Local LLM stack eyes chargebacks

Good idea on paper, but the economics get ugly fast once you price real GPUs, cooling, uptime, and maintenance; for 1-2 users, this looks more like an internal appliance than a scalable token business.

• –Chargeback only works if the meter is clean, the rate card is defensible, and usage is steady enough to recover capex.
• –Hosted routing layers still look compelling for this use case: pay-as-you-go token billing has no minimum spend, and fallback/reliability are built in.
• –The high-end local-model path is not cheap; community guidance around Kimi-class models shows serious VRAM and interconnect requirements, even before you account for expansion.
• –Electricity is the visible cost, but the hidden costs are the real trap: downtime, cooling, and upgrade churn when model requirements jump.
• –For privacy, control, and predictable workflows, local makes sense; for pure ROI, hybrid or hosted inference still looks safer unless utilization is very high.