Calculator inputs
Example data table
Example only. Replace with your own numbers for accurate planning.
| Scenario | Iterations | Experiments / Iteration | Compute Cost | Labor Cost | Data + Labeling | Total Program Cost |
|---|---|---|---|---|---|---|
| Baseline tuning | 4 | 10 | $3,400.00 | $9,920.00 | $1,120.00 | $17,200.00 |
| Feature + data refresh | 6 | 12 | $7,140.00 | $18,900.00 | $2,040.00 | $32,900.00 |
| Heavy benchmarking | 8 | 18 | $22,032.00 | $29,280.00 | $3,840.00 | $62,600.00 |
Formula used
The calculator estimates an all-in cost by combining variable and one-time components, then applying credits, overhead, and contingency.
Labeling = iterations × items × cost/item
Tip: Use contingency when experiments have high rerun rates or unstable data pipelines.
How to use this calculator
- Set iterations to match your improvement roadmap.
- Estimate experiments per iteration, including hyperparameter sweeps.
- Enter compute hours, unit count, and unit-hour rate.
- Add data costs for acquisition, labeling, and storage footprint.
- Populate labor rates and hours for each iteration.
- Include extras like evaluation runs, tools, and one-time setup.
- Apply adjustments for discounts, overhead, and contingency buffers.
- Press Calculate cost to view results above the form.
- Use Download CSV or Download PDF for sharing.
Compute unit economics across iterations
The compute block converts your roadmap into a repeatable unit cost: experiments × hours × rate × units. If one experiment averages 3.5 hours on 2 GPUs at $4.25 per GPU‑hour, the run costs about $29.75. Multiply by experiments per iteration and by iterations to estimate the training budget before adjustments. This framing helps you negotiate reserved capacity, spot expensive sweeps, and quantify the impact of trimming run time.
Labor planning with role-based effort
Iteration work often dominates compute. The labor section totals the per‑iteration hours for ML engineering, data science, and MLOps, then scales by iterations. For example, 35 + 18 + 10 hours equals 63 hours per iteration. At rates of $60, $55, and $50, that iteration totals $3,590. Use this to validate staffing, compare in‑house vs contractor rates, and decide when automation (pipelines, templates, evaluation harnesses) pays back.
Data, labeling, and storage as growth drivers
Data acquisition and labeling typically rise as you chase edge cases. Enter GB per iteration and cost per GB for collection and processing, plus labels and cost per label for review and QA. Storage uses an average footprint over the project duration, capturing checkpoints, logs, and datasets. When you test a “data refresh” scenario, increase GB and labels first; you will see whether model quality improvements are worth the operational load.
Overhead, credits, and contingency for governance
Budgets rarely equal raw expenses. Apply discounts for credits or negotiated pricing, then add overhead for security, privacy reviews, and platform support. Contingency covers retraining due to data drift, failed runs, or new compliance requirements. A practical approach is 5–15% overhead and 5–10% contingency for stable programs, and higher buffers for new architectures or fast‑changing data sources.
Scenario sensitivity and decision-ready outputs
Use the averages and burn rate to communicate choices. Average cost per iteration supports stage‑gate planning, while average per experiment highlights the value of early stopping and better baselines. Weekly burn translates totals into finance language aligned with sprint cadence. Export CSV for spreadsheets and PDF for reviews, then rerun with alternative assumptions (fewer experiments, lower hours, more labeling) to identify the biggest levers for faster stakeholder alignment.
FAQs
What does the grand total include?
It combines compute, data ingest, labeling, storage, labor, tools, evaluation, and miscellaneous costs, then adds one‑time items. After that, discounts are applied and overhead plus contingency are added.
How should I estimate the unit-hour compute rate?
Use your cloud price per GPU/instance hour or an internal chargeback rate. If you run on mixed hardware, calculate a weighted average from recent bills or job logs.
How do I model reserved capacity or credits?
Enter reserved savings or credits as a percentage in “Discount / credits”. If only compute is discounted, reduce the rate per unit‑hour instead for a more precise result.
Why does storage use average GB rather than per-iteration GB?
Artifacts grow and shrink over time. Average footprint captures checkpoints, logs, and datasets across the project duration without forcing you to forecast every spike.
What overhead and contingency percentages are reasonable?
For stable pipelines, 5–15% overhead and 5–10% contingency are common. Increase buffers when data quality is uncertain, compliance work is heavy, or reruns are frequent.
How can I compare multiple scenarios quickly?
Run the calculator for each scenario, download CSV files, and place them into one spreadsheet tab. Compare grand total, cost per experiment, and weekly burn to find the strongest levers.