Compare local and cloud AI by total cost per accepted task, not by an isolated token price or GPU specification. Include hardware allocation, electricity, service fees, retries and human review. The arithmetic example below uses invented inputs to show the method; it is not a market quote or benchmark.
Why the cheapest token is not always the cheapest result
A low-priced request can become expensive if it needs repeated corrections. A purchased computer can be poor value if it spends most of the month idle. Our accounting unit is an accepted task: a result that meets criteria written before the run. Define that unit first, such as a tested code change or a correctly labeled product image. Do not compare a rough draft from one system with an approved deliverable from another.
Build a comparable monthly cost sheet
Use one currency and one billing period. Separate money already spent from new purchases you would make for this decision. Do not call a local workload free because the computer is already on the desk; record incremental electricity and the time it displaces. Equally, do not charge a whole workstation to a single small experiment if it has other useful work.
| Cost item | Local workflow | Hosted workflow |
|---|---|---|
| Fixed allocation | Chosen share of hardware and maintenance | Subscriptions or reserved capacity |
| Variable usage | Electricity and consumables | Tokens, jobs, tools and storage |
| Failures | Re-runs and debugging | Retries and failed billable requests |
| Human time | Setup, review and corrections | Integration, review and corrections |
Worked example: make the assumptions visible
These numbers are invented teaching inputs, not measured costs. Suppose a local workflow has $80 in monthly allocated costs and produces 400 accepted tasks. That is $0.20 per accepted task. Suppose a hosted workflow spends $36 across 600 attempts but only 300 results are accepted. Its usage cost is $0.12 per accepted task, not $0.06. If it also requires $30 of review time, the total becomes $66 divided by 300, or $0.22. Changing the acceptance count can reverse the apparent winner.
| Illustrative scenario | Monthly included cost | Accepted tasks | Cost per accepted task |
|---|---|---|---|
| Local allocation only | $80 | 400 | $0.20 |
| Hosted usage only | $36 | 300 | $0.12 |
| Hosted usage plus example review cost | $66 | 300 | $0.22 |
Insert dated provider prices rather than guessed rates
Google's Argon API pricing currently distinguishes input, cached input and output including thinking, with introductory rates ending November 30. NVIDIA's RTX Spark announcement gives hardware capabilities, not a universal retail cost. These sources illustrate why a budget needs dated inputs. Obtain a regional hardware quote and the provider's applicable billing rates, then replace every example number in this worksheet. Check taxes and currency conversion separately.
Measure a pilot before calculating a break-even point
Use a representative mix of easy and difficult tasks. Record attempts, accepted results, total elapsed time and review effort for each route. If one route cannot complete a required task, do not hide that failure by averaging only successful jobs. A simple break-even formula uses extra fixed cost divided by per-task savings; it is meaningful only when outputs are comparable and the savings remain positive. Read the hardware planner and our RTX Spark buying checklist before treating memory capacity as a business return.
- Use an explicit acceptance rubric and keep failed attempts.
- Track the same scope on both routes.
- Label quoted, measured and assumed inputs separately.
- Recalculate when pricing, utilization or quality changes.
Frequently asked questions
Are the worksheet numbers real service prices?
No. They are invented teaching inputs. Replace them with your own dated quotes, invoices and pilot measurements.
What is cost per accepted task?
The included cost divided by the number of results meeting your predefined acceptance criteria. State which costs you include.
Does local AI always cost less than an API?
No. Utilization, hardware, energy, output quality, retries and review effort can change the result.
Sources & further reading
Primary sources checked Oct 14, 2026. Vendor statements are attributed; editorial advice is our own.
- 1Gemini Developer API pricing ↗Google AI for Developers
- 2RTX Spark Windows PC announcement ↗NVIDIA · Oct 7, 2026
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