Calculator Inputs
Enter realistic values for your pipeline. Defaults represent a small engineering validation cycle.
Formula Used
This estimator treats each fold run as a repeatable “unit job” and aggregates cost drivers.
- Runs = k × repeats × configurations
- Compute hours = runs × (prep + train + eval)
- Wall-clock hours = compute hours ÷ (parallel × efficiency)
- Compute cost = compute hours × cost/hour
- Labor cost = (runs × eng/run + reporting) × rate
- Storage cost = GB × cost/GB-month × months
- Overhead = base × overhead%
- Contingency = (base + overhead) × contingency%
- Total = base + overhead + contingency
How to Use This Calculator
- Set k, repeats, and configurations to match your plan.
- Enter realistic prep, training, and evaluation hours per run.
- Use a blended compute cost/hour including platform overhead.
- Fill in parallel workers and reduce efficiency if bottlenecks exist.
- Add labor time for monitoring and fixed reporting tasks.
- Include storage if artifacts must be retained for reviews.
- Apply overhead and contingency for realistic budgeting.
- Click Calculate, then export CSV or PDF for sharing.
Example Data Table
Sample scenarios to demonstrate how fold count and parallelism affect cost and timeline.
| Scenario | k | Repeats | Configs | Compute hr/run | Workers | Efficiency | Est. runs | Est. wall-clock (h) |
|---|---|---|---|---|---|---|---|---|
| Quick validation | 5 | 1 | 1 | 2.75 | 1 | 85% | 5 | 16.18 |
| Hyperparameter sweep | 5 | 1 | 12 | 2.75 | 6 | 80% | 60 | 34.38 |
| High-confidence study | 10 | 3 | 4 | 2.75 | 10 | 75% | 120 | 44.00 |
Workload scales with folds and repeats
Runs grow as k × repeats × configurations. Moving from 5-fold to 10-fold doubles runs, so compute-hours and labor-hours typically double too. Repeating a 5-fold cycle three times creates 15 runs per configuration. If you compare 12 configurations, that becomes 180 total runs. Use these multipliers first; they reveal whether you need fewer folds, fewer repeats, or smarter search strategies to control validation scope. A single extra configuration multiplies runs by k repeats.
Compute economics follow per-run time
Per-run compute-hours equal prep + training + evaluation. For example, 0.25 + 2.00 + 0.50 = 2.75 hours. At $1.20 per hour, each run costs $3.30 in compute. With 60 runs, compute cost becomes $198. These simple unit costs help you benchmark alternatives, like early stopping or smaller feature sets, because shaving 0.25 hours per run saves 60 × 0.25 = 15 hours. At scale, rounding errors hide meaningful budget drift quickly.
Parallelism changes calendar time, not spend
Parallel workers reduce wall-clock time but do not reduce total compute cost. This calculator uses effective workers = workers × efficiency. With 6 workers at 80% efficiency, effective capacity is 4.8. If total compute-hours are 165, the estimated wall-clock time is 165 ÷ 4.8 = 34.38 hours. Improving efficiency from 70% to 85% cuts wall-clock by about 18% for the same workload. Queue delays are excluded; add margin for shared clusters.
Labor and reporting often dominate small jobs
Engineering effort is modeled as hours per run plus fixed reporting. If you spend 0.20 hours per run and run 60 jobs, monitoring totals 12 hours. Add 1 reporting hour for summaries, making 13 hours. At $35 per hour, labor cost is $455, which can exceed compute for lightweight models. Track labor separately so you can justify automation, better dashboards, or standardized evaluation scripts. Standardized templates can reduce per-run time to 0.10 hours.
Buffers make budgets resilient
Base cost combines compute, labor, and storage, then applies overhead and contingency. If base cost is $700 and overhead is 10%, subtotal becomes $770. A 5% contingency adds $38.50 for retries, failed runs, or extra analysis, bringing total to $808.50. For regulated or safety-critical work, you may raise contingency to 15–25%. Use unit cost per run to compare plans consistently across teams. Storage is minor, yet audits may require months longer.
FAQs
1) What does “cost per fold-cycle” represent?
It is the total estimated budget for completing one full k-fold pass for a single configuration, before repeats multiply it. Use it to compare different k values while keeping configurations constant.
2) How should I set the efficiency percentage?
Start from 70–90%. Reduce it if you expect data loading bottlenecks, shared-cluster queueing, or frequent restarts. Increase it only when runs are stable, cached, and automation is mature.
3) Does adding more workers reduce the total cost?
No. More workers mainly reduce calendar time. Total compute-hours stay similar, so compute spend is roughly unchanged. Extra workers can still raise cost indirectly if they require higher-priced instances.
4) How can I estimate engineer hours per run?
Track a pilot batch. Include time for monitoring, reviewing logs, fixing minor failures, and recording results. Divide total hands-on time by the number of runs, then add separate reporting hours.
5) When should I increase contingency?
Increase it when models are unstable, data quality is uncertain, or new infrastructure is being used. Higher contingency also fits compliance reviews where reruns, documentation, and extended retention are likely.
6) Can I account for discounts or reserved pricing?
Yes. Enter a blended compute cost per hour that already reflects discounts, credits, or spot pricing. If pricing varies by stage, use weighted averages or run separate scenarios and compare exports.