The useful takeaway is that “local is cheaper” only makes sense after you define the workload. Hardware turns inference into a capacity-planning problem, while cloud turns it into a usage-based operating cost, so comparing only the per-token price misses the real trade-off.

For production AI work at IT Path Solutions, I’d look at this as a routing decision rather than a local-vs-cloud decision: keep predictable, high-volume or privacy-sensitive workloads close to the data, while sending frontier reasoning or bursty workloads to the cloud. That also makes utilization a much better metric than simply asking which option has the lower headline cost.

The hidden costs around maintenance and model drift are important too. If the local system sits underutilized, its “cheap” inference capacity is effectively stranded capital. The real break-even point is therefore based on sustained useful utilization, not just token volume.