Is running AI locally cheaper than paying for an API?

It is when your usage is steady and heavy and you would otherwise pay frontier-model prices. Three 256 GB Mac Studios, enough for a frontier-size open model such as DeepSeek V3.1, cost $28,497, or about $960 a month over three years including power. A workload that costs thousands a month in frontier API fees pays that back in months. Against cheap hosted open models, owning costs about the same, and the case for local rests on privacy and control.

A worked example, using list prices on October 26, 2026: a team whose coding agents use 1.5 billion input and 300 million output tokens a month would pay about $6,000 a month at Claude Sonnet 5.5 rates ($2 / $10 per million tokens) and about $12,000 at Claude Opus 5.5 rates ($4 / $20), before caching discounts.

Three M5 Ultra Mac Studios with 256 GB each cost $28,497 and draw at most about 385 W each, roughly $169 a month at $0.20 per kWh running flat out. If they can carry the workload, they pay for themselves in about 5 months against Sonnet pricing and under 3 months against Opus pricing.

The same workload on DeepSeek’s hosted API ($0.30 / $1.20 per million tokens at peak) costs about $810 a month, slightly less than owning. Against hosted open models, the reasons to go local are privacy, control and fixed costs, not price.

The condition that matters is throughput. Make sure the hardware can serve your peak load at an acceptable speed by testing your own prompts, and keep the hardest tasks on a frontier model if open models fall short.

Updated Oct 26, 2026