Buy a GPU or rent the tokens? The hardware question behind every AI project
Every AI conversation we have eventually hits the same question: "should we buy a GPU, or should we rent?" The honest answer is: it depends on your math, and the math is less scary than it looks.
Two very different cost shapes
Renting inference from a model provider is an operating cost: you pay per token, it scales with your traffic, and it starts at whatever the first request costs. There's no hardware to buy, patch, cool or replace, and no depreciation on your books.
Buying or leasing accelerators is a capital cost: a large up-front number, fixed, that you amortize whether or not the workload shows up. It also commits you to running the model yourself: updates, security, availability, all of it.
Neither is the default. The right one is the one where the arithmetic works.
When renting is the right answer
For most small and mid-size companies, renting inference is the right starting point:
- The workload is spiky, or just starting. You shouldn't pay for peak capacity you'll use twice a month.
- You want the model to improve under you. Providers ship better models; a rented one upgrades, a self-hosted one is what you deployed.
- You don't have, or don't want, a team that runs inference infrastructure.
- The data doesn't legally or practically need to stay on your own hardware.
When self-hosting starts to make sense
We see it in a few situations:
- Volume and predictability. A steady, measurable inference load where the per-token bill keeps growing and the arithmetic on owned hardware finally crosses over.
- Data constraints. Some workloads simply can't leave your premises. Open-weight models on your own infrastructure is the standard answer there.
- Fine-tuning and latency control. When you're training or tuning on your own data, or when every millisecond of latency has a price.
The question to ask first
Before any purchase, write down one number: how much inference the workflow actually needs, measured, for a month. Then do the two arithmetic problems side by side. In our experience, the first version of the project almost always lands on the rental side, and that's fine. The door to owning hardware opens when the workload proves itself, not before.