Data Labelling Market Size | Global Outlook 2025

Q: What is the size of data labelling market?

The size of data labelling market was projected at USD 2.5 billion in 2024 and is projected to be USD 7.2 billion by 2031.

Q: What are the emerging tools used in data labelling?

AI-assisted labelling tools, edge-based annotation, and active learning platforms are gaining traction.

Q: Which types of data are commonly labelled?

Image, video, audio, text, and sensor data are commonly labelled for use in vision, NLP, and speech recognition.

Q: Who are the primary users of data labelling solutions?

AI developers, research institutions, automotive OEMs, healthcare companies, and security firms are primary users.

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Code: MTA4470

Publication Date: Jun 2025

What is the size of Data Labelling Market?

According to 6Wresearch internal database and industry insights, the data labelling market was exhibited at USD 2.5 billion in 2024 and is estimated to be USD 7.2 billion by 2031, expanding at a CAGR of 16.6% during the forecast period 2025–2031.

The data labelling market is flourishing with the several factors including the surging demand for high-quality training data to support AI and machine learning models. Data labelling prepares high quality of annotated data to train models that ensures annotated and categorized datasets for supervised learning, computer vision, NLP, and autonomous systems.

Key Growth Factors of the Data Labelling Market

Growing adoption of AI/ML across sectors
Increase in image, video, and text datasets used for deep learning applications
Surge in demand for human-in-the-loop and automated labelling solutions
Rise in autonomous systems, including self-driving vehicles and robotics
Growing importance of data accuracy and bias mitigation in model training
Expansion of crowd-sourced and outsourced data annotation services

Data Labelling Market Trends

The data labelling market is undergoing for the transformation with the automation tools, synthetic data generation, and AI-assisted labelling platforms. Enterprises are investing in scalable annotation pipelines with version control and auditability. Adoption is rising in industries such as automotive, BFSI, agriculture, healthcare, and defence. The focus is shifting toward domain-specific datasets, multilingual labelling, and semi-supervised learning support.

Emerging Developments in the Data Labelling Market

Some recent developments observed in the market such as the integration of active learning, edge labelling, and real-time annotation tools. There is growing emphasis on quality assurance, annotation platform interoperability, and adaptive label taxonomies. Today, the vendors are submerged the collaboration features with the API access, and role-based access control to streamline enterprise deployment. Low-code interfaces and platform localization are also gaining momentum.