Estimate the all-in cost of one AI transcription task, including usage-based transcription charges, optional human review, storage, and fixed workflow overhead. The breakdown highlights the dominant expense and calculates cost per audio minute, which is useful for internal budgets, customer pricing, and comparisons between automated and reviewed workflows.
Task cost = audio minutes × API rate + review minutes ÷ 60 × hourly cost + stored MB ÷ 1,024 × storage rate + fixed overhead.
The main result is a planning estimate for one transcription workload or its available capacity. It is only as accurate as the pricing, model-speed, token-density, and workload assumptions entered. Use measured production values whenever possible and run high- and low-case scenarios before committing capacity or a customer price.
This planning estimate does not guarantee vendor billing, model performance, transcription accuracy, translation quality, or service-level objectives.
For 60 audio minutes at $0.006/minute, 10 review minutes at $30/hour, 100 MB stored at $0.023/GB-month, and $0.10 overhead, total cost is about $5.46.
Use representative production values or a weighted average from recent tasks. Peak values are better when testing capacity risk.
Not automatically. Increase workload, overhead, tokens, or utilization headroom to model retries and operational variance.
Yes. Enter each option’s measured speed, infrastructure assumptions, and labor or usage prices as separate scenarios.
Headroom helps absorb variable media length, traffic bursts, startup time, retries, and queueing without immediately missing targets.
Update it after pricing, models, hardware, workflow stages, average task size, or concurrency patterns change.
| Variable | Meaning |
|---|---|
| Workload | Media length, source volume, or concurrent demand entered above. |
| Efficiency | Measured model rate, token density, batching gain, or utilization assumption. |
| Overhead | Workflow work outside the primary model calculation. |
| Result | Estimated requirement or capacity before unmodeled operational variance. |