We’re in the middle of evaluating automation platforms for our enterprise, and the licensing situation is starting to feel like a shell game. Right now we’re paying separately for OpenAI, Anthropic, Deepseek, and a few others—each with their own contract, their own billing cycles, their own support channels. It’s a nightmare to track, and our CFO is asking legitimate questions about why we can’t just have one bill.
I’ve been looking at platforms that claim to consolidate this—essentially bundling 400+ AI models under a single subscription. On paper, it sounds like a no-brainer: unified pricing, one contract, everything in one place. But I keep hitting the same mental block. Are we actually saving money, or are we just swapping fragmentation for vendor lock-in?
With Zapier and Make, at least I know what I’m paying for and I can walk away if something better comes along. But when a platform is your gateway to every major AI model, that’s a different kind of risk. What happens when their pricing changes? What happens if they deprecate a model integration?
Has anyone actually modeled the TCO difference between keeping subscriptions separate and consolidating them? I’m not just looking at the monthly cost—I’m looking at switching costs, contract terms, and long-term flexibility. What am I missing in this calculation?
I went through this exact calculation last year. The spreadsheet looked good on day one, but what actually saved us money was operations overhead.
When we were managing five separate AI subscriptions, we had one person spending about 6 hours a week just tracking usage, managing API keys, and dealing with billing issues. That’s roughly 300 hours a year. Once we consolidated, that dropped to maybe 45 minutes a week for monitoring.
The lock-in concern is legit, but here’s the thing—you’re already somewhat locked in with OpenAI and Claude. They’re not going anywhere. The real question is whether losing the flexibility to mix and match different providers is worth the operational savings. For us, it was.
Vendor lock-in is real, but I’d frame it differently. You’re not locked into a vendor—you’re locked into a set of models that happen to be bundled together. If a better model comes out, the platform has incentive to add it because they make money on volume, not scarcity.
What actually matters is whether the models they bundle cover your use cases. If you only use GPT-4 and Claude, and those are both included, then the switching cost isn’t that high. You can always pull your workflows and run them elsewhere if needed.
One thing nobody talks about: when you consolidate licensing, you also get better visibility into actual usage patterns. Our CFO could never tell which teams were burning through OpenAI credits. Now we can see exactly where the spend is happening and make smarter decisions about which models to use where. That visibility alone has paid for the consolidation.