Running your own AI model instead of paying per request

Paying per request to an AI provider is the right default for most work: low volume, varied tasks, and no need to manage anything yourself. But for a narrow, high-volume, well-defined task (sorting items into categories, pulling specific fields out of documents, a fixed-format summary) the economics flip once volume passes a certain point, and a smaller AI model running on infrastructure you control gets both cheaper and more predictable.

The predictability matters as much as the cost. A model you run yourself doesn't change behaviour when a provider updates theirs. Response time depends on your own infrastructure, not a shared service's traffic. And there's no per-request bill that spikes awkwardly along with usage.

This isn't an argument against provider APIs generally. For anything needing broad reasoning, or infrequent and varied requests, an API is still the right call. Building your own infrastructure for a low-volume task is wasted effort in the other direction. It's a straightforward calculation based on volume and scope, not an ideological one, and it's worth actually running the numbers before defaulting to whichever approach feels more familiar.

This is squarely production-infrastructure territory: the work we call MLOps. Running, updating, and monitoring a self-hosted model is real ongoing engineering work, not a one-time setting to flip.