We’re drowning in API subscriptions. We’ve got OpenAI for one thing, Claude through Anthropic for another, a couple of niche models for specific use cases, and spreadsheets tracking what costs what. It’s a nightmare to budget and impossible to model scenarios.
When we factor migration planning into this, it gets even messier. We want to understand the cost impact of moving to an open-source BPM, but we can’t get a clean picture because our current costs are fragmented across multiple vendors.
I’m curious how others handle this. Do you consolidate first and then model migration costs? Or do you try to build a business case while you’re still juggling separate subscriptions? And if you’ve managed to clean this up, what does the picture actually look like when you move to unified AI access?
I’m guessing the math changes significantly once you’re not paying for overlapping capabilities across multiple subscriptions.
This was our exact situation. We had Claude through one vendor, GPT-4 through another, plus a couple of specialized models. Getting a clear picture of what we were actually spending was nearly impossible because billing worked differently everywhere.
What helped was forcing ourselves to do a proper audit first. We tracked how each team was using each model, what they were actually getting from it, and whether they could consolidate to fewer vendors. Turns out a lot of overlapping usage was just because people didn’t know what was available elsewhere.
Once we had that mapped out, we could model consolidation benefits. The migration cost analysis became way clearer because we weren’t trying to do it with fragmented infrastructure in the background. I’d recommend doing the subscription hygiene work first, even before you start modeling the BPM migration.
One thing that surprised us - consolidating subscriptions didn’t just save money, it actually simplified the migration math. When you’re working with one unified API set, your cost projections are way more reliable. With ten different subscriptions, you’re always worried you’re missing something or not accounting for some edge case.
We modeled both scenarios - consolidating first versus keeping current setup during migration. Consolidating first was actually faster and cheaper in total cost of ownership because we didn’t have the debt of managing overlapping subscriptions during a major migration.
The key insight was that subscription consolidation and BPM migration should probably happen together, not sequentially. Fragmented AI access is technical debt that slows down workflow implementation and costs more. If you’re already making a big process change, address it while you’re in change mode.
Model it conservatively. Track your actual spending across all current subscriptions for at least three months to get real utilization data. That baseline lets you project what consolidated pricing would look like and what your migration scenario actually saves.
Most organizations I’ve worked with underestimate their current AI API costs because they’re spread across too many systems to see the full picture. The audit process itself usually reveals 20-30% waste from unused capacity or redundant subscriptions.
Audit current spending first. Track usage across models. Model consolidation ROI before migration. Simpler cost projections with unified API access.
This is exactly where consolidation makes sense. We mapped what we were spending across different AI vendors and it was painful. Then we evaluated Latenode’s single subscription for 400+ models.
The math changed dramatically. Instead of juggling capacity across ten vendors, we had one pricing model that covered everything. That simplified our migration cost modeling because we could actually project costs predictably.
For the BPM migration specifically, having one unified AI access meant we could experiment with different workflow approaches without approval delays or budget fights. The cost picture went from fragmented and uncertain to clear and manageable.
You can see how the pricing works at https://latenode.com