Worked example: busy support bot break-even
The default scenario uses 2,500 input tokens, 800 output tokens, 2,000 requests per day, and 30 active days. That is 150 million input tokens and 48 million output tokens per month.
At $0.75 per 1M input tokens and $4.50 per 1M output tokens, cloud usage is $112.50 plus $216.00, or $328.50 per month.
A local system drawing 450 watts for 8 hours per day at $0.1856 per kWh uses 108 kWh per month, or $20.04 in electricity. With $20 monthly upkeep, local operating cost is $40.04 per month.
Monthly savings are $328.50 - $40.04 = $288.46. A $2,000 local system reaches simple break-even in about 6.9 months, before resale value, engineering time, reliability risk, and model-quality differences.
How we calculate local vs cloud AI cost
Cloud monthly cost equals monthly input tokens divided by 1,000,000 times the input-token price, plus monthly output tokens divided by 1,000,000 times the output-token price.
Local monthly operating cost equals active kilowatt-hours times electricity rate, plus monthly upkeep. Simple break-even is local hardware cost divided by cloud monthly cost minus local monthly operating cost.
One-year local TCO includes the hardware purchase and 12 months of operating cost. Three-year local TCO subtracts the editable resale value and adds 36 months of operating cost.
When does local AI become cheaper than API usage?
Local AI becomes compelling when the same workload repeats every month, the local model is good enough, and the monthly API bill is high enough to recover hardware cost quickly. The cash result should still be checked against model quality, latency, privacy, uptime, and support needs.
What goes into local AI total cost of ownership?
Direct TCO includes hardware, electricity, maintenance, and resale value. A production decision should also include installation time, updates, monitoring, backups, security review, cooling, failed hardware risk, and the cost of keeping a fallback API path.
How electricity changes the break-even point
Electricity can be small for occasional local tests and material for always-on systems. Use measured wall power when you have it. GPU board power alone can understate the cost of the whole workstation or server.
When cloud AI is still the better choice
Cloud API usage is often better for low volume, bursty traffic, fast model upgrades, managed reliability, and workloads that need frontier model quality. Local AI is strongest when privacy, predictable volume, offline use, or direct control matters.
