Private AI: when is running it locally worth it?

The question comes up constantly: should we run models on our own hardware or use a cloud service? The answer rests on four criteria, not on a technical preference.

1. Data sensitivity If contracts, medical files or customer data cannot — legally or contractually — leave the company, the decision is made. Data that cannot be sent anywhere is the strongest argument for local inference.

2. Expected usage volume Intermittent use by a handful of people rarely justifies buying hardware. Heavy sustained use flips the equation, because per-call cloud cost accumulates while hardware cost is fixed.

3. Latency requirements Some cases need an immediate response inside the local network, particularly when the assistant is embedded in an operational process.

4. Ability to operate it Local hardware means responsibility: updates, monitoring, backups and model version management. If nobody will take that on — in-house or under a support contract — the cloud option is more realistic.

What RAG actually means RAG in plain terms: instead of the model answering from memory, we first search your documents for the relevant passages and hand those to the model to compose an answer. The benefit is that the answer is tied to a source you can verify.

Common mistakes - Indexing everything without permissions: the fastest way to leak internal information to someone who should not see it. - Measuring success by impression: assemble a set of real questions with known answers and measure against them. - Expecting perfection: the model makes mistakes. Showing the source with every answer makes those mistakes detectable. - Starting too big: begin with one specific case that one team uses daily.

In short The decision is not "local beats cloud". It is: what data, what volume, what latency, and who operates it? Those four answers decide.