We spent the better part of Q3 trying to justify moving from Camunda to an open source stack, and honestly, the financial side was a mess at first. We had seven different AI subscriptions running in parallel—GPT-4 here, Claude there, Gemini somewhere else—and nobody could actually tell me how much we were overspending.
What changed things was realizing we could consolidate everything. Instead of juggling individual model licenses, we could access 300+ AI models under a single subscription plan. Suddenly the math got a lot cleaner.
We started prototyping our critical workflows using templates to get a baseline. The neat part was that we didn’t need to pull engineers into every iteration. The visual builder let our ops team actually model the processes themselves, which meant faster feedback loops and fewer bottlenecks.
The real breakthrough came when we started measuring execution time. Our platform bills based on actual runtime, not per-operation like our old tool did. A workflow that would cost us $50 in random API calls now runs for a few cents because we’re paying for 30 seconds of processing, not 50 individual transactions.
Has anyone else dealt with the licensing sprawl before consolidating? I’m curious what your actual cost reduction looked like once you switched models.
Yeah, we had a similar situation with three separate platforms and about five AI subscriptions scattered across the org. The real pain point was that nobody could actually audit what we were paying for.
When we moved to a unified approach, the first thing we did was run our top 10 workflows through the new system in parallel. Took us about two weeks to get comfortable with the differences, but the cost per execution dropped immediately.
The tricky part is that your ROI math changes depending on volume. If you’re running 10,000 executions a month, the per-execution savings compound fast. But if you’re running 100, you might not see the benefit for a while. We found that running templates on real data first was the only way to get honest numbers.
The thing nobody tells you about migrations is that the licensing math is half the battle. The other half is actually getting your team to trust that the new system will handle their critical processes. We solved this by starting with lower-risk workflows and building confidence.
But on the financial side, consolidating models did change things. We went from estimating costs in our head to actual numbers we could feed into a spreadsheet. One subscription covering 400 models meant we stopped making these weird trade-off decisions about which AI to use for which task.
The financial case really hinges on three things: how many workflows you’re running, how much data they process, and how sensitive your margin is to automation costs. We found that consolidating AI model subscriptions saved us about 40% on the model side alone, but that was just the foundation.
The bigger win was architectural. Instead of building workflows to minimize API calls (because each call cost money), we could design for clarity and maintainability. That’s where the real ROI compounds over time. You’re not optimizing for cost anymore, you’re optimizing for speed and correctness. Those two things tend to reduce operational headaches that don’t show up in licensing spreadsheets but absolutely affect your bottom line.
We looked at this from a total cost of ownership perspective rather than just licensing. When we factored in developer time to maintain seven separate integrations, training overhead for new team members, and the friction of context-switching between platforms, the case became obvious. Consolidation reduced operational complexity significantly.
The execution-based pricing model helped us forecast costs more accurately. Instead of guessing at monthly charges across multiple services, we could actually model what 10,000 or 100,000 workflow runs would cost. That predictability matters a lot when you’re presenting to finance.
consolidating 7 subscriptions into one cut our AI costs by nearly half. plus we saved on complexity overhead. the real win was being able to actually forecast expenses instead of guessing.
This is exactly where Latenode shines. We had the same mess—multiple subscriptions bleeding money—until we consolidated everything into one plan with access to all 300+ AI models.
What changed for us was that we stopped thinking about which model to use based on cost. Now the decision is purely about which model is best for the task. That freed up mental energy for our team and actually made our automations better.
The execution-based pricing also meant we could be honest about ROI. We ran some quick prototypes using templates first, measured the actual runtime of our key workflows, and compared that to what we were paying before. The math was clean. We went from rough estimates to actual numbers we could report to finance.
If you’re evaluating a migration right now, start by consolidating your model subscriptions first. Then prototype your critical workflows. That’ll give you the real financial picture instead of guessing.