When Should an Enterprise Run AI Locally Instead of Using a Cloud LLM?
Security Boulevard, Wednesday, July 15th, 2026
Five signals for deciding whether AI workloads belong on local infrastructure rather than a cloud LLM.
The article lays out when enterprises should host AI locally instead of relying solely on cloud LLM providers.
Five decision signals drive placement: data classification requirements in regulated industries such as healthcare and finance, token volume costs where breakeven typically falls between $15,000 and $40,000 in monthly cloud spend, regulatory compliance obligations, latency and availability constraints, and the need to control proprietary models.
The author argues most enterprises are best served by hybrid deployments that route different workloads appropriately rather than an all-or-nothing choice. A three-year total cost of ownership analysis covering infrastructure, platform, personnel, and operations should guide the decision.