I’ve been wrestling with this for months now. We’re running n8n self-hosted across several teams, and the licensing picture is getting messy. Right now we’re paying for separate subscriptions to OpenAI, Anthropic, and a couple others, plus the n8n licensing on top. Every time a new AI model comes out that looks useful, I’m having to justify another line item to finance.
The thing that’s been bugging me is that I can’t actually see the full picture of what we’re spending. We’ve got some models being used heavily, others sitting there barely touched, and I genuinely don’t know if consolidating would save us money or just trade one mess for another.
I’m trying to figure out if there’s a framework people use to calculate this stuff. Like, when you’re looking at TCO, what are you actually including? Just the subscription costs, or are there hidden things I’m missing—deployment overhead, time spent managing integrations, that kind of thing?
Has anyone actually done the math on whether moving to a platform that bundles multiple AI models into one subscription actually reduces your total spend, or does the complexity just shift somewhere else?
We went through exactly this last year. The trap is thinking TCO is just subscription costs. We were paying for four separate AI services plus n8n, but the real hit was developer time.
Every time someone needed a different model, I’d have to set up new auth, integrate it, test it. Ended up being maybe 20-30% of our actual cost when you account for engineering hours. Once we consolidated everything to one subscription covering multiple models, that went away almost completely.
The other thing nobody talks about is procurement chaos. We had four different contracts, renewal dates all over the place, different support tiers. Just managing that was exhausting. The financial piece matters, but the operational simplification was honestly the bigger win for us.
One thing I’d add that caught us off guard: vendor lock-in costs aren’t always obvious up front. When you’re spread across multiple services, switching one is painful but isolated. When everything’s bundled, you want to make sure you’re not trading flexibility for cost savings.
That said, if the bundled option actually covers what you need, the consolidation math usually works in your favor. Just make sure you’re not paying for coverage you don’t need. We spent a month auditing which models we actually used across all our workflows. Turned out we were paying for stuff that hadn’t been touched in six months.
The way I approach this is to pull three months of actual usage data from each service. API call counts, monthly active models, that kind of thing. Then I map that against what a consolidated subscription would cost. You’ll usually find that somewhere between 30-50% of what you’re paying for isn’t being used at all.
What matters more is future-proofing though. If you’re consolidating, you want a platform that lets you add new models without renegotiating contracts every quarter. That flexibility is worth something, even if the spreadsheet doesn’t show it clearly.
I’d recommend treating this in phases. First, audit your actual usage over the past six months. Second, identify which models are critical versus experimental. Third, calculate the cost per model invocation across your current setup. Finally, compare that directly to what a bundled subscription would cost based on realistic usage projections.
One detail that matters: deployment overhead in self-hosted setups often scales non-linearly. Managing fifteen separate integrations is more than fifteen times harder than managing one. If you can reduce operational complexity while also reducing licensing costs, that’s usually the right move.
Audit actual usage first. Most orgs pay for models they barely touch. Consolidating usually cuts 30-40% of costs once you remove the waste. Plus simpler ops.
I’ve been in similar situations, and the consolidation question usually comes down to one thing: are you paying for capability you’re not using?
With platforms like Latenode that give you access to 400+ AI models through one subscription, the math changes significantly. Instead of managing separate contracts with OpenAI, Claude, Deepseek, and whoever else, you’re looking at one clear cost and one invoice. We cut our licensing overhead by about 35% just from eliminating the vendor sprawl alone.
The real win though is operational. When your whole team knows they can use any model without worrying about separate API keys or separate billing, workflows get built faster. You’re not stuck debating whether to use gpt-4 or Claude because of licensing concerns—you just pick the best tool for the job.
If you’re wrestling with this decision, I’d honestly spend an hour on https://latenode.com and see how their consolidated approach maps to your current workflow. Could save you weeks of TCO analysis.