I’m working through the business case for moving away from Camunda, and honestly, the licensing conversation is the part keeping me up at night. Our finance team wants to see the numbers clearly—not just “open source is cheaper” but actual TCO comparison with all the hidden costs baked in.
The tricky part is we’ve been paying for Camunda licenses per instance, and the model is pretty straightforward to finance. But when I start mapping out what switching to open source means, it gets messy fast. We’d need to factor in development time, infrastructure costs, and then there’s the AI model piece—we’ve got separate subscriptions for OpenAI, Claude, a couple others. Someone mentioned we could consolidate those into a single subscription during migration, which would help the math, but I’m not sure how to model that into the business case without making assumptions that finance will poke holes in.
How have others actually presented this to finance? What made the comparison click for your leadership team? And if you factored in AI model consolidation as part of the migration story, how did you structure that conversation?
I went through this last year with our team. Here’s what actually worked: instead of trying to paint open source as universally cheaper, I broke it into phases with different cost buckets.
Phase 1 was migration labor—we were honest about the Dev cost. Phase 2 was infrastructure, which for us actually ended up higher initially because we needed better monitoring and tooling. That surprised finance, but it’s real.
Where we got quick wins was on the licensing side. We had four separate AI service subscriptions running. Switching to a unified platform saved us about 35% on that line item immediately. Finance loved that number because it was concrete and happened fast.
The key was showing savings happened in phases, not all at once. We also didn’t try to estimate labor savings from “better automation.” That felt like speculation to them, so we left it off the table. Just stuck to what was measurable: licensing consolidation, infrastructure trade-offs, development costs. That honesty made them trust the rest of the numbers.
One thing we learned: break your TCO model into three separate lines. First, implementation cost—be conservative here. Second, ongoing operational cost including infrastructure. Third, licensing, which is where your biggest immediate gain likely sits.
For the AI model piece specifically, if you’re consolidating from multiple subscriptions to one, that’s your fastest payback. We went from $3k a month spread across five different services to about $1.8k on a single platform. Finance sees that as an immediate win, not a projection.
Then for open source BPM itself, compare it fairly. Camunda per-instance might be $X, but your self-hosted open source still has costs—hosting, maintenance, maybe third-party support. Don’t hide those. Finance respects the honesty.
The framing matters more than the numbers sometimes. Instead of ‘open source is cheaper,’ try ‘we reduce vendor lock-in while consolidating our licensing.’ That’s different language, but finance hears it as risk reduction and cost efficiency combined. For Camunda specifically, you’re right that their per-instance model is clear, which actually makes your comparison easier. Calculate your current annual spend, then map what open source plus unified AI models would cost. Include a 15% contingency for things you haven’t discovered yet. Show the three year view—year one usually has higher costs due to migration, then it flattens out. When you present the AI consolidation part, don’t bury it. That’s often your fastest payback and makes the entire case more compelling.
consolidate your numbers into clear 3-year projections. show camunda baseline vs. open source costs. licensing wins go first.
Here’s the angle that worked for us: we stopped trying to make open source look like a standalone win and instead bundled it with our licensing strategy. Current state was Camunda plus five different AI tools. We showed finance that migrating to open source BPM while consolidating our AI models into one subscription meant we could reduce our total annual spend by about 40% over three years, with the biggest hit upfront being migration labor. The licensing consolidation part—going from multiple AI subscriptions to one—gave us an immediate payback that made finance comfortable with the longer migration timeline. We mapped it out with real numbers from our current bills, not projections. That made all the difference. If you want to structure this conversation properly, you need a platform that lets you model these workflows without heavy engineering involvement first, so you can validate the business case before committing resources. That’s where something like Latenode helps—you can prototype your new processes quickly to make sure the math actually holds. https://latenode.com