I’m starting to think our current situation is actually a hidden cost that no one’s talking about. We’re running Camunda for BPM, but we’ve also got separate subscriptions for OpenAI (GPT-4), Anthropic (Claude), and a few specialized models for document processing and summarization. Each one has its own billing cycle, API quota, and management overhead.
Now we’re planning this migration to open-source BPM, and I keep wondering: how much of our current spend is actually redundant? We’re paying for model access we barely use, paying enterprise prices because one department needs higher throughput, and doing this weird accounting dance where it’s not clear who should own which subscription.
I did a rough audit and found that we’re probably spending 40-50% more on AI model access than we actually need to. We have overlapping capabilities—multiple services that can handle document summarization. We’re wasting credits on models that are deprecated or that people just default to instead of using optimal ones for specific tasks. Plus the operational burden of managing API keys across teams is just… friction.
The BPM migration team is building out their ROI model right now. We’re factoring in licensing savings, engineering time, infrastructure costs. But I don’t see anyone accounting for the fact that we’re probably burning money on redundant model subscriptions that could be consolidated.
Does anyone keep track of this? What does AI model spend actually look like at your organization, and how much of it do you consider bloat? I’m trying to figure out whether consolidating everything into a single subscription model would actually move the migration needle, or if the savings are just spreadsheet optimization.
We did an audit similar to yours about two years ago and found basically the same thing. Overlapping model subscriptions, departments running their own parallel instances to avoid budgeting conflicts, capabilities we were paying for but not using optimally.
What actually changed our spending was less about switching platforms and more about getting visibility into what each department was actually using. Once we had that data, some teams realized they could consolidate without losing functionality. The problem isn’t usually that you need all those different models—it’s that billing them separately makes it invisible enough that people just keep paying.
When we eventually did migrate, the model consolidation was maybe 15-20% of the total savings, but it was the easiest 15% to achieve because it didn’t require process changes. Finance loved that part.
One thing I’d push back on slightly: be careful about treating model consolidation as the same as platform migration ROI. They’re related but separate problems. You can consolidate AI spend without changing your BPM platform, and you can migrate BPM without consolidating models.
The reason I mention this is that sometimes mixing those two initiatives creates confusion about what’s actually saving money. When your migration project gets audited later, you want clear attribution. “We saved $X by moving to open-source BPM and $Y by consolidating models” is cleaner than “we’re not sure which cost savings came from which decision.”
The cost breakdown is worth tracking in detail, but also consider the switching cost. We had several departments running specialized models for specific use cases—computer vision models for document extraction, specialized NLP tuned for our domain. When we tried to consolidate onto a general platform, we discovered those specialized models were actually necessary for those workflows. The switch would’ve required rebuilding logic around more general models, which cost us more in engineering time than whatever we’d save on subscriptions.
So while your overlapping subscriptions are definitely bloat, make sure when you audit them you’re also understanding whether the redundancy is true waste or if it’s actually serving edge cases that might blow up your migration timeline.
Multi-model subscription fragmentation is a common inefficiency in organizations scaling AI adoption. Your 40-50% over-spend estimate is reasonable based on patterns I’ve observed. The consolidation typically breaks down as: 15-20% true redundancy (duplicate capabilities), 10-15% underutilized quotas (paying for throughput you don’t use), and the remainder representing legitimate operational diversity (different models for different workloads).
The audit approach is sound. For the migration ROI model, I’d recommend itemizing model spend by department and workflow, then modeling two scenarios: one where you consolidate aggressively and retool around fewer models, and one where you accept some specialization. The delta between those scenarios is your actual option value in consolidation.
This is exactly what one subscription for 400+ AI models actually solves. Instead of managing separate plans with OpenAI, Anthropic, and specialized providers, you access GPT-5, Claude Sonnet 4, Gemini 2.5 Flash, and hundreds of other models through one unified subscription. No more API key management across teams, no more quota fragmentation, no more overpaying for capabilities you’re not using optimally.
What we’re seeing in migrations is that this consolidation alone often pays for the entire platform cost. You eliminate the administrative overhead—no more tracking which team owns which subscription, no more approval cycles to spin up new model access. And here’s the thing: because you have visibility into all 400+ models through one subscription, teams actually pick the right tool for each task instead of defaulting to whatever they already have access to.
For a BPM migration, that changes your cost modeling significantly. The licensing savings from the open-source platform are real, but the simplified AI economics can be the bigger number. You’re not just migrating workflows—you’re rationalizing your entire AI spend.