You’re Already Paying OpenAI. But Are You Paying Them 12x?
- Dave Findlay
- Sep 5
- 2 min read
LLMs are expensive. You probably know that.
But you might be paying for them a lot more than you realize.
Not just once. Not just through OpenAI or Anthropic.
You’re paying them five times or more, through half the SaaS tools in your stack.
And the bill? It’s growing.
How We Got Here
It’s never been easier to spin up a GenAI feature.
Vendors wrap OpenAI, Anthropic, or Gemini in a slick UI… and call it a productivity tool.
Meanwhile, every SaaS platform is shipping AI features at speed. Features that you didn't ask for and likely don't use or need:
Slack messages that summarize themselves
CRMs that write outreach emails
Docs that auto-generate bullet points
And almost all of them are just passing your data to a foundation model API under the hood.
You don’t control the model. You don’t know how much of your spend is tied to it. And
you’re probably paying for the same model multiple times.
Example Stack: Paying Twice (or More)
Let’s make it concrete.
You’re a mid-sized company with a modern SaaS stack:
Notion AI for meeting summaries
Slack AI for chat and search
Glean AI for knowledge base answers
HubSpot AI assistant for marketing
Grammarly GO for writing help
All five tools claim AI benefits. But guess what?
They all rely on OpenAI or Anthropic under the hood.
So now you're paying 5x markup on inference costs, with no visibility and no control.
Where the Money’s Going
VCs have taken notice:
The money in AI is not in startups — it's flowing to NVIDIA, OpenAI, and the big cloud providers.
Anthropic just raised funding at a $18.4B valuation.
These are not public utilities. They are some of the most expensive suppliers in your digital supply chain.
This Isn’t Sustainable
Three compounding problems are brewing:
Redundant Spend: You're paying for the same inference multiple times, via SaaS middlemen.
Black Box Pricing: Most vendors don't expose LLM usage, model choice, or cost drivers.
Vendor Lock-in: You're stuck with the models your tools chose — not the ones that suit your needs.
Meanwhile, the costs of compute and energy are rising. At scale, this becomes a real business risk.
What Can You Do?
As a buyer:
Ask vendors which foundation models they use
Push for LLM usage reporting and controls
Audit your stack for redundant model access
As a builder:
Explore Small Language Models (SLMs) for edge or embedded tasks
Partner directly with model providers to cut out the middle layer
Design your own memory and retrieval layer to optimize context use
Final Thought
The model you choose matters. But how many times you're paying for it might matter even more.
Better architecture, smarter design, and conscious procurement can reduce your exposure.
And remember: the model isn’t your only cost.
Design decisions become budget decisions.
It’s time to treat them that way.
At Fuse, we believe a great data strategy only matters if it leads to action.
If you’re ready to move from planning to execution — and build solutions your team will actually use — let’s talk.
