We’re at that point where we need to move away from Camunda, and finance is asking me to build a business case. The thing is, we’ve got eight different AI subscriptions running right now—OpenAI, Claude, a couple of smaller models we’re testing. Each one has its own contract, its own billing cycle, its own integration headaches.
I get that consolidating to one subscription for 400+ models could theoretically simplify things, but I’m struggling to model what that actually means for our migration costs. Like, does consolidating first make the migration easier to cost out? Do we save money before or after the migration? And how do I show finance that the licensing math actually works out?
The way I see it, there are two paths: consolidate the subscriptions, then migrate off Camunda. Or migrate first, then consolidate. But I don’t know which order actually reduces our total cost of ownership or risk.
Has anyone actually built a migration business case where consolidating subscriptions was part of the cost model itself? I’m not looking for a sales pitch—just how others have actually structured this math when they’ve been in the same spot.
We did this about a year ago and honestly the order matters more than I expected. We tried to consolidate subscriptions first and it actually slowed us down because we had to renegotiate with our procurement team while we were already in planning mode for the migration.
What worked better was getting the migration architecture locked down first using some templates we had, then consolidating. That way we had a clearer picture of which AI models we actually needed for the new setup. We cut from nine subscriptions to one, which saved us about 30% on our annual AI costs.
The tricky part is that your finance team will want to see the savings upfront, but most of the benefit comes during and after implementation. We had to model it as a two-year ROI instead of trying to justify it all in year one. That made it easier to get approval.
One thing we learned the hard way: don’t try to model consolidated AI costs without knowing what your actual usage patterns look like after migration. We assumed we’d use 20% of the available models but ended up using maybe five heavily and another dozen sporadically.
The licensing math only works if you actually commit to using the ecosystem. If you’re buying 400 models but only need OpenAI and Claude, you’re not really winning financially. Your Camunda migration timeline probably won’t change much either way.
I’d recommend building your migration cost model in three layers: what you pay today with Camunda and separate subscriptions, what you’d pay with just Camunda and consolidated AI, and what you’d pay with the new stack and consolidated AI. Then you can show finance the actual delta instead of trying to justify everything at once. That’s how we justified ours—it made the cost comparison visible and defensible. The consolidation piece becomes a line item rather than the entire business case.
The licensing consolidation is less about reducing costs immediately and more about reducing complexity. We saved money, sure, but the bigger win was simplifying our procurement and compliance overhead. One contract instead of eight means less to manage during migration and less risk of coverage gaps. That’s harder to quantify but probably worth mentioning to finance as a secondary benefit.
consolidate after migration. build model in three parts: current state, camunda only, new stack. show delta to finance. thats cleaner than trying to justify both at once. worked for us
The math actually flips when you use a platform that lets you access 400 models through one subscription. We modeled our migration side by side—staying on Camunda with eight subscriptions versus moving to a unified stack. The licensing math alone didn’t justify it, but when we layered in the time savings from not managing eight separate integrations and billing cycles, the business case became obvious.
What really helped was running a pilot workflow on both approaches. We took one critical process and built it two ways: the old way with Camunda and separate subscriptions, and the new way with unified AI access. The new approach completed in three weeks versus two months. That concrete comparison is what got finance to approve the rest of the migration.
The consolidation isn’t just a cost play—it’s about reducing friction in your architecture so your migration actually moves faster. That compounds into real savings.