Why AI Got Cheap and What It Means for Small Biz
I explain why AI pricing collapsed, how that reshapes the software choices for owner‑operators, and what concrete steps you can take to own your stack.
When I first started swapping out rented tools for a single owned platform, the price of AI seemed like a distant concern. Over the last few years the cost curve has inverted, and the impact on owner‑operators is immediate. I’ve watched the same budget line shrink from a costly experiment to a line‑item that can be amortized across the whole business.
The hardware and cloud cost curve flattened
GPU manufacturers moved from a niche market to a volume business, and the economies of scale are now visible in every price sheet. Cloud providers responded by offering per‑second billing and spot instances that bring the marginal cost of a thousand inference calls down to pennies.
At the same time, the software stack that powers large language models has been open‑sourced, trimmed, and containerised. You can spin up a model on a modest VM and pay only for the compute you actually use. The result is a pricing model that resembles a utility bill rather than a capital expense.
SaaS became a commodity, not a moat
When AI services were first released, the differentiation was the model itself. Now the same core capability is offered by dozens of vendors, each pricing it to undercut the other. The competitive pressure has forced the per‑token rates down to the point where the marginal cost is negligible for most business workflows.
That commoditisation means the SaaS you rent is no longer a strategic advantage; it is a template you still have to bend. The UI may look polished, but the underlying logic still reflects the vendor’s generic use case, not the nuances of your operation.
Owning the stack beats renting a template
In the rebuild I led for a ~200‑employee owner‑operated firm, we replaced 21 SaaS tools with a single, internally owned system. The new platform runs on the same cheap AI compute we now treat as a utility, and the business sees $0 ongoing cost for the software layer itself.
Because the code lives in our own repository, we can stitch AI agents directly into the workflow where they add value, instead of forcing the workflow to adapt to a pre‑built integration. The result is a tighter feedback loop, faster iteration, and no surprise price hikes from a third‑party vendor.
A practical path for owner‑operators
Start with a map of the exact steps your team takes to deliver a product or service. Identify every SaaS touchpoint that merely approximates those steps. Then ask: can the same logic be expressed in a small module that runs on our own servers?
If the answer is yes, prototype the module using the cheap AI APIs that now cost pennies per thousand calls. Test it against the SaaS version for speed and accuracy. When the prototype holds up, retire the SaaS subscription and integrate the module into your core system.
- Catalog every recurring SaaS expense.
- Group related functions into logical domains (e.g., sales, inventory, HR).
- Build a minimal AI‑enabled microservice for each domain.
- Replace the SaaS with your microservice and monitor performance.
- Iterate, adding automation where the data shows the biggest friction.
The effort pays off quickly because you eliminate the hidden cost of vendor lock‑in and you gain the freedom to tweak the logic whenever the market shifts. In my experience, the first domain you own yields enough savings to fund the next one, creating a virtuous cycle of ownership.
The sharp takeaway
If AI pricing is now cheap enough to treat as a utility, the only reason to keep paying for a SaaS subscription is if the software does something you cannot replicate yourself. Map, prototype, and replace – that is the fastest way to turn cheap AI into real competitive advantage for a small business.