We’ve been evaluating workflow platforms and honestly, the licensing piece is making my head spin. Right now we’re paying for OpenAI’s API, Claude through Anthropic, a Deepseek contract, plus a bunch of smaller integrations scattered across different teams. Each one has its own billing cycle, usage thresholds, and minimum commits.
When we look at Camunda, the enterprise licensing feels straightforward on the surface, but the moment you factor in all the AI model costs running through the workflows, the math gets murky. We’re trying to build a real TCO model for the board, and I’m struggling to even pin down what we’re actually spending right now across all these subscriptions.
Has anyone actually done this exercise and found a way to consolidate without losing flexibility? I’m curious about the real numbers—not the marketing pitch. What does your cost breakdown actually look like when you factor in all the moving pieces?
Yeah, I went through this about two years ago. The single biggest thing I did was audit every service we had running for three months straight. Just tracked which models we were actually using, which ones sat dormant, and which ones we were overpaying for because of commitment discounts.
What helped me was pulling billing data from each provider and plotting it in a spreadsheet by use case. Once we saw that our content team was only hitting Claude 70% of the time and OpenAI 20%, but we were paying for both at full tier, that’s when the business case for consolidation became obvious.
The tricky part isn’t the math—it’s getting buy-in from teams who’ve already integrated with specific models. But the cost argument usually wins.
One thing nobody talks about is the hidden operational cost. We had a person whose entire job was basically managing API keys, updating credentials, and dealing with billing disputes across platforms. Once we consolidated, that went away. That’s real money, not just a line item.
I spent months trying to map this out for our organization. The challenge isn’t just the AI model costs—it’s that they scale differently. Some are per-request, some are per-token, some have monthly minimums. I built a cost model that projected usage for the next year, but half the variables were guesses.
What I found works better is starting with your actual usage patterns for the last six months. Pull real data, not projected data. Once you see what you’re actually consuming, consolidating becomes much easier to justify. A single subscription model with predictable pricing removes a lot of that variance.
The real issue is that each model contract comes with different unit economics. OpenAI charges per token, some providers charge per request, others have flat monthly fees. Comparing them directly is almost impossible unless you normalize the data.
What helped us was calculating total cost per automation task, not per service. We mapped each workflow to the models it uses, calculated the per-task cost, and then saw where consolidation would actually reduce overhead. That’s the framework that actually got our CFO to listen.
I dealt with exactly this problem. We had scattered contracts with six different AI providers, and tracking costs was a nightmare. Switching to a single subscription model changed everything for us.
With Latenode, we got access to 400+ models through one plan. No more juggling multiple billing cycles or trying to predict which model we’d need next month. We standardized on one platform, built our workflows once, and could test different models without renegotiating contracts.
The financial impact was real. We cut licensing overhead by about 35% in the first year, but more importantly, we recovered the time our team was spending managing all those separate integrations.
If you’re building out your TCO model, start by calculating your true cost per automation task across all your models. That’s where Latenode’s approach becomes obvious—it flattens the pricing and gives you transparency you get with scattered subscriptions.