10,000-sensor factory pays $300K/year in cloud API fees but edge inference costs $0/query

A large automotive factory with 10,000 vibration, temperature, and acoustic sensors sampling at 1kHz generates 864 million inference requests per day for quality anomaly detection -- at $0.0001 per cloud API call (the cheapest tier), that is $86,400/day or $31.5M/year, which is economically absurd for detecting whether a weld sounds right. Even at aggressive volume discounts, the per-query cost model of cloud APIs breaks down completely when sensor counts reach industrial scale, because the pricing is designed for human-initiated queries (hundreds per user per day), not machine-initiated queries (millions per sensor array per day). The structural issue is that cloud API pricing is per-query while sensor data is continuous and high-frequency -- the economic models are fundamentally incompatible. Running Gemma on a $200 edge device at each sensor cluster eliminates per-query costs entirely, reducing the total cost of inference from millions per year to a one-time hardware investment of $50,000 for the entire factory.