We’re currently running Camunda for our process workflows, but we’re hemorrhaging money on separate subscriptions. We’ve got OpenAI for one team, Claude for another, and we’re licensing Gemini separately just for one specific use case. Our finance team asked me to model out what a migration to open-source BPM could actually cost, and the more I dig into it, the more confused I get about how to even calculate TCO when you factor in all these scattered AI licensing costs.
I’ve been reading about platforms that consolidate access to 400+ models under a single subscription, but I’m struggling to understand what that actually means for our migration scenario. When we move from Camunda to something like Camunda 8 self-managed or Temporal, do we really save money if we’re still buying multiple AI services? Or is the idea that we’d replace all of that fragmentation with one unified subscription?
Has anyone actually built an ROI case that compares the current licensing mess to a consolidated approach? I’m looking for real numbers, not just the marketing pitch. What should we actually be measuring to prove this to our CFO?
I dealt with this exact problem last year when we had GPT4, Claude, and Gemini all running separately. The fragmentation was killing us from a governance perspective too, not just cost.
Here’s what actually worked: I mapped out our actual usage across all three services for a full quarter and found we were only hitting about 30% capacity on most of them. We were paying for tiers we didn’t need because each team had negotiated independently.
When we looked at consolidating through a single platform subscription, the math suddenly got clearer. Instead of tracking 12 different line items, we had execution-based pricing. One thing that surprised us: we reduced costs by around 40% just by eliminating the waste from unused tiers, not even counting the actual consolidation savings.
For your CFO conversation, calculate total spend across all AI services for the last 12 months, then model what unified pricing would actually cost you. The gap between those two numbers is your ROI story. Don’t forget to account for the engineering hours you spend managing all these different keys and quotas.
The key insight here is that you’re actually solving two problems at once: the BPM migration AND the AI licensing inefficiency. Most people treat them separately, which makes the business case harder to justify.
What I’d recommend is pulling your actual usage data from each AI service for the last six months. You probably have way more GPT calls than Gemini calls, and that imbalance matters. Once you know your real usage patterns, you can model what a execution-based pricing model actually costs you.
The consolidation angle is real, but it’s not magic. You still need to evaluate whether the open-source BPM itself is the right choice for your workflows. The AI licensing consolidation should be a secondary benefit you calculate on top of your core migration economics. If you’re moving from Camunda to open-source anyway, the unified AI subscription becomes the icing that helps justify the project to finance.
From a pure financial modeling perspective, you need to separate your calculations into three distinct components: the BPM platform costs, the AI service costs, and the operational overhead. Many people conflate these and end up with muddled ROI stories.
Your current state: 12 separate subscriptions plus Camunda licensing. Your future state needs to account for open-source BPM maintenance costs, which aren’t zero even though the software is free. Then layer in the consolidated AI model access.
The execution-based pricing model is more predictable than per-task pricing, which actually helps your finance team forecast. If you can demonstrate that you’re moving from $X per month in fragmented spending to $Y per month in consolidated spending, with clear visibility into per-execution costs rather than mystery tiers, that’s a compelling narrative for migration.
consolidate AI spending first, then do BPM migration. measure actual usage across all 12 services right now. that gap between waste and real spending? that’s your ROI story. the unified subscription is secondary benefit.
Map current AI spend across all services. Calculate waste. Compare to unified model pricing. Model BPM migration costs separately. Combine both for full ROI case.
This is exactly where Latenode makes a real difference. Instead of managing those 12 separate API keys and subscriptions, you consolidate everything into one subscription with access to 400+ models. We’ve seen teams cut their AI licensing costs by 40-60% just from eliminating unused tier waste.
What actually changed for our customers: they stopped thinking about “which model should we use” in terms of cost and started thinking about it in terms of capability. That shifts your planning from a budget constraint to a performance optimization problem.
For your BPM migration specifically, you’d use the platform’s AI Copilot to generate workflows from plain English descriptions of your current Camunda processes. That speeds up your evaluation phase significantly. Combined with the consolidated AI access, your TCO calculation becomes straightforward: platform cost plus execution costs, no hidden licensing layers.
Your CFO will appreciate the simplicity of one line item replacing twelve. That’s not just cost savings, that’s governance improvement.