Operate local AI responsibly
Understand the real cost
Compare local hardware, electricity, maintenance, utilization, and model quality with the complete cost of an API.
Local inference has no per-token invoice, but it is not free.
Count the hardware over its useful life, electricity while computing, idle power when the machine must stay available, storage, maintenance time, and replacement cost.
Then compare an equivalent service. A small local model should not be justified by comparing it only with the price of a much stronger frontier API.
Local often makes sense when:
- you already own suitable hardware
- data must remain inside a controlled boundary
- usage is frequent and predictable
- a modest model passes the task evaluation
- offline operation matters
An API often makes sense when usage is low or spiky, the task needs frontier quality, you do not want to operate hardware, or scaling beyond one machine matters.
A hybrid design is normal. Route private routine work locally and difficult public-data work to a cloud model when policy allows it.
Lesson completed