Dataset Labeling Cost Calculator
Estimate what annotating a training dataset costs from item count, labels per item, price per label and a QA/review overhead — plus the cost per finished item.
How it works
Base = items × labels per item × price per label · Total = base × (1 + review overhead%)
Labeling cost is driven by the number of annotations, not just items — a single image with ten bounding boxes costs ten labels’ worth of work. On top of raw annotation you pay for quality: a second reviewer, adjudication of disagreements and spot-checks, typically 10–30% more, and skimping here is the fastest way to poison a model with noisy labels. Complex tasks (segmentation, expert medical or legal review) push the price per label up by orders of magnitude versus simple classification, so match the price field to your task, not a generic crowd rate.
Worked example
10,000 items each needing 1 label at $0.08 per label is 10,000 annotations for $800 base. A 15% QA overhead adds $120, for $920 total — about $0.092 per finished item.
Frequently asked questions
Why price per label instead of per item?
Because effort scales with annotations: object detection may need many boxes per image, and NLP tasks may need multiple spans per document. Counting labels captures that, then labels-per-item converts back to a per-item cost.
How much should I budget for QA?
For straightforward tasks, 10–20% covers a review pass and spot-checks. For high-stakes or subjective labels, double-labeling with adjudication can add 50–100%. Quality overhead is cheaper than retraining on bad data.
What about in-house vs. crowd labeling?
Crowd platforms charge per label; in-house or expert labeling is usually priced by hour. If you know hourly rate and items-per-hour, divide to get an effective price per label and enter that here.
Related calculators
Educational estimate — real costs and memory depend on your provider, framework, model architecture and pricing, which change often. Verify against your own bills and profiling before committing budget.