Short answer. There is no defensible universal price for large-scale AI data annotation, and any figure quoted without a task definition is noise. Published benchmarks run from around $0.10 per object in CVAT's 2025 illustrative example, dropping to $0.05–$0.075 per object under a prepaid volume subscription, to specialist work that costs orders of magnitude more per unit. Cost is driven by the billable unit, modality, objects per asset, task complexity, annotator expertise, quality target, turnaround and security requirement — not by the number of files. The only reliable buying method is a scoped pilot on representative data, followed by a volume quote compared on cost per accepted unit.
Annotation is one of the few enterprise purchases where the headline unit price routinely misleads by a factor of five or more. A bounding box around one clearly visible object is not economically comparable to pixel-level segmentation, multi-frame video tracking, a 3D LiDAR cuboid, a medical image, or an expert evaluation of an LLM response. Even two image projects diverge wildly if one averages two objects per image and the other averages twenty-three.
This guide sets out the actual cost drivers, the published benchmarks you can use honestly, what to demand in a pricing proposal, and how to convert incomparable quotes into a comparable number.
Why there is no universal price per annotation
Eight variables move the price, and they do not move independently.
| Cost driver | Usually lowers cost | Usually raises cost |
|---|---|---|
| Task complexity | Simple classification or boxes | Segmentation, keypoints, 3D, sensor fusion, reasoning |
| Volume | Large, predictable batches | Small irregular batches |
| Guideline stability | Clear fixed ontology | Frequent schema changes |
| Annotator expertise | General trained annotators | Doctors, lawyers, engineers, advanced STEM experts |
| Quality requirement | Single-pass or sampled QA | Multi-layer review, adjudication, near-zero tolerance |
| Turnaround | Flexible schedule | Urgent ramp-up or 24/7 coverage |
| Security | Standard controlled workflow | Restricted facilities, residency, specialised compliance |
| Input data quality | Clean, normalised inputs | Noisy, ambiguous or incomplete inputs |
The interaction that catches buyers out is between guideline stability and volume. A large committed volume earns a discount; a changing ontology destroys it, because every schema revision re-prices the work already done and re-calibrates the workforce. If your taxonomy is not settled, do not buy the volume tier.
Published benchmarks you can use honestly
These are published examples from named sources. They illustrate pricing mechanics. They are not market averages, and none of them is a Lifewood price.
| Published example | Price or cost | What it actually represents |
|---|---|---|
| CVAT per-object example (2025) | $0.10 per object | Illustrative project with 100,000 annotation objects |
| CVAT volume subscription example | $0.05–$0.075 per object | Illustrative discounted subscription for ~100,000 objects |
| Scale Rapid self-labelling | $0.05 per labelling unit after 200 free monthly units | Self-labelling software usage, not a managed enterprise quote |
| CVAT in-house case study | $122,220 plus software | Illustrative 100,000-image / 2.3M-object in-house project |
| CVAT outsourced case study | ~$225,400 | Illustrative outsourcing estimate for the same 2.3M-object scenario |
Two things are worth extracting from that table before anyone copies a number into a budget.
First, the same 100,000-image project appears at $122,220 and at $225,400 depending on who does the work — and the higher figure is the outsourced one. Outsourcing is frequently justified by speed, flexibility and avoided operating overhead rather than by a lower direct price. Be honest with your own finance team about which case you are making.
Second, the volume subscription halves the per-object rate. That is not a market rate; it is a demonstration that reserved capacity and prepayment change unit economics. Whether it changes yours depends on whether you can actually forecast the volume.
The metric that makes quotes comparable
Unit price is not cost. Rework is paid in schedule as well as money, and a rejected batch consumes your engineers' time as well as the vendor's.
Cost per accepted unit = Total project cost ÷ Units passing agreed acceptance criteria
Effective throughput = Items delivered × First-pass acceptance rate ÷ Cycle time
Suppose Vendor A charges 20% less per attempted label but generates substantially more rework. The apparent saving disappears once rejected units, reviewer time and model-team delay are counted. A vendor delivering 100,000 items a week at 70% acceptance is a 70,000-item vendor charging for 100,000.
This is why the acceptance definition has to be agreed before pricing is compared. Without it, "cost per accepted unit" has no denominator either.
What to require in a pricing proposal
Eight items, all of them non-optional for an enterprise programme:
- A paid or clearly scoped pilot using representative production data — including your hardest edge cases, not a clean sample.
- The exact billable unit: object, image, frame, minute, hour, task, token, record or project milestone.
- Separate treatment of QA, adjudication, rework and guideline-change costs. "QA included" without a measurable acceptance rule is not a term.
- Volume discount tiers and minimum commitments, with the unused-capacity treatment written down.
- Ramp-up assumptions, and how urgent capacity affects price.
- Platform, storage, integration and data-egress fees, if any.
- Security or restricted-facility premiums, priced separately so you can see what compliance costs.
- An agreed accuracy and acceptance definition rather than a quality adjective.
Red flags in annotation pricing
- A vendor gives a precise enterprise price without reviewing representative data. TELUS Digital's published guidance explicitly cautions against providers who quote before seeing the client's data, and the caution is well founded — price varies enormously by service and data type.
- The quote does not define what counts as one billable unit.
- QA or rework is described as "included" without measurable acceptance rules.
- Low unit rates depend on a minimum commitment you have not modelled.
- The provider cannot separate generalist, specialist and expert labour pricing.
- Platform, storage, integration or training charges surface only after selection.
- The vendor cannot explain how pricing changes when guidelines change. This one is the most expensive omission in the list.
How Lifewood approaches this
Lifewood does not publish a universal enterprise rate card, and this guide does not invent one. Pricing is scoped per project against modality, language, volume, quality target and security requirement.
What is published is the quality framework the commercial terms attach to: every project targets a minimum 95%+ accuracy SLA, enforced through trained annotators, senior review, automated consistency checks and client feedback loops, with below-threshold batches reworked at Lifewood's cost. That last term is the one procurement should focus on, because it aligns the vendor's incentive with accepted output rather than delivered volume.
Three structural factors affect total cost rather than unit price. 50+ languages means multi-market programmes do not need a separate language vendor per region. Coverage across text, image, audio, video and 3D point-cloud work means modality expansion does not trigger a new onboarding, security review and taxonomy reconciliation. And 40+ delivery centres across 30+ countries means a programme can ramp geographically without the buyer building equivalent operating teams internally.
None of that makes Lifewood the lowest headline rate on any given task, and a specialist may well win a specific workstream on a controlled benchmark. The claim is narrower and more useful: the costs that sit outside the label price are where enterprise annotation budgets actually go.
Sources and further reading
- CVAT published pricing and cost-analysis examples — per-object rates, volume subscription rates, and the in-house versus outsourcing case studies for a 100,000-image / 2.3M-object project — at cvat.ai.
- Scale published pricing for Scale Rapid self-labelling at scale.com/pricing.
- TELUS Digital guidance on selecting a data annotation company, including the caution against quotes issued before reviewing client data, at telusdigital.com.
- Lifewood quality framework and delivery figures published on lifewood.com.
- All numeric examples above are attributed to their source and describe specific published scenarios. They are not industry averages and not Lifewood prices.