Data Annotation vs. Data Labeling: What's the Difference?

Are data annotation and data labeling the same thing?

Ask five vendors whether data annotation and data labeling mean the same thing, and expect at least three different answers. Some use the words as pure synonyms across their own product pages. Others draw a real line: labeling means assigning a single, predefined tag to an item, while annotation means adding richer structured markup, a bounding box, a segmentation mask, a named-entity span, a transcript, a written rationale, that a flat tag can't capture. Both usages show up constantly in the wild, so neither one is wrong on its own. What matters for a buyer is knowing which one a specific vendor means before a scope document gets written, because the two usages imply different deliverables and different pricing units.

The precise version: labeling is the narrower term when practitioners bother to distinguish them at all, and annotation is the superset. Every labeling task is a form of annotation. Not every annotation task is what most people mean by labeling. But three of the biggest names in the industry, Amazon Web Services, Labelbox, and Appen, use "labeling" and "annotation" interchangeably in their own documentation ^[6][8][10]. Treating the word alone as a reliable signal of scope is a mistake. Treating the underlying task, taxonomy, and output format as the real spec is not.

What data labeling means when the term is used narrowly

In its narrowest, most defensible sense, data labeling means assigning one value from a fixed, predefined list, a taxonomy, to a whole item. A photo gets one category tag. An email gets "spam" or "not spam." A support ticket gets a topic. AWS's own definition captures this cleanly: it describes labeling as identifying raw data and adding meaningful, informative labels so a model can learn from it. ^[4] The international standard ISO/IEC 22989:2022 is more precise still: it defines a label, specifically, as "the target variable assigned to a sample," nothing more. ^[8] What makes this the narrow case is the output shape: one flat value, chosen from a closed set, applied to an entire item. There's no coordinate geometry, no span boundary, no free text involved. It's the fastest, cheapest, and most standardized category of annotation work, and it's genuinely what most people picture when they hear the word "labeling."

What data annotation covers that a narrow reading of labeling doesn't

Data annotation, used precisely, is the umbrella. It includes everything labeling covers, plus every task that requires marking structure inside an item rather than tagging the item as a whole. SuperAnnotate's own breakdown of annotation task types runs from image classification and bounding boxes through instance, semantic, and panoptic segmentation, keypoint and pose estimation, optical character recognition, named-entity recognition, coreference resolution, and audio transcription. ^[4] Appen's data-annotation offering spans a similarly wide list: intent classification and entity recognition in text, bounding boxes and instance segmentation in images, verbatim transcription and speaker diarization in audio. ^[6]

The pattern across both vendors is the same: classification, the labeling-style task, is one line item on a much longer menu. The rest of that menu, drawing a box, tracing a polygon, marking where one entity ends and another begins, transcribing speech verbatim with timestamps, ranking two model responses against each other, needs an annotator to construct something rather than pick from a list: coordinates, offsets, a transcript, a ranking. That construction step is the real dividing line, on the rare occasions a dividing line gets drawn at all.

That's a rare formal answer to the question this article opened with, and it's an authoritative one: ISO/IEC 22989:2022, the international standard for AI concepts and terminology, treats a label as one specific kind of descriptive information that annotation can attach, alongside metadata and anchors, not as another word for the same process. ^[2] It's the clearest evidence available that, wherever a genuine hierarchy exists between the two terms, annotation sits above labeling, not beside it.

Where vendor and practitioner usage diverges

The complication: the vendors who build the tools most buyers use don't enforce this line, and their own documentation proves it. AWS's SageMaker Ground Truth developer guide lists at least 13 built-in task types, from single-label and multi-label image classification through bounding boxes, semantic segmentation, named-entity recognition, and 3D point-cloud tracking, and files every one of them under the single umbrella of a "labeling job." ^[2] A bounding box and a one-tag classification task are, in AWS's own product taxonomy, both just "labeling."

Labelbox names its own annotation product "the data labeling platform" and describes drawing bounding boxes, polygons, and segmentation masks as "labeling data" throughout its documentation. ^[2] Appen does the same thing from the other direction: its data-annotation page opens by defining annotation as, in its own words, the process of labeling raw data to make it usable for machine learning. ^[4] Three companies that between them process an enormous share of the world's commercial annotation volume treat the two words as synonyms in their own marketing and technical documentation.

So when a real distinction does get drawn in practice, it usually isn't coming from a platform's product naming. It's coming from buyers and specs that need a shorthand for "simple, one-tag classification" versus "everything else," and land on the same hierarchy ISO/IEC 22989:2022 already formalized: annotation as the process, label as one of its outputs. ^[2] No single authority enforces that hierarchy across the commercial market, which is exactly why AWS, Labelbox, and Appen can each use the words as synonyms without contradicting a standard most of their customers have never read.

The ambiguity isn't a minor footnote at the size this market has reached. Grand View Research sizes the global data labeling market at $18.63 billion in 2024, projected to grow to $57.63 billion by 2030. ^[6] A narrower forecast from MarketsandMarkets, scoped specifically to "data annotation and labeling," puts 2022 volume at $0.8 billion growing to $3.6 billion by 2027, a 33.2% CAGR. ^[12] A meaningful share of that spend runs through contracts where the word "labeling" or "annotation" is doing real scoping work, whether or not the two parties agree on what it means.

Labeling vs. annotation tasks, side by side

Because vocabulary isn't reliable, the table below sorts by what a task requires an annotator to produce, not by which word a vendor markets it under.

Notice the billing-unit column does most of the real work. AWS's own Ground Truth pricing charges by the number of dataset objects reviewed, and vendor-workforce cost is set per label by the vendor. ^[2] A busy image with several objects to box costs more than a plain image with one classification tag, even if both show up as "one file" on a spec sheet. That's the mechanism that actually matters, not whichever word appears in the contract's title.

A worked example: scoping a product-catalog project that needs both

Take a mid-size apparel marketplace launching visual search and structured attribute filters. The raw material: 20,000 product photos and their existing text descriptions. A vague RFP would ask a vendor to quote "labeling 20,000 product listings." A precise one decomposes the job first.

• 1Separate the tasks by deliverable shape, not by vocabulary. This project contains three distinct tasks: a category tag per photo (labeling, narrow sense), a garment bounding box per photo (annotation), and attribute spans on the description text for color, material, fit, and pattern (annotation).
• 2Count units, not files. Twenty thousand photos isn't the real unit count. If the taxonomy has 42 fixed categories, that's 20,000 classification tags. If photos average 1.4 visible garments each (a model wearing a top and a jacket counts twice), that's roughly 28,000 bounding boxes. If descriptions average 3.5 taggable attribute mentions each, that's roughly 70,000 spans.
• 3Write the taxonomy and output schema before requesting quotes. The 42-category list, the box schema (x, y, width, height, category), and the span schema (start offset, end offset, attribute type) all need to exist as a document a vendor can quote against, not as a verbal description of "labeling the catalog."
• 4Ask every candidate vendor to quote against the decomposed unit counts, not against "20,000 images." A vendor who reads "labeling" as flat per-image classification and a vendor who reads it the way AWS's product does, encompassing bounding boxes, will price the same word differently unless the RFP pins down the task itself.
• 5Budget the QA layer as its own line, regardless of which tasks are involved. Inter-annotator agreement checks, gold-standard items, and spot audits apply to labeling and annotation tasks alike; Dayda's companion guide to how labeling quality gets measured covers the specific metrics and thresholds to ask a vendor for.

Decomposed this way, the category tags are the cheapest line on the invoice by a wide margin: one flat value, applied once, with no coordinates or offsets to check. The bounding boxes and attribute spans cost more per unit precisely because AWS's own billing logic generalizes across the industry, charging by the object reviewed rather than the file. ^[2] A vendor quoting the whole project as a single per-image "labeling" rate is almost always folding the box and span work into that number somewhere, and a buyer who can't see the decomposition can't tell whether that number is fair.

What to ask a vendor, regardless of which word is on their homepage

Since the terminology won't reliably tell a buyer what they're paying for, the questions below do the work the vocabulary can't. Ask them before a statement of work gets signed, not after the first batch of deliverables comes back looking different from what a phone call implied.

• 1What is the exact task type and taxonomy version? "Labeling" or "annotation" alone isn't a task. Classification against a 42-item taxonomy, bounding boxes against a 5-class object list, and named-entity tagging against a 12-entity schema are three different jobs that happen to share a verb.
• 2What is the billing unit, and how is a "unit" defined in the contract? Per item, per object, per span, per audio-minute, and per hour are not interchangeable, and a contract that leaves "unit" undefined leaves the total cost undefined too.
• 3What does the output schema look like before work starts? A vendor should be able to hand over a sample JSON object for a single labeled or annotated item before the batch begins, not after delivery.
• 4What are the inter-annotator agreement target, the gold-standard pass rate, and the QA sampling percentage? These apply whether the task is a simple tag or a complex annotation, and a vendor who can't produce numbers for all three is asking a buyer to trust the work on faith.
• 5Which workforce will actually perform the task, and does it match the task's judgment requirement? A generalist crowd workforce is a fine fit for spam tagging and a poor fit for clinical entity spans or nuanced preference rankings, independent of whether the contract calls the work "labeling" or "annotation."
• 6For preference ranking, red-teaming, or rationale-writing tasks specifically, is that scoped and priced as its own line? These annotation-family tasks are often quoted separately from both "labeling" and "annotation" line items because the judgment involved is closer to expert review than to tagging.

A buyer who works through this checklist gets a comparable quote regardless of which word two vendors prefer. Dayda's broader guide to buying AI training data walks the rest of the purchase process, provenance vetting, sampling, and negotiation, once the task itself is properly scoped.

The word doesn't decide the scope. The spec does.

Data labeling and data annotation overlap enough, and get used interchangeably by enough of the industry's biggest platforms, that chasing a universal definition is a waste of a buyer's time. The precise version of the distinction, labeling as a single predefined tag, annotation as the broader category that includes labeling plus structured markup, is worth knowing, because it explains why a bounding box costs more than a classification tag and why a preference ranking gets quoted differently than either. But no vendor is obligated to use the words that way, and several of the largest don't.

What protects a project's budget and timeline is a scope document that names the task, the taxonomy, the output schema, and the billing unit explicitly, so "labeling" or "annotation" never has to carry meaning on its own. For a buyer weighing whether to build that pipeline in-house or source data that already comes with structured, schema-defined annotations attached, the second option is usually the faster path to a working model.