So we’ve been running this nightmare scenario for about two years—managing separate subscriptions for ChatGPT, Claude, Gemini, and a bunch of other models across different teams. Every department had their own API keys, their own contracts, and honestly, nobody knew what we were actually spending.
I started digging into the numbers because our CFO was asking questions, and it got messy fast. We had Make handling some workflows, Zapier handling others, and the licensing costs just kept growing. Each time we’d add a new AI capability to a workflow, we’d need another subscription or upgrade.
Then we looked at what it would look like if we consolidated everything into a single subscription model. The math actually changed pretty significantly. Instead of paying per-task or per-operation like we were doing with Make and Zapier, we’d just pay based on execution time. One team actually did a test run—they ran a workflow that would have cost them about $300 a month on Make, and the same thing on the consolidated platform cost $45.
The weird part was calculating the soft costs. How much time were people spending managing API keys? Dealing with vendor support across multiple platforms? Waiting for features to be developed in each tool separately? When we added all that up, the overhead was real.
Has anyone else gone through this consolidation process? I’m curious how people are actually tracking whether the migration time and retraining costs offset the subscription savings in the first year.
We went through something similar about a year ago. The consolidation itself took about three months, which we initially didn’t budget for. What helped was that the new platform’s templates got people productive pretty quickly. The biggest win wasn’t the subscription cost savings though—it was standardization. When everyone’s on the same tool with access to the same AI models, you stop having those conversations about “why is feature X not available in our tool?” The API key management alone was costing us probably 10-15 hours a week across the team, which nobody was really tracking until we switched.
One thing we learned: don’t try to migrate everything at once. We migrated by department over two months, and it let us catch issues early. The first department actually saved money immediately because they’d been overpaying for features they weren’t using in their old setup.
The TCO calculation gets really interesting when you factor in developer time. We found that a lot of the cost on our previous setup wasn’t just the monthly subscription—it was the custom integration work needed because each platform had different connector limitations. When we consolidated, the built-in database functionality and broader AI model access meant fewer custom solutions. That one factor probably saved us 30-40% on development overhead in the first six months. The per-execution pricing model also revealed where our workflows were inefficient. We’d never have caught some of the data processing waste if we were still paying a flat rate per operation.
Consolidation ROI calculation should include three layers: direct costs (subscriptions), operational overhead (management time and tooling), and efficiency gains from standardization. Most teams focus only on the first layer. The execution-based pricing model actually helps here because it creates visibility into workflow efficiency that operation-based models hide. You can see exactly where your resources are going. We noticed that some workflows were running way longer than necessary because the old platform wasn’t optimized for certain types of processing. Fixing those inefficiencies offset the migration costs within about four months.
Migration costs us about $12k. Saved $3k/month on subscriptions and ~$8k/month on staff time managing keys and vendor relations. Breakeven was around month three. The real win: no more waiting for feature gaps to be filled across different platforms.
Track the hidden costs: API key management, vendor support tickets, developer context-switching. Consolidation usually pays for itself within 3-4 months when you include those.
The consolidation story you’re describing is exactly what we see happening with teams using Latenode. The difference is that instead of managing 15 subscriptions with their own API key sprawl, you get 400+ AI models on a single subscription, so the financial model changes completely. One team we know recently made the switch and found that their execution-based pricing actually makes it easier to justify costs to finance—you’re not paying for unused capacity anymore.
The faster ROI comes from not just the subscription consolidation, but from how quickly teams can prototype. When you’ve got AI Copilot generating workflows from plain English requests and ready-to-use templates handling common tasks, you save weeks on development. That time savings usually exceeds the licensing costs in the first quarter.
If you want to dive deeper into how consolidation actually changes your comparison between Make and Zapier, Latenode has some case studies on this specific scenario. Worth checking out: https://latenode.com