DATA LABELING MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2025-2032)

Data Labeling Market Size and Forecast – 2025-2032

The global data labeling market is poised to reach at a sum of USD 4.87 Bn in 2025 to USD 29.11 Bn by 2032, exhibiting a compound annual growth rate (CAGR) of29.1% from 2025 to 2032.

Key Takeaways

• Based on Data Type, the Image/Video segment is poised to hold a share of 43.6% in 2025.
• By Vertical Insights, the IT & telecom division is projected command with a share of 31.9% in 2025.
• North America is the leading regional market, accounting for an estimated 31.60% of the market share in 2025, while Asia Pacific, holding a share of 28.4% in 2025, is expected to be the fastest-growing region.

Market Overview

As machine learning and AI become more popular, there is a growing need for large amounts of annotated datasets to train algorithms. Also, improvements in natural language processing and computer vision have increased the need for high-quality labeled datasets. A lot of tech companies and research groups are spending a lot of money to make their own datasets using data labeling platforms and crowd-sourced solutions to make the most advanced AI apps.

Current Events and Its Impact

Uncover macros and micros vetted on 75+ parameters: Get instant access to report

Role of AI in Data Labeling Market

The data labeling firm is using AI-powered automation to make things more accurate and easier to scale. Scale AI is a leading data labeling platform that has changed the game by adding machine learning algorithms directly into their labeling processes. Their platform combines human knowledge with AI pre-labeling, in which algorithms make initial annotations that human annotators then check and improve. This mixed method has allowed Scale AI to handle millions of data points for self-driving car companies, cutting labeling time by as much as 80% while keeping the accuracy needed for applications that are critical to safety.

For instance, in April 2024, Sapien AI Corp., announced it raised $5 million in a seed funding round to build out its service of providing high-quality annotation and labeling for training artificial intelligence models.

Segmental Insights

To learn more about this report, Download Free Sample

Data Lableing Market Insights, by Data Type – Image/Video Segment Dominates the Global Data Labeling Industry

In terms of data type, image/video segment is estimated to comprise the largest portion of 43.6% in the market in 2025. A key driver of this segment's success is the continued development of computer vision and machine learning techniques that require massive visual datasets for training image recognition models.

As object detection, image classification, semantic segmentation, and other computer vision tasks become increasingly sophisticated, the demand grows exponentially for labeled images to power applications in areas like autonomous vehicles, medical imaging, surveillance, smartphone cameras, and others.

For example, online retailers may have catalogs containing millions of product images that need to be consistently tagged. Social networks and cloud storage providers also accumulate huge repositories of user-uploaded photos that could benefit from automatic tagging.

Sahara AI, a company based in Los Angeles, has launched the Data Services Platform (DSP), which rewards users with cryptocurrency for performing data annotation tasks like labeling images, transcribing audio, or assessing AI-generated text. Such innovations are accelerating the data labeling market demand.

Data Lableing Market Insights, by Vertical – IT & Telecom Leads Adoption Due to Data-intensive Nature of the Sector

Based on vertical, the IT & telecom sector has emerged as the largest adopters of data labeling services, holding an estimated share of 31.9% in 2025. Many factors contribute to this leading position, but chief among them is the data-intensive nature of work in the IT sector.

Companies involved in software, cloud services, internet infrastructure, and related fields routinely accumulate vast troves of customer data that require sorting and tagging. Whether it's log files, support tickets, website content, app usage metrics, or communications data, the volumes have grown exponentially in the big data era.

As a result, IT companies have become early adopters of machine learning and the application of neural networks to assist with processing their datasets at scale. A ready supply of skillfully annotated examples is crucial to training these AI systems and keeping pace with technological change.

Regional Insights

To learn more about this report, Download Free Sample

North America Data Labeling Market Trends

North America is expected to dominate the data labeling market revenue, holding a share of 31.6% in 2025. The region’s lead can be attributed to a strong presence of global technology companies and growing focus on artificial intelligence and machine learning technologies. Government initiatives to support research and development of emerging technologies have also contributed to North America's leadership.

Asia Pacific Data Labeling Market Trends

The Asia Pacific region, holding a share of 28.4% in 2025, is expected to exhibit the fastest growth in the data labeling market owing to increasing digital transformation across industries in countries like China, India, and South Korea. Large population and rapid technological adoption provide immense opportunities for data annotation services in Asia Pacific.

Data Labeling Market Outlook for Key Countries

U.S. Data Labeling Market Trends

The U.S. is still the leader in the global data labeling market because of cutting-edge AI research, big investments in automation, and the widespread use of machine learning (ML) in many fields. Big tech companies like Amazon, Microsoft, and Google are at the forefront of machine learning. They use huge datasets to train AI models for things like self-driving cars, healthcare diagnostics, and personalized recommendation systems.

Local companies like Scale AI, Labelbox, and Appen USA also play an important role in providing annotation solutions to businesses and research institutions. Government-backed AI projects and university-led research programs are also helping the market grow, making sure that data labeling technologies keep getting better.

China Data Labeling Market Trends

The China data labeling market is growing steadily because the government wants to speed up the use of AI and AI is becoming more common in both industrial and consumer applications. Alibaba, Tencent, and Sensetime are some of the companies that are leading the way in creating deep learning models that use huge amounts of labeled data for things like facial recognition, smart surveillance, and self-driving cars.

Additionally, local players such as iFlytek, Megvii, and ByteDance are making significant contributions by advancing AI-driven annotation tools. The Chinese government's AI roadmap, which puts a high value on being able to collect and analyze data on its own, has led to strategic partnerships between tech companies, research institutions, and government agencies. These partnerships have made the data labeling ecosystem even stronger.

For instance, in September 2025, China announced to enforce regulations requiring both explicit and implicit labeling of AI-generated content. Announced by the Cyberspace Administration of China (CAC), the measures aim to combat misinformation and enhance digital transparency. 

India Data Labeling Market Trends

India has established itself as a global outsourcing hub for data annotation, due to its large pool of skilled workforce, cost-effective services, and strong IT infrastructure. Global service providers like Wipro, Infosys, and Tech Mahindra are actively offering high-quality data labeling services to AI-driven companies worldwide.

Startups such as iMerit, Cogito Tech, and Playment have also gained traction by providing specialized annotation solutions for industries like healthcare, autonomous driving, and e-commerce. The government’s push for digital transformation, the rise of AI-focused startups, and collaborations with international firms have reinforced India’s position as a key player in the global AI training data supply chain.

For instance, in September 2025, Uber announced to turn its 1.4 million driver partners in India into a workforce for its latest venture: AI data labeling.

U.K. Data Labeling Market Trends

The U.K. data labeling market is experiencing rapid adoption, driven by the rising implementation of AI-powered solutions across sectors like healthcare, finance, and autonomous vehicles. Companies such as Anthropic, Appen, and Scale AI are contributing to AI model training by developing high-quality labeled datasets for predictive analytics, fraud detection, and medical imaging applications.

Additionally, local firms like Mindtech Global, Faculty AI, and DeepMind are playing a crucial role in refining AI-driven annotation techniques. The U.K. government’s support for AI research, combined with strong industry-academic collaborations and funding for AI-based startups, is fostering a robust ecosystem for data labeling and annotation services.

Market Players, Key Development, and Competitive Intelligence

To learn more about this report, Download Free Sample

Key Developments

• In August 2025, BigID, announced Data Labeling for AI, a new capability that helps organizations classify and control which data can be used in generative AI models, copilots, and agentic AI systems.
• October 2024, Clarifai, Inc., a company engaged in computer vision and AI orchestration, partnered with Crimson Phoenix, a provider of data-enabled solutions, to enhance AI-driven data labeling and computer vision technologies for unstructured data
• In September 2024, the National Geospatial-Intelligence Agency (NGA), a combat support agency within the U.S. Department of Defense, announced plans to launch a USD 700 million data labeling competition aimed at enhancing AI and machine learning capabilities

Market Report Scope

Data Labeling Market Report Coverage

Market Dynamics

To learn more about this report, Download Free Sample

Global Data Labeling Market Driver - Rapid adoption of AI and ML technologies across various industries

The global business landscape is witnessing significant technological advancements in the form of artificial intelligence and machine learning applications. These next generation technologies are finding widespread usage across major industry verticals like healthcare, automotive, banking & finance, manufacturing, and others. AI-based algorithms are being utilized to automate mundane tasks, enhance decision making capabilities, obtain useful insights from large volumes of data and much more.

However, for AI/ML models to perform with high levels of accuracy, they need to be fed with huge troves of labeled input data. Labeling is the process of manually examining raw data like text, images, audio/video files and associating appropriate labels with them that clearly identify or classify what the data represents. This labeled data is then used to train AI algorithms which helps them in learning complex patterns and relationships within the information to eventually be able to process new unlabeled data on their own.

With AI becoming deeply ingrained in modern business processes, organizations are ramping up their adoption of advanced analytics solutions driven by machine learning techniques. This widespread integration of AI technologies across sectors have made the availability of labeled data an imperative.

Global Data Labeling Market Opportunity - Emergence of automated data labeling tools and platforms

One major opportunity for the global data labeling market is the emergence of automated data labeling tools and platforms. Various AI-based technologies such as computer vision, natural language processing, and machine learning are now enabling the automation of certain data labeling tasks. Automated data labeling solutions can significantly reduce the dependence on human annotators and the associated costs.

These platforms employ the latest ML techniques to streamline data collection, annotation and management. The advancement of automated data labeling tools is expected to disrupt the market by lowering the entry barriers for organizations and boosting the overall revenues of the data labeling industry.

Analyst Opinion (Expert Opinion)

• The data labeling market is projected to grow significantly over the forecast years, driven by the rapid adoption of AI and ML technologies across various sectors. The increasing need for high-quality labeled data to train these models is a key factor propelling market growth.
• A major hindrance to market growth could be the high costs associated with data labeling processes and concerns regarding data privacy and security.
• The North America region is expected to continue dominating the market, attributed to its technological advancements and strong demand for AI and ML applications.

Market Segmentation

• Data Type Insights (Revenue, USD Bn, 2020 - 2032)
• Image/Video
• Text
• Audio
• Vertical Insights (Revenue, USD Bn, 2020 - 2032)
• IT & Telecom
• Automotive
• Healthcare
• BFSI (Banking, Financial Services, and Insurance)
• Retail & E-commerce
• Regional Insights (Revenue, USD Bn, 2020 - 2032)
• North America
• U.S.
• Canada
• Latin America
• Brazil
• Argentina
• Mexico
• Rest of Latin America
• Europe
• Germany
• U.K.
• Spain
• France
• Italy
• Russia
• Rest of Europe
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• ASEAN
• Rest of Asia Pacific
• Middle East
• GCC Countries
• Israel
• Rest of Middle East
• Africa
• South Africa
• North Africa
• Central Africa
• Key Players Insights
• Reality AI
• Globalme Localization Inc.
• Global Technology Solutions
• Alegion
• Labelbox Inc.
• Scale AI Inc.
• Trilldata Technologies Pvt Ltd
• Appen Limited
• Playment Inc.
• Dobility Inc.
• CloudFactory
• Mighty AI (acquired by Uber)
• Samasource
• Cogito Tech LLC
• iMerit

Sources

Primary Research interviews

• Data Labeling Service Providers
• AI/ML Technology Companies
• Enterprise Data Scientists and AI Engineers
• Cloud Platform Providers
• Others

Databases

• Statista
• IBISWorld
• Bloomberg Terminal
• Factiva
• Others

Magazines

• AI Magazine
• Data Science Central Magazine
• MIT Technology Review
• Analytics India Magazine
• Others

Journals

• Journal of Artificial Intelligence Research
• IEEE Transactions on Pattern Analysis and Machine Intelligence
• Machine Learning Journal
• Others

Newspapers

• The Wall Street Journal
• Financial Times
• Reuters
• TechCrunch
• Others

Associations

• Association for the Advancement of Artificial Intelligence (AAAI)
• Partnership on AI
• AI Industry Alliance
• International Association for Machine Learning (IAML)
• Others

Public Domain sources

• U.S. Bureau of Labor Statistics
• European Commission Digital Strategy Reports
• World Bank Digital Development Reports
• OECD AI Policy Observatory
• Others

Proprietary Elements

• CMI Data Analytics Tool, Proprietary CMI Existing Repository of information for last 8 years