AI insights · Pricing

AI data annotation cost in the UK (2026)

Labelling the data behind an AI model in the UK typically costs about £0.04 to £0.70 per object for image bounding boxes in 2026, with a full 20,000-image project landing near £2,000 outsourced or £12,000-plus if you build the capability in-house. This guide gives current, sourced ranges so you can budget the least glamorous part of AI honestly.

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Data annotation is the work of labelling raw data — drawing boxes around cars in a photo, tagging the sentiment of a review, marking where a name appears in a contract — so a model can learn from it. It is priced three ways: per label, per hour, or per project. As a rough anchor, straightforward image labels run £0.02–£0.12 each, a bounding box around an object sits around £0.04–£0.70, and richer work like pixel-level segmentation climbs to £0.40–£1.60 an image and beyond. Specialist medical or legal labelling, where you need a domain expert holding the pen, is a different game entirely at £0.80 to £4-plus per item.

One honest caveat before the numbers. Almost every published annotation rate is quoted in US dollars and delivered by offshore teams. The figures here are converted to sterling as indicative ranges, with UK-onshore work sitting firmly at the top of each band — you pay a premium of several times over for a labeller in Manchester rather than Manila. Whether that premium is worth it depends entirely on how sensitive your data is and how much the labels being wrong would cost you.

Indicative pricing

What labelling costs by data type

Annotation type	Typical UK-indicative unit cost (2026)
Image classification (per label)	£0.02–£0.12
Object detection, bounding box (per object)	£0.04–£0.70
Semantic segmentation (per image)	£0.40–£1.60
Text and sentiment tagging (per item)	£0.01–£0.08
Named-entity recognition (per item)	£0.04–£0.20
Audio transcription (per minute)	£0.40–£2.40
Video annotation (per minute)	£0.80–£8.00
Specialist / medical labelling (per item)	£0.80–£4.00+

Sources: GigaBPO data labeling benchmarks 2026; DataX Power annotation pricing 2026; HabileData annotation cost guide 2026; BasicAI data annotation cost guide; Label Your Data pricing. · Indicative ranges converted to GBP, updated September 2026.

Who does the work

Per-hour rates and what a whole project costs

Unit prices are handy for a quick estimate, but many teams are billed by the hour, and the spread there is enormous. A crowdsourced labeller working through a platform might cost a few pounds an hour; a trained specialist reviewing sensitive records costs ten times that. The gap is not markup — it is consistency. Expert annotators cost far more per hour and produce labels a model can actually trust, which is why cheapness at the label level so often turns into an expensive re-labelling exercise later.

Delivery model / scale	Indicative UK-relevant cost (2026)
Crowdsourced / offshore annotator	£5–£12 per hour
Managed offshore team (with QA)	£8–£20 per hour
UK onshore / specialist annotator	£24–£47 per hour
20,000 product photos, outsourced end to end	~£2,000
20,000 product photos, in-house team + tooling	£12,000–£14,000
10,000 medical images, expert-labelled	~£19,500

Sources: GigaBPO data labeling cost benchmarks 2026; BasicAI complete pricing guide; DataX Power annotation pricing 2026. · Indicative ranges converted to GBP, updated September 2026.

Notice the in-house versus outsourced line. For a one-off batch, outsourcing wins on price almost every time — that same 20,000-image job is roughly six times cheaper handed to a specialist provider than staffed internally, once you count tooling, management and quality control. In-house only starts to make sense when labelling is continuous, the data is too sensitive to leave the building, or the domain knowledge simply cannot be handed to a stranger.

Cost drivers

What actually moves the price

Complexity of the label

Ticking “cat or dog” on a photo is pennies. Drawing a precise outline around every pedestrian in a street scene, frame by frame, is a different order of effort — and rates rise sharply as the number of classes and the precision demanded go up.

Who is allowed to label it

General data can go to a crowd. A radiology scan or a legal clause needs a qualified pair of eyes, and expert annotators cost five to fifteen times more per hour. Domain requirements are the fastest way to move from pennies to pounds per item.

Quality and review rounds

The headline rate is never the whole bill. Written guidelines, quality checks and rework routinely add 25 to 50 percent, and having two people label the same item to catch disagreement — consensus labelling — can add another 20 to 50 percent. It buys accuracy you will be glad of.

Volume

Bigger batches earn discounts: pass ten thousand labels and per-unit rates commonly drop 10 to 30 percent. But volume cuts both ways — a huge, badly briefed dataset just means you pay to label the wrong thing at scale.

All of this sits upstream of the model, which is why we treat it as part of AI data engineering rather than an afterthought. Get the labels right and the rest of an AI-driven development project runs smoother; get them wrong and no amount of clever modelling rescues it.

Buy well

How to brief an annotation project

The buyers who get clean data for a fair price tend to do the unglamorous work first: they write down exactly what a good label looks like before anyone starts drawing boxes. Ambiguity is what you actually pay for — every case an annotator has to guess at is a case you will re-do.

Write a labelling guide with real examples of edge cases — the blurry photo, the half-hidden object, the sarcastic review — and decide the rule before, not after.
Ask how quality is measured. Consensus scoring, gold-standard test items and a named reviewer matter more than the per-label price.
Start with a small paid pilot of a few hundred items. It surfaces gaps in your guidelines cheaply, before they are baked into fifty thousand labels.
Get set-up, per-item rate and quality-assurance overhead quoted as separate lines, so you can see what you are really paying for.
Confirm who owns the labelled dataset and that it will not be reused to train anyone else’s model.

Data changes under you, too. A model trained on last year’s product photos drifts as the catalogue turns over, so labelling is rarely a one-and-done purchase — pairing it with AI-driven monitoring is how you catch the day the labels stop matching reality.

Watch for

Red flags when comparing quotes

A price with no quality plan

A rock-bottom per-label rate and no mention of review rounds, gold standards or accuracy targets is not a bargain. You are buying labels you will have to check yourself, which is the cost moved rather than removed.

No pilot on offer

A supplier confident in their process will happily label a small batch first. Reluctance to pilot usually means the guidelines are thin or the accuracy will not survive contact with your real, messy data.

Vague on data handling

If your data includes anything personal or regulated, a provider who cannot explain where labelling happens, who sees it and how it is deleted is a data-protection problem waiting to happen.

Fully automated, no humans

AI-assisted pre-labelling genuinely cuts cost, but a pipeline with no human in the loop quietly bakes its own mistakes into your training set. Ask where people check the machine’s work.

We are a founder-led, senior-only UK studio, and we build and operate our own AI products in regulated and consumer-facing sectors — so the trade-offs above come from labelling data for systems we actually ship, not from reselling someone else’s service. If you want a second opinion on an annotation quote or a realistic figure for your own dataset, there are more pricing breakdowns across our insights hub.

FAQ

Data annotation cost: common questions

It depends on the data type and who labels it. As a guide, simple image labels run £0.02–£0.12 each, bounding boxes £0.04–£0.70 per object, and pixel-level segmentation £0.40–£1.60 an image. By the hour, offshore labelling starts around £5 and UK onshore or specialist work reaches £24–£47. A full 20,000-image project is roughly £2,000 outsourced against £12,000–£14,000 done in-house.

For a one-off batch, outsourcing is almost always cheaper — a 20,000-image job runs around £2,000 with a specialist provider versus £12,000–£14,000 once you count an in-house team, tooling and quality control. In-house earns its place only when labelling is continuous, the data is too sensitive to send out, or the work needs domain knowledge you cannot hand to an outside crowd.

A basic bounding box around one object typically costs £0.04–£0.70, depending on how cluttered the scene is and how many object classes you are tracking. Pixel-perfect segmentation, where the outline follows the exact shape rather than a rough box, is far more work at £0.40–£1.60 an image and higher for dense scenes or medical imagery.

Four things, mostly. The complexity of each label, whether it needs a domain expert rather than a general worker, how many quality and review rounds you run, and your total volume. Guidelines, checks and rework alone add 25 to 50 percent on top of the base rate, and consensus labelling — two people on the same item — can add another 20 to 50 percent.

Less than vendors often imply, if you label the right examples. Rather than paying to annotate everything, start with a representative sample, train, see where the model fails, and label more of those hard cases — an approach that puts your budget where it changes the result. A small paid pilot of a few hundred items first will tell you far more than a large order placed blind.

Offshore is several times cheaper per hour and perfectly good for general, non-sensitive labelling at volume. UK onshore or specialist teams cost £24–£47 an hour but make sense when the data is personal or regulated, when accuracy is safety-critical, or when the labellers need context a remote crowd will not have. Many projects sensibly mix the two.

Partly. AI-assisted pre-labelling now does a first pass — the machine proposes labels and a person corrects them — and that has pushed some per-object rates down. What it does not do is remove the human safely. A pipeline with no one checking the model’s suggestions bakes its own errors into your training data, so the realistic saving is faster human review, not no humans at all.

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