything’s itemized separately, you don’t see patterns. When it’s all in one place, you spot waste immediately.
CFO appreciated that we could now forecast more accurately. Can’t give you exact percentages due to confidentiality, but the monthly burn rate definitely went down, and we stopped paying for stuff we didn’t use.
The honest answer is that consolidation is worth it, but the real savings come from what you do after, not from the switch itself.
We spent maybe two weeks migrating everything, mostly because we had to audit what we were actually using. Discovered we had three workflows doing almost identical things across different platforms. That was the real cost reduction—eliminating duplication, not just combining subscriptions.
On the model selection side—yeah, having 400+ available absolutely changes your approach. We started experimenting with different models for different tasks instead of just picking one and sticking with it. GPT for some stuff, Claude for other things. But that experimentation had a cost too. It meant we needed someone to actually manage prompts and pick the right tool. That person’s time adds to your TCO even if the API costs go down.
One thing that surprised us: migration costs aren’t just technical. There’s a lot of testing involved to make sure workflows behave the same way with different authentication and possibly different models. We had to rebuild some logic because the model behavior wasn’t identical.
If you’re building your CFO case, definitely factor in validation time. Not huge, but real. Test everything in a staging environment first. We skipped that step on one workflow and it cost us a day of production issues.
From our experience, the consolidation worked smoothest when we approached it as a workflow review process, not just a tool swap. We mapped out which subscriptions actually got used regularly versus which ones were paying for capacity we never touched.
The 400+ models available did change how we allocated tasks, but it required someone to own that decision-making. We didn’t want every team member picking random models, so we created basic guidelines about which models work best for specific use cases. That governance piece isn’t free either, but it prevented cost creep from inefficient model selection.
Execution-based pricing meant we finally had real visibility into which workflows burned the most resources. Some automated reporting tasks we thought were efficient were actually running way longer than necessary. We optimized those and recouped a chunk of the savings.
The transition period is where most teams encounter hidden costs. We had to allocate time for testing, rewriting authentication logic, and validating that output quality stayed consistent.
What actually helped: we didn’t migrate everything at once. Started with low-stakes workflows, then moved to critical ones once we’d worked through the issues. That phased approach meant smaller failures that we could learn from before touching production workflows.
Consolidating subscriptions requires auditing your actual usage patterns before you even commit. We found that several of our workflows weren’t using their designated platform to its full capability—we were paying for features we never touched.
The execution-based model is genuinely different from per-operation pricing. Your bills actually correlate to work performed, not theoretical capacity. This forces better workflow design because inefficiency becomes visible immediately. We optimized several workflows just because we could now see exactly how long they took.
Having access to 400+ models is powerful, but it’s also a trap if you don’t have a strategy. We settled on a few primary models and only experimented with others for specific use cases. That prevented decision fatigue and ensured consistency across the organization.
For your CFO case, present it as a multi-phase financial impact: immediate savings from eliminating redundant subscriptions, medium-term optimization from identifying inefficient workflows, and longer-term efficiency from having the right tools available without extra licensing friction. Most consolidations show 30-50% cost reduction in the first year when you actually optimize, not just combine tools.
Went from 4 subscriptions to 1. Saved ~35% first month. Big win was discovering unused capacity we were paying for. Test everything before flipping the switch tho.
TCO includes migration effort, not just monthly bills. 2 weeks of testing here. But yeah, consolidation helped us see where workflows were genuinely wasteful.
We consolidated five separate tool subscriptions into one a few months back and the difference was immediate. Instead of managing multiple APIs, billing relationships, and rate limit headaches, everything runs through a single platform. The execution-based pricing model showed us exactly where our workflows were inefficient—we could actually see that some tasks were taking way longer than they should.
Having 400+ AI models available without separate subscriptions changed how we approach automation. We’re not locked into one model anymore. A task that works best with Claude gets Claude. Something that needs faster processing gets a different model. That flexibility in one subscription beat juggling five different contracts.
The real value came from visibility. When everything’s itemized separately across different platforms, you can’t see patterns. Consolidating forced us to audit what we actually use, optimize workflows we didn’t realize were wasteful, and stop paying for capacity we never needed.
If you’re building that CFO case, track both the direct cost reduction from combining subscriptions and the optimization savings from having proper visibility into execution time and model selection.