I’m deep in an enterprise automation evaluation right now, and I keep hitting the same wall. We’ve got Make and Zapier on the table, but every time I try to model total cost of ownership, I’m adding up separate subscriptions for ChatGPT, Claude, maybe a few other models depending on the workflow. It’s getting ridiculous.
Right now we’re paying for:
- Zapier’s base tier
- ChatGPT API access
- Claude API access
- A couple of niche models for specific tasks
Each one has its own pricing tier, its own seat limits, its own billing cycle. The finance team is already asking questions, and I don’t have clean answers.
I’ve been hearing about platforms that bundle AI model access into a single subscription, which sounds great in theory, but I’m skeptical about whether it actually simplifies the math or just moves the complexity around.
How are other people handling this? Are you tracking licensing separately per model, or have you found a way to consolidate? And more importantly—when you do consolidate, does the financial picture actually improve enough to justify a platform migration?
I went through the exact same thing about six months ago. We had Make as our base, then kept bolting on services for GPT, Claude, and a couple others.
The real problem wasn’t the features—it was visibility. Finance couldn’t track what was being used where, so every month was a surprise. One team would spin up a Claude workflow, another would add GPT somewhere else, and you’d just see the costs pile up.
We ended up consolidating to a single platform subscription that includes all the major models. Sounds simple, but the actual benefit was more about governance than savings. Suddenly we could see which workflows actually use which models, which ones are wasting tokens, and where people are over-provisioning.
Savings-wise, we probably cut about 30-40% off the total AI licensing spend because we stopped paying for redundant subscriptions and unused tiers. But the bigger win? We could finally explain to finance exactly what we’re spending and why.
Migration took maybe three weeks to rebuild our critical workflows. Not painful, but not trivial either.
You’re asking the right question, but you might be approaching it wrong. Before you worry about consolidation, you need to baseline what you’re actually using. Spend a week tracking every AI model activation in your current workflows. You’ll probably find that 80% of your work runs on maybe two or three models, and the rest is scattered across rarely-used services.
Once you have that data, the TCO calculation becomes clear. You can figure out what a single-subscription platform would cost versus your current sprawl. Most of the time, the math works out because you’re eliminating overhead—fewer contracts, fewer billing relationships, fewer teams managing their own integrations.
The real question isn’t whether consolidation saves money. It’s whether your team can actually maintain and optimize a centralized licensing model. Some organizations are too decentralized for that.
Total cost of ownership with fragmented AI licensing is genuinely difficult to model because you’re comparing different variable cost structures. Zapier charges per task, Make charges per operation, and your AI subscriptions charge per token or per API call. These don’t scale together.
When you consolidate to a single platform with bundled AI access, you shift from multiple variable costs to one predictable fixed cost plus variable usage. That’s easier to forecast, but it requires discipline. You need to actually monitor what your workflows are consuming.
I’d recommend building a simple spreadsheet that tracks your current spend across 12 months, then creating a scenario model for a consolidated platform using your actual usage patterns. That gives you a real apples-to-apples comparison rather than guessing.
Track your current usage for 30 days. Most companies find their actual AI model spend is way higher than they thought, and consolidation cuts it by 25-40%. Talk to your vendor about volume pricing though—the headline single-sub price isnt always the actual price you’ll pay.
This is exactly what we solved by moving our workflows to a single platform subscription for all our AI models. Instead of tracking five different services and five different billing cycles, we get everything in one place. We could see exactly what we were using, which models were actually driving value, and which subscriptions we’d been paying for but never really used.
The financial difference was significant—turned out we had Claude, GPT, and a couple others all running in parallel for redundancy, even though we only needed one or two. Once we had visibility, we could actually optimize.
The bigger win was time. Our team wasn’t managing integrations and API keys across a dozen different services anymore. We built workflows faster, debugged faster, and spend less time on infrastructure.
If you want to see how a real consolidation works, check out https://latenode.com