Local LLM cost debate resurfaces

Local inference keeps sounding like the frugal choice, but the practical answer depends heavily on workload shape.

• –APIs usually win for occasional use, frontier-model quality, low setup time, and predictable developer ergonomics.
• –Local models win when requests are frequent, data sensitivity is high, latency needs to stay on-device, or teams can reuse existing GPU hardware.
• –The hidden cost of local is operational: model selection, quantization, VRAM limits, updates, evals, serving, and lower average output quality.
• –The pragmatic setup for many developers is hybrid: local for cheap/private routine tasks, APIs for hard reasoning, coding, and production-grade reliability.