AI 트레이닝 데이터셋 시장 : 시장 점유율 분석, 산업 동향 및 통계 데이터, 성장 예측(2026-2031년)

According to Mordor Intelligence, the aI training dataset market size is expected to grow from USD 8.74 billion in 2025 to USD 11.91 billion in 2026 and is forecast to reach USD 49.82 billion by 2031 at 33.14% CAGR over 2026-2031.

This report is Segmented by Data Modality (Text, Image and Video, Audio and Speech, and More), Dataset Offering (Off-The-Shelf Datasets, Custom Dataset Creation, and More), Deployment (On-Premises, and More), End-User Industry (IT and Telecom, Automotive and Modality, Healthcare and Life Sciences, BFSI, Retail and E-Commerce, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global AI Training Dataset Market Trends and Insights

Expansion of Multimodal LLMs and Generative AI Workloads

The spread of multimodal large language models has changed what buyers expect from the AI training dataset market. Providers now need to supply synchronized text-image pairs, time-aligned video-audio sequences, and other records that preserve meaning across modalities rather than within a single data type alone. This has raised the value of datasets that can support both training and evaluation for image reasoning, video understanding, and cross-modal retrieval. The scaling challenge is visible in the MINT-1T release, which expanded open-source multimodal data by combining PDFs, HTML, and arXiv material into a 1.02 trillion-token corpus. The same demand is carried into agentic systems, where models need interaction traces, task demonstrations, and environment feedback beyond static labels. As a result, the artificial intelligence training dataset market is seeing faster growth in complex annotation and cross-modal quality assurance than in basic labeling volume.

Rising Demand for Domain-Specific Datasets in Regulated Workflows

The AI training dataset market is gaining momentum as regulated workflows require general-purpose corpora that are not sufficient. Healthcare, legal, and financial use cases require data that is de-identified, traceable, and labeled by qualified reviewers, which increases the value of suppliers that already operate in controlled environments. PhysioNet expanded on that pattern in 2025 with releases such as ER-REASON, which demonstrated ongoing institutional demand for research-grade clinical reasoning datasets under governed access terms. This is one reason healthcare is the fastest-growing end-user segment through 2031, as AI developers need annotated clinical notes, medical imaging, and structured records that can support high-stakes applications. The cost profile is also different from general data work because expert review, de-identification, and audit documentation are built into delivery rather than added later. That keeps margins firmer for providers embedded in regulated workflows and makes domain access a durable advantage in the artificial intelligence training dataset market.

Data Privacy, Sovereignty, and Compliance Burdens

Privacy and compliance rules remain the most structural restraint on the AI training dataset market. The EU AI Act enters full enforcement on August 2, 2026, and requires high-risk AI systems to use datasets that are relevant, representative, and documented with strong traceability. Those obligations interact with GDPR data minimization rules, which can limit the amount of personal information that may be retained in training corpora. That tension increases project costs because providers need localized workflows, stronger documentation, and more legal review before data can move into production. It is especially difficult in healthcare, finance, and public-sector deployments, where representativeness and privacy must be demonstrated simultaneously. The AI training dataset market will continue to grow, but providers that cannot support provenance, localization, and auditability will face a narrower addressable customer base.

Other drivers and restraints analyzed in the detailed report include:

1. Greater Use of Synthetic and Simulated Data
2. Scaling of Physical AI and Autonomous Systems
3. High Cost of Expert Annotation and Quality Assurance

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Text data accounted for 46.53% of the AI training dataset in 2025, making it the largest modality. That lead reflected continued demand for pretraining corpora, instruction-tuning datasets, and evaluation material for large language models across both frontier and enterprise development programs. The structure of LLM training still favors text because pretraining, supervised fine-tuning, and alignment each require distinct text assets, and each step imposes higher quality thresholds than the one before. This has kept demand steady for licensed corpora, specialist instruction sets, multilingual material, and human preference data. NVIDIA's HelpSteer3-Preference release in 2025 illustrated that shift by providing more than 40,000 human-annotated preference pairs across STEM, coding, and multilingual tasks under a CC-BY-4.0 license. In practice, this means the AI training dataset market continues to rely on text as the foundation for model capabilities, even as other modalities gain ground.

Audio and speech data remain stable because voice interfaces, multilingual recognition, and low-resource language initiatives still require labeled speech and paralinguistic features. Multimodal data is gaining importance as developers increasingly combine text with image, audio, and structured context inside a single training flow. Video data is the fastest-growing modality, with a 33.94% CAGR through 2031, driven by clip-level alignment, dense captioning, and temporally ordered events for vision-language and physical AI systems. The supply challenge is more severe in video than in static-image work because action boundaries, scene changes, and synchronized instructions all require precise timing and review. MINT-1T demonstrated the scale of infrastructure needed to train competitive multimodal models, pushing open-source multimodal corpora to far larger token volumes than earlier datasets. As a result, the AI training dataset industry is moving toward a model in which text remains foundational, while video becomes the primary driver of higher-value annotation demand.

Off-the-shelf datasets accounted for 46.84% of the AI training dataset market in 2025, maintaining their leading position across offering types. Buyers favored this model when speed, cost control, and standard use cases mattered more than deep customization. Catalog-based procurement is still useful for early model development, testing, and generalized training tasks where common benchmarks and broad corpora are acceptable. That advantage is reinforced by the maturing marketplace layer, where structured metadata and standardized license terms reduce procurement friction. The launch of licensing structures for AI training content in 2025, including the Copyright Licensing Agency's Generative AI Training License, reflected the move toward more formalized exchange models. This helps the AI training dataset market maintain a large standardized supply channel even as enterprise requirements become more specific.

Custom dataset creation is the fastest-growing offering, with a 33.74% CAGR through 2031, because regulated and domain-heavy buyers need corpora that catalog products that are rarely provided by cataloging systems. Healthcare, BFSI, government, and other high-scrutiny users want bespoke datasets with documented provenance, compliance support, and bias review that can fit a defined workflow. Rights-cleared content is part of that shift, as shown by the New York Times licensing agreement with Amazon in May 2025 for AI training access to newsroom archives and affiliated properties. This creates a more split revenue structure inside the AI training dataset market, with high-volume standard products on one side and lower-volume, higher-margin custom work on the other. It also favors providers that can combine expert annotation, legal clearance, and audit-ready documentation within a single delivery model. The AI training dataset industry is therefore moving toward a more layered commercial structure rather than a single dominant procurement format.

Geography Analysis

North America accounted for 34.11% of the AI training dataset market share in 2025, driven by frontier AI labs, hyperscaler infrastructure, and enterprise buyers prioritizing expert-annotated, rights-cleared data. The U.S. leads demand with high-spend users in healthcare, financial services, and defense, deploying advanced models. Scale AI's 2025-2026 office expansion highlighted providers growing near major enterprise AI hubs. Canada supports demand with autonomous vehicle development and bilingual NLP work, while Mexico offers cost-efficient labor for U.S.-linked annotation programs.

Asia-Pacific is projected to grow at a 34.14% CAGR, the fastest in the market, through 2031. Government-backed AI programs in China, India, and South Korea drive demand across manufacturing, healthcare, smart cities, and autonomous systems. India combines a large annotation labor pool with growing expert-level workflows in medical, legal, and reasoning data. China boosts demand through public and private AI investments, while Japan and South Korea focus on automotive, semiconductor, and precision manufacturing AI programs requiring sensors and multimodal data.

Europe's AI training dataset market is shaped by compliance-driven procurement rather than annotation volume. The EU AI Act's Article 10 pushes developers toward documented, auditable, and bias-examined datasets for high-risk applications, favoring specialist European providers. AI Verse's EUR 5 million (USD 5.3 million) January 2026 funding reflects interest in synthetic computer vision data amid compliance needs. South America, led by Brazil, sees emerging demand for fintech and agritech that requires local text and geospatial data. The Middle East and Africa are at early stages, with Qatar, Saudi Arabia, and the UAE advancing domestic data procurement and the development of unstructured data.

1. Scale AI, Inc.
2. Appen Limited
3. Samasource Impact Sourcing, Inc.
4. iMerit Technology Services Private Limited
5. Labelbox, Inc.
6. SuperAnnotate AI, Inc.
7. DefinedCrowd Corporation
8. Dataloop Ltd.
9. Kili Technology SAS
10. Toloka AI B.V.
11. Shaip
12. Cogito Tech LLC
13. Clickworker GmbH
14. LXT AI, Inc.
15. CloudFactory Limited
16. NEXDATA TECHNOLOGY INC.
17. Innodata Inc.
18. Snorkel AI, Inc.
19. Tonic.ai
20. V7 Ltd.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Expansion of Multimodal LLMs and Generative AI Workloads
  • 4.2.2 Rising Demand for Domain-Specific Datasets in Regulated Workflows
  • 4.2.3 Greater Use of Synthetic and Simulated Data
  • 4.2.4 Scaling of Physical AI and Autonomous Systems
  • 4.2.5 Shift Toward Post-Training Preference, Agent Trajectory, and Evaluation Data
  • 4.2.6 Growth of Rights-Cleared Licensed Content Markets
• 4.3 Market Restraints
  • 4.3.1 Data Privacy, Sovereignty, and Compliance Burdens
  • 4.3.2 High Cost of Expert Annotation and Quality Assurance
  • 4.3.3 Training-Data Contamination from AI-Generated Web Content
  • 4.3.4 Fragmented Licensing Provenance and Chain-of-Custody Requirements
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Suppliers
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Data Modality
  • 5.1.1 Text
  • 5.1.2 Image and Video
  • 5.1.3 Audio and Speech
  • 5.1.4 Multimodal and Sensor-Rich Data
• 5.2 By Dataset Offering
  • 5.2.1 Off-the-Shelf Datasets
  • 5.2.2 Custom Dataset Creation
  • 5.2.3 Dataset Marketplaces and Licensed Exchanges
• 5.3 By Deployment Model
  • 5.3.1 On-premises
  • 5.3.2 Cloud
  • 5.3.3 Hybrid
• 5.4 By End-User Industry
  • 5.4.1 IT and Telecom
  • 5.4.2 Automotive and Mobility
  • 5.4.3 Healthcare and Life Sciences
  • 5.4.4 BFSI
  • 5.4.5 Retail and E-commerce
  • 5.4.6 Government and Defense
  • 5.4.7 Media and Entertainment
  • 5.4.8 Manufacturing and Industrial
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 South America
    • 5.5.2.1 Brazil
    • 5.5.2.2 Argentina
    • 5.5.2.3 Rest of South America
  • 5.5.3 Europe
    • 5.5.3.1 United Kingdom
    • 5.5.3.2 Germany
    • 5.5.3.3 France
    • 5.5.3.4 Italy
    • 5.5.3.5 Spain
    • 5.5.3.6 Rest of Europe
  • 5.5.4 Asia-Pacific
    • 5.5.4.1 China
    • 5.5.4.2 Japan
    • 5.5.4.3 India
    • 5.5.4.4 South Korea
    • 5.5.4.5 Rest of Asia-Pacific
  • 5.5.5 Middle East and Africa
    • 5.5.5.1 Middle East
      • 5.5.5.1.1 United Arab Emirates
      • 5.5.5.1.2 Saudi Arabia
      • 5.5.5.1.3 Rest of Middle East
    • 5.5.5.2 Africa
      • 5.5.5.2.1 South Africa
      • 5.5.5.2.2 Egypt
      • 5.5.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Scale AI, Inc.
  • 6.4.2 Appen Limited
  • 6.4.3 Samasource Impact Sourcing, Inc.
  • 6.4.4 iMerit Technology Services Private Limited
  • 6.4.5 Labelbox, Inc.
  • 6.4.6 SuperAnnotate AI, Inc.
  • 6.4.7 DefinedCrowd Corporation
  • 6.4.8 Dataloop Ltd.
  • 6.4.9 Kili Technology SAS
  • 6.4.10 Toloka AI B.V.
  • 6.4.11 Shaip
  • 6.4.12 Cogito Tech LLC
  • 6.4.13 Clickworker GmbH
  • 6.4.14 LXT AI, Inc.
  • 6.4.15 CloudFactory Limited
  • 6.4.16 NEXDATA TECHNOLOGY INC.
  • 6.4.17 Innodata Inc.
  • 6.4.18 Snorkel AI, Inc.
  • 6.4.19 Tonic.ai
  • 6.4.20 V7 Ltd.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment