I’ve been trying to build a proper cost model for evaluating whether we should consolidate our AI services, but I’m running into a structural problem with how to actually account for everything.
Right now we have:
- 10+ separate AI subscriptions (different vendors, different pricing models)
- Self-hosted n8n infrastructure (server costs, database, maintenance)
- Engineering time spent managing integrations and keeping everything updated
When I try to project what consolidation would look like, I can calculate the new licensing cost easily enough. But the infrastructure cost question gets messy. We’d still need self-hosted n8n regardless, so how do I actually isolate the cost that’s specific to managing multiple AI subscriptions versus the baseline infrastructure cost?
And then there’s the operational piece. How much of our engineering team’s time is actually consumed by managing all these separate vendors, and how much would that free up after consolidation? Those are the numbers that feel hardest to lock down.
I’ve seen some people just look at subscription costs and call it done, but that feels like we’re missing half the picture. Has anyone figured out a practical way to build this model that actually accounts for the operational complexity without just making up numbers?
The way I’ve seen this actually work is to break it into layers. Start with pure licensing: what you pay today versus what you’d pay tomorrow. That’s the easy number.
Then layer in infrastructure: self-hosted costs don’t go away, but they might go down if consolidation lets you reduce duplicate systems or simplify your stack. We ended up decommissioning a few proxy services and monitoring tools once we consolidated, so that was real savings.
The operational piece is where most teams mess up. We tracked time sheets for a month on everything related to managing AI vendor relationships—procurement, credential rotation, integration maintenance, debugging vendor-specific issues. It was shocking. Once we had that number, we could project how much of it would evaporate after consolidation. Not all of it, but probably 60-70%.
The real breakthrough was realizing the infrastructure layer isn’t actually a variable in the consolidation decision. It’s a constant. The consolidation ROI comes from licensing and ops, not infrastructure.
You need to separate fixed costs from variable costs. Self-hosted infrastructure is mostly fixed—you’re paying for the servers regardless. The consolidation affects licensing (direct cost) and engineering time (indirect cost). Model them separately.
For licensing, it’s straightforward multiplication. For the operational cost, I’d recommend looking at specific activities: How many hours per month does your team spend on credential management? How often do you rebuild integrations when vendors change their APIs? How much time goes into compliance reviews for each vendor? Those are the things that actually shrink when you consolidate.
Once you have those numbers, the TCO becomes three separate equations. The self-hosted piece stays the same. The licensing goes down. The ops burden shrinks but probably doesn’t disappear entirely. Add them up and you get your real cost delta.
The correct framework separates fixed infrastructure costs from variable operational and licensing costs. Your self-hosted n8n infrastructure is a sunk cost and baseline—it should be excluded from the consolidation ROI analysis. Instead, focus on the delta: current licensing spend plus the fully-loaded cost of managing multiple vendors versus projected licensing spend plus estimated operational overhead post-consolidation.
Include in your operational cost analysis: time spent on vendor management, API credential rotation and security reviews per vendor, system integration maintenance, and compliance overhead per integration point. Express these in fully-loaded hourly rates. Once you have the current annual operational cost, project the percentage reduction you’d achieve through consolidation—typically 40-70% depending on how many vendors you’re managing. The consolidation ROI is then licensing savings plus operational overhead reduction, minus one-time migration costs amortized over your planning horizon.
infrastructure is fixed cost, not part of ur consolidation math. focus on: licensing savings + ops reduction - migration cost. thats ur real ROI
Model licensing and ops separately from infrastructure. Self-hosted cost stays constant. ROI comes from licensing reduction and ops time freed up.
I struggled with this exact modeling problem when we were evaluating consolidation. The breakthrough was realizing that self-hosted infrastructure is a constant you’re paying regardless—it shouldn’t muddy your ROI calculation for consolidation.
What actually moves the needle is licensing and operational overhead. When we tracked it, we found that managing 10 separate AI vendors was consuming roughly 240 hours per year in engineering time—credential rotation, troubleshooting vendor-specific quirks, updating integrations when APIs shifted. Once we consolidated to a single subscription covering 400+ models, that overhead dropped by about 65%.
The financial model became clear: current licensing spend (10 vendors) plus operational cost (at fully-loaded engineer rate) versus consolidated licensing spend plus reduced ops overhead. The infrastructure layer—self-hosted n8n servers—stayed exactly the same in both scenarios, so we treated it as a wash.
The ROI kicked in around month four of running the consolidated setup. After that, it was just pure savings.
If you want to see how platforms that consolidate AI access handle the licensing piece, check out https://latenode.com