Dataset
Hybrid QA review
βLLM pre-label
βhuman QA
βhybrid total
Hybrid cost by QA sample rate
Higher QA coverage costs more but catches more LLM mislabeling β the right rate depends on how costly a wrong label is downstream.
| QA sample | Hybrid total | Savings vs human-only | % saved |
|---|
Why the hybrid model beats a flat vendor quote
Every labeling vendor β Labelbox, Scale AI, Label Your Data and the rest β quotes a per-item rate for human annotation, and those rates are the right numbers to plug into the human-only side of this calculator. What most quotes don't model is the workflow that's become standard practice in 2026: have an LLM pre-label the entire dataset first, then route only a sample to human reviewers for quality assurance rather than having humans create every label from scratch. The LLM pre-label cost is typically a small fraction of a cent per item for straightforward classification or entity tasks β far below any human rate β and the human QA pass moves faster than first-pass labeling because verifying a proposed label is quicker than generating one, commonly 2-3x the speed and a correspondingly lower effective rate.
The variable that actually decides your savings is the QA sample rate, not the LLM cost β LLM pre-labeling is cheap enough at scale that it barely moves the total either way. Sample 10-25% and you keep most of the savings but accept that unreviewed LLM errors ship uncaught into the dataset. Sample 100% and you've effectively kept full human oversight, just at the faster QA rate instead of the slower creation rate β savings shrink but so does risk. There's no universally correct number: it's a direct trade against how expensive a wrong label is downstream, which is why the table above sweeps the full range rather than picking one for you.
This calculator is intentionally vendor-agnostic. Plug in your actual quoted per-item rate from whichever platform you use, adjust the QA multiplier to match your own reviewers' real speed differential, and the hybrid math holds regardless of provider. For the LLM-side token cost of a labeling prompt at real model prices, cross-check against the LLM price comparison tool.
How this calculator works
The AI Data Labeling Cost Calculator estimates the total cost of annotating a dataset under two workflows and shows the difference between them. The first is human-only labeling, where every item is priced at your human rate per item. The second is an LLM-assisted hybrid, where a model pre-labels each item at a lower per-item cost and a human reviews only a sampled share for quality assurance. The main drivers are the number of items to label, the task type (which sets default rates), your human-only rate, the LLM pre-label cost, the QA sample rate, and the QA-versus-full-label rate multiplier, since reviewing a pre-labeled item usually costs less than labeling from scratch.
The key trade-off is sample rate versus label quality. A lower QA sample rate cuts cost but catches fewer model errors, while a higher rate raises spend and narrows the hybrid's savings. Start by pricing your current human-only baseline, then test QA rates to find the point where projected savings still leave enough human review to keep error rates acceptable for your task type. Remember the LLM pre-label cost and the QA multiplier are estimates; verify them against a small pilot batch before committing to a workflow at full scale.
Frequently asked questions
How much cheaper is LLM-assisted labeling than human-only?
It depends heavily on how much human quality-assurance review you keep. An LLM can pre-label every item for a small fraction of a human labeler's per-item rate, but verifying a label is faster (and cheaper) than creating one from scratch -- reviewers typically move 2-3x faster on QA than on first-pass labeling. At a 25% human QA sample rate, hybrid workflows commonly land at 30-50% of human-only cost; reviewing 100% of LLM labels (recommended for safety-critical or regulated data) narrows that gap significantly since QA-at-scale approaches full labeling cost, just at the faster review rate rather than the slower creation rate.
What QA sample rate should I actually use?
There's no universal answer -- it's a function of how costly a wrong label is. Low-stakes, high-volume tasks (content categorization, non-critical recommendation signals) can often run at 10-25% QA sampling with spot-checks for drift. Anything feeding a decision with real consequences -- medical, legal, safety-classification, or data that trains a model making consequential downstream decisions -- generally warrants close to 100% human review of LLM-generated labels, at least until measured LLM accuracy on that specific task is proven stable over time.
Does this replace vendor-specific labeling platform pricing?
No -- platforms like Labelbox, Scale AI, or Label Your Data quote their own per-unit rates depending on task type, tooling, and managed workforce quality tier, and those numbers belong in the per-item rate field here. This calculator is vendor-agnostic: it models the workflow trade-off (human-only vs LLM-pre-label-plus-QA) rather than any single vendor's price sheet, so you can plug in your own quoted rate and see how the hybrid math changes your total regardless of which provider you use.