On-Device LLM Inference: When to Move AI Off the Cloud
Most teams discover that running AI inference in the cloud has sharp edges only after they've already hit them: a HIPAA audit that traces back to PHI crossing API boundaries, latency numbers in staging that look fine until a user on a spotty connection reports "it just spins," or a per-inference API bill that looked reasonable at 10,000 requests per day and catastrophic at 10 million. On-device inference is often the right answer — but the reasons teams reach for it, and the problems they hit when they do, are rarely the same ones that show up in blog post comparisons.
This is a practical guide to the decision: when local execution beats cloud APIs, which small models actually deliver, and what the deployment lifecycle looks like once the benchmark demo is over.
