Calculator Inputs
Use the grid below. Large screens show three columns. Smaller screens show two. Mobile shows one.
Example Data Table
| Scenario | Current Samples | Target Samples | Avg Size (MB) | Versions | Retraining Runs | Estimated Total Cost |
|---|---|---|---|---|---|---|
| Pilot Expansion | 50,000 | 150,000 | 2.0 | 2 | 4 | $31,480.00 |
| Growth Program | 200,000 | 750,000 | 2.4 | 3 | 6 | $187,960.00 |
| Enterprise Scale | 1,000,000 | 3,500,000 | 3.1 | 4 | 10 | $1,046,250.00 |
These sample rows show planning-style estimates only. Actual costs vary by modality, tooling, labor region, and storage policy.
Formula Used
New Samples = max(Target Samples − Current Samples, 0)
Base Data GB = (New Samples × Average Sample Size MB) ÷ 1024
Stored Data GB = Base Data GB × Versions per Sample
Sourcing Cost = New Samples × Sourcing Cost per Sample
Labeling Cost = New Samples × Labeling Cost per Sample
QA Cost = New Samples × QA Cost per Sample
Preprocessing Cost = Stored Data GB × Preprocessing Cost per GB
Storage Cost = Stored Data GB × Monthly Storage Cost per GB × Retention Months
Compute Cost = Retraining Runs × Compute Cost per Run
Subtotal = Sourcing + Labeling + QA + Preprocessing + Storage + Compute
Overhead Cost = Subtotal × Overhead %
Total Cost = Subtotal + Overhead Cost
How to Use This Calculator
Enter your current dataset size first. Add your planned target size next. Provide average sample size, variable labor costs, preprocessing cost, storage rate, retention period, retraining count, compute per run, and overhead percentage.
After submission, the calculator shows the result above the form. Review total budget, cost per sample, stored data volume, and component breakdowns. Use the Plotly graphs to understand where spending grows fastest.
Download the result as CSV for spreadsheets or PDF for reporting. Adjust versions per sample when you store raw, processed, augmented, or compressed copies of the same data.
FAQs
1) What does this calculator estimate?
It estimates the cost of growing a machine learning dataset. It combines sourcing, labeling, quality review, preprocessing, storage, retraining compute, and overhead into one planning figure.
2) Why does storage use versions per sample?
Teams often keep raw, cleaned, augmented, and compressed variants. Versions per sample helps you reflect that stored footprint instead of underestimating long-term storage spend.
3) Should compute cost scale with samples?
Often yes, but not always linearly. This calculator uses retraining runs and cost per run for simplicity. You can increase runs or run cost to model heavier experiments.
4) Does this work for image, text, audio, and video datasets?
Yes. The calculator is modality-agnostic. Just set realistic sample size, labor rates, storage assumptions, and compute costs for the dataset type you manage.
5) What is included in overhead percentage?
Overhead can include project management, vendor coordination, data tooling, legal review, audit work, security controls, or any extra budget not directly tied to a single sample.
6) Why can total cost stay above zero when no new samples are added?
Because compute or operational settings may still carry cost. If you keep retraining runs and overhead above zero, the total will still reflect those budget items.
7) Can I use this for vendor comparison?
Yes. Keep sample counts constant and change sourcing, labeling, QA, or storage assumptions. That makes the calculator useful for comparing multiple dataset scaling approaches.
8) Is the result an exact budget?
No. It is a planning estimate. Real budgets depend on annotation complexity, failure rates, data rejection, duplicate rates, infrastructure discounts, and retraining behavior.