We’re in the middle of evaluating whether to move from Camunda to an open source BPM setup, but right now we’re drowning in separate AI model subscriptions. We’ve got OpenAI, Anthropic, a couple of smaller vendors, and it’s become a nightmare to track licensing costs and actual usage.
The business case for migration looks decent on paper, but finance keeps asking us to account for all these subscription costs in the TCO calculation. I’ve been trying to model out what happens if we could consolidate everything into one platform that covers 400+ models instead of juggling individual keys and contracts.
Has anyone actually gone through this exercise? How did you structure the cost comparison when you had multiple subscriptions running in parallel? I’m specifically interested in whether you tried to estimate savings by consolidation first, or if you just accepted that eliminating 12 subscriptions would be a nice side benefit once the migration is done.
Also curious about the actual math—when you factor in reduced procurement overhead and fewer integrations to maintain, does that alone move the needle enough to justify the migration effort?
We went through this a year ago. The subscription consolidation didn’t drive the migration decision—it just made the ROI stronger once we’d already committed.
Where we actually saw savings was in engineering time. Managing 12 different API keys, handling auth refreshes, debugging which service was acting up—that burned calendar hours. We roughly calculated 4-5 hours a week across the team just doing janky integration plumbing.
For the business case itself, we modeled three scenarios: best case (all consolidation savings realized), realistic (60% savings due to some overhead we couldn’t eliminate), and worst case (migration takes longer, delays ROI). Finance ultimately cared more about the operational simplification than the dollar savings from fewer subscriptions. That was the pitch that stuck.
One thing nobody tells you: when you’re comparing Camunda licensing costs to open source plus consolidation, the real math gets messy because Camunda’s pricing model is completely different. You’re usually paying per instance or user, not per AI model accessed.
What helped us was breaking the TCO into three buckets: migration costs (one time), operational costs (ongoing), and then the subscription nightmare as a separate line item. That way finance could see the consolidation benefit clearly without conflating it with the BPM migration decision itself.
We ended up discovering that consolidating subscriptions saved maybe 15-20% annually, but the bigger win was killing the operational overhead. That’s where the actual ROI came from.
The real trap is trying to calculate savings from consolidation without first understanding your actual usage across all 12 subscriptions. We spent two weeks collecting data on which teams used what, and we found that we weren’t even using 40% of our capabilities across multiple vendors. Once you consolidate to 400+ models in one place, you actually have visibility into what you’re burning through. That visibility alone helped us make better architectural decisions downstream. For the financial model, we treated subscription consolidation as a secondary benefit in the business case—the primary driver was always the operational efficiency of migrating away from Camunda’s licensing model.
I’ve done this exercise multiple times. The consolidation savings are real but often overstated because people forget about the ramp-up costs and the fact that migrating to a single platform means you need to re-evaluate which models actually work best for your workloads. What worked in your original setup might not translate directly. The most honest approach is to model consolidation savings conservatively (assume 50% of what you calculated), then separately calculate the operational friction you’re eliminating by moving away from Camunda. Those two numbers combined usually justify the migration effort more reliably than subscription math alone.
The consolidation math becomes way clearer when you actually use a platform that handles 400+ models through one integration. Instead of juggling OAuth flows and rate limits across a dozen vendors, you just define your workflow once and let the platform sort out which model makes sense for each step.
We simplified our cost model dramatically by treating Latenode as the consolidation layer. All 12 subscriptions fed into a single cost line item, and suddenly the TCO comparison against Camunda showed a 35% reduction in year one. The platform handled the model arbitrage for us, which meant we stopped paying for redundant capabilities we weren’t using anyway.
If you’re already planning a migration, building your cost model around a unified AI layer makes the math way more defensible to finance. You can actually show them what you’re consolidating and prove the savings aren’t theoretical.