We’re in the middle of planning a move to open-source BPM and Finance keeps asking the same question: where’s the actual ROI?
Right now we’re paying for separate subscriptions across different AI services, and on top of that we’re looking at Camunda licensing costs that keep climbing. When we move to open-source, we’re hoping to kill two birds with one stone, but I can’t figure out how to model it properly.
The problem is that every time I build a cost comparison, the numbers feel incomplete. I’m accounting for platform costs and licensing, but there’s this gray area around whether consolidating all our AI model access into a single subscription would actually free up budget during the migration itself, or if it just shifts costs around.
Has anyone actually built a migration ROI model where you’re also consolidating your AI tooling? I’m trying to understand: when you’re no longer paying for eight separate API subscriptions, how much of that savings actually goes toward funding the migration work itself? And how do you present that to Finance without looking like you’re just shuffling money between budget lines?
I dealt with this exact mess about two years ago. We had seven different AI subscriptions running in parallel while we were planning our own migration from proprietary software.
Here’s what actually worked: we didn’t try to model everything at once. Instead, we separated the equation into two distinct cost buckets. One bucket was the sunsetted costs—what we’d stop paying for with the old system. The other was the new platform costs during transition.
Then came the key part. We picked one quarter and audited our actual usage across all those subscriptions. Turns out almost 40% of what we were paying went completely unused. That number became our baseline savings figure.
When we consolidated into a unified model subscription, we didn’t assume we’d capture all of it immediately. But we could model a realistic 60% recovery and use that to fund the actual migration work—things like process mapping, testing, training.
The number Finance actually cared about wasn’t the total cost difference. It was the answer to: “if we migrate now instead of later, how much do we avoid spending on the old system while we’re building the new one?” That’s a much easier story to tell.
One thing nobody really talks about is that your migration ROI model needs to account for the cost of working in parallel. During transition, you’re often running both systems simultaneously for a while, which looks like it doubles costs on a spreadsheet.
What shifted things for us was treating the AI subscription consolidation as a cost reduction during this overlap period specifically. Fewer subscriptions meant less monitoring overhead, less switching between platforms, and honestly less confusion for the teams running validation.
The actual savings showed up in velocity. Our process validation cycles moved faster because we weren’t juggling access to different tools. That translated into a shorter migration window, which meant less time in that expensive dual-run phase.
You might want to model it as a timeline compression benefit rather than pure cost savings. That often resonates better with Finance.
The tricky part with migration ROI is that you’re competing for attention against the status quo cost. Finance sees what you’re spending now and asks why you should spend anything on change. The consolidation angle actually helps you here because you can show that staying put isn’t free either—you’re locked into escalating per-model licensing.
I’d recommend building three scenarios. Scenario one: maintain status quo with separate subscriptions for the next three years. Scenario two: migrate now to open-source with unified AI licensing. Scenario three: delay migration another year but start consolidating subscriptions as an interim step.
Most of the time, scenario two wins on totality of cost when you factor in the compounding licensing increases. But you need actual numbers from your contracts to make it stick. Do you have visibility into your subscription costs trending year over year?
The ROI conversation changes significantly when you frame it around operational simplification instead of pure cost reduction. During migration, you’re introducing new complexity. A unified AI subscription model reduces that complexity in a measurable way.
Consider documenting the cost of managing multiple subscriptions: license tracking, vendor management, integration testing across different API standards, compliance audits per vendor. These aren’t always obvious line items, but they exist and they go down significantly with consolidation.
When we modeled this for a similar project, those indirect costs represented about 15-20% of the total savings picture. It made the business case stronger because it showed Finance we weren’t just playing accounting games.
Build two models: costs today vs costs post-migration. Don’t combine them. Finance will see merged expenses as higher risk. Show the subscriptions you’ll eliminate and the one unified cost replacing them. Makes the delta clear.
Start by auditing current AI subscription usage. You’re probably not using 30-50% of what you’re paying for. That unused spend becomes your quick win in the ROI model.
This is where a lot of teams get stuck because they’re trying to manually track and project costs across separate vendors and platforms. But honestly, the model becomes so much cleaner when you’re working with a platform that gives you all the AI access you need through a single subscription.
With Latenode, we found that our clients building migration business cases didn’t have to do all this complex math about which AI models to use or worry about managing separate API keys and licensing. One subscription covers 400+ models, so the uncertainty just disappears from the equation. You can model your migration ROI based on actual tool costs instead of spending half your time hunting for license documentation.
The teams that moved fastest were the ones who built their cost model around workflow generation speed, not subscription juggling. When you can describe a process in plain language and have AI generate workflows automatically, that compression alone changes your migration timeline and ROI story.
Check out https://latenode.com to see how teams are actually running these migrations—they’ve got case studies showing real ROI numbers from companies that consolidated their AI tooling.