We’re seriously evaluating a move from our current proprietary BPM setup to open source, but the finance team keeps asking questions I can’t answer with any confidence. They want to see the actual numbers—not just “open source is cheaper.”
Right now we’re spending money on licensing, but we’re also paying for multiple AI model subscriptions scattered across different tools because our workflows need different capabilities. It’s messy. The argument for open source makes sense in theory, but I need to model this out properly.
What I’m trying to figure out is: when you move to open source BPM, how do you actually calculate the total cost of ownership? Do you need to keep those separate subscriptions, or is there a way to consolidate them? And how do you factor in the migration effort itself—the time to recreate workflows, test everything, make sure nothing breaks?
Has anyone actually built a financial model for this transition that held up under scrutiny? What were the biggest cost surprises you didn’t anticipate?
We did this two years ago and honestly the biggest mistake we made was underestimating the testing phase. You can’t just assume your workflows will work the same way on a different platform.
For the subscription consolidation part though—that’s where we actually found real savings. We had something like seven different AI tools we were paying for separately. Once we moved to open source and standardized on fewer models, we could actually negotiate better rates. The key is figuring out which models you actually need versus which ones you added because they were “nice to have.”
One thing that helped us: we ran a pilot on one department first. That let finance see real numbers instead of estimates. The migration for that one workflow took about three weeks, and we could show them exactly what it cost in labor and what we saved monthly afterward. Then scaling to the rest was much easier to justify.
The finance conversation changed for us when we stopped thinking about it as “migration cost plus ongoing costs” and started breaking it into phases. Phase one was just getting one workflow working on the new platform. Phase two was the full rollout.
Phase one actually cost us money upfront—dev time, testing, mistakes. But phase two was way cheaper because we understood the patterns. Finance appreciated that breakdown because it wasn’t just a scary one-time number.
The tricky part nobody talks about is operational risk during transition. When I looked at our numbers, the licensing savings looked great until we factored in having two systems running in parallel during cutover. That costs money—people monitoring both, making sure data stays in sync, handling edge cases.
We ended up building a spreadsheet that tracked: old platform costs, new platform costs, parallel running costs, and migration labor. We ran three scenarios—best case, realistic, and worst case. Finance wanted the realistic one, obviously. That honest framing made them way more comfortable committing to the project because there were no surprises later.
In our migration, consolidating AI model subscriptions made a meaningful difference but not as dramatic as I initially thought. We went from paying roughly $800 a month across multiple vendors down to about $400-450 with a unified platform. The real savings came from needing fewer models once we stopped paying for overlapping capabilities.
The licensing math for open source itself is straightforward—lower upfront vendor costs. What gets complicated is the hidden costs: infrastructure, support, training your team on new tools, and the labor to actually build and maintain everything you no longer buy from the vendor. Make sure finance understands that open source isn’t free, it’s just a different cost structure.
We saved about 40% overall when we accounted for consolidating subscriptions and ditching vendor licensing. The migration cost us 200 hours of dev time upfront. After that, the savings kicked in immediately. Your mileage depends on workflow complexity tho.
This is exactly where a lot of teams get stuck, but there’s a smarter way to think about it. Instead of migrating blind, use a platform that lets you model the workflows first without committing to the whole migration.
With Latenode, you can take your process descriptions and have the AI generate migration workflows to compare against your current setup. That means you’re not asking finance to approve a migration based on guesses—you’re showing them actual, runnable workflows that prove the concept. You can even consolidate your AI model subscriptions into one plan instead of juggling multiple vendors, which cuts your subscription costs immediately.
Finance responds way better when you show them: “Here’s the workflow we’re moving, here’s the cost before, here’s the cost after, and here’s proof it actually works.” That’s the difference between a risky bet and a data-driven decision.