Local AI vs Cloud APIs: What's Right for a Small Product?

If you're building a small AI product, one decision shapes everything: do you run the model yourself, or call a hosted API?

The case for local

Running an open model on your own server keeps user data on-premise, has no per-call cost, and gives you full control. For privacy-sensitive content — confidential spreadsheets, internal documents — this matters.

The cost of local on CPU

Without a GPU, inference is slow and effectively single-threaded. A 9B model can take a minute or more per generation, and two users at once means the second one waits. That's fine for occasional, bursty use; it breaks down under real concurrency.

A pragmatic middle path

• Use a small, fast model for interactive features (chat, short answers) so they stay responsive.
• Queue heavy jobs (document generation) so requests don't time out waiting on the model.
• Route only non-sensitive work to a cloud API when you need speed — for example, narration over aggregated numbers rather than raw data.

There's no universally right answer. Start with what your privacy requirements demand, then optimize for speed only where users actually feel it.