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AI Agent Data Labeling Cost Calculator

Estimate cost, timeline, and review overhead for training data: image classification, NER, RLHF rankings, and synthetic labels with human validation.

Scenario presets

Labeling project inputs

Items to label Labels per item Task complexity

NER, multi-label docs, bounding boxes, search relevance.

Workforce / vendor

Higher per-label cost, better quality controls and SLAs.

Cost per label ($) Labels per hour (implied throughput) Review rate (%) Platform / tooling monthly ($) Overlap / votes per item Setup hours Setup hourly rate ($) Project duration (months) Synthetic labels (%) Synthetic input tokens / label Synthetic output tokens / label LLM input cost / 1M tokens ($) LLM output cost / 1M tokens ($)

Estimates are directional. Last updated: 2026-07-07. See notes.

Total labels

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Cost per label

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Total project cost

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Estimated timeline

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Human label cost

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Synthetic label cost

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Review cost

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Platform / tooling

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Setup cost

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Reviewed items

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Verdict

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Frequently asked questions

What types of labeling projects does this cover?

It covers image classification, text classification, named-entity recognition (NER), search relevance, bounding boxes, RLHF preference rankings, and synthetic labels with human validation.

How is cost per label calculated?

For vendor/crowd work, cost equals the per-label price times the number of labels. For in-house teams, the calculator uses labels-per-hour and an implied hourly rate derived from your setup labor cost.

What is the review rate?

Review rate is the percentage of items that need a second-pass human review or quality audit. It adds labor cost and improves accuracy but slows delivery.

When is synthetic labeling cheaper?

Synthetic LLM labeling is usually cheapest for text-heavy tasks where exact ground truth is less critical, but it needs a validation layer to catch systematic errors.

How can I reduce labeling spend?

Start with active learning, label the most uncertain examples first, use weak labels and LLM pre-labeling, reduce overlap votes after quality stabilizes, and pool similar tasks into larger batches.

Does this include model training cost?

No. Use the LLM Fine-Tuning Cost Calculator or Agent Output Value Calculator to estimate downstream training and ROI.

LLM Fine-Tuning Cost Calc → Agent Output Value Calc → Agent vs Hire Calc → Benchmark Selector →

Data labeling cost estimates blend typical vendor per-label pricing, crowd-labor rates, and synthetic LLM API cost. Replace defaults with quotes from your chosen provider. Quality and turnaround times vary by task complexity.