Top 22 Data Labeling Solution and Services Companies in Global [Updated] | Global Growth Insights

Data labeling refers to the process of identifying raw data—images, videos, text files, etc.—and adding one or more meaningful labels to provide context. These labels help machine learning (ML) models make accurate predictions and decisions. Data labeling solutions and services are essential in industries like autonomous driving, finance, e-commerce, medical imaging, and voice recognition, as they enable supervised learning models to be trained effectively.

Labeling services can be performed manually, automatically, or via a hybrid approach using AI and human-in-the-loop (HITL) systems. These services form the backbone of most AI systems in use today.

Data Labeling Solution And Services Market size was valued at $0.03Bn in 2024 and is projected to touch $0.04Bn in 2025, ultimately reaching $0.16Bn by 2033. This growth reflects a compound annual growth rate of 23.06% during the forecast period from 2025 to 2033

Global Data Labeling Solution and Services Market Size in 2025

By 2025, the global market for data labeling solutions and services is set to witness significant growth. This is fueled by the sharp rise in demand for labeled training data to improve the accuracy of AI and ML models. Around 61% of all AI projects globally now require externally labeled data for model training.

Major sectors contributing to this demand include automotive (with 28% of labeling tasks), healthcare (18%), retail and e-commerce (22%), and financial services (15%). AI-based content moderation alone accounted for 9% of all video and image labeling tasks in 2024. Increased investment in autonomous systems, robotics, and conversational AI is expected to accelerate service adoption further.

USA: Growing Data Labeling Solution and Services Market

The U.S. is the largest contributor to the global data labeling market, holding a commanding over 40% market share. Enterprises in the U.S. are investing heavily in AI R&D, with 69% of AI startups outsourcing labeling tasks. Over 52% of computer vision applications in the U.S. use third-party labeling services for training datasets.

Healthcare, autonomous driving, and defense are the top industry verticals leveraging U.S.-based service providers. Federal agencies and military initiatives are also pushing demand for confidential and secure labeling workflows, opening up opportunities for HITL and encrypted labeling systems. In addition, government compliance requirements, such as those under the AI Bill of Rights, have led to a 37% rise in demand for auditable and explainable AI training data.

Regional Market Share & Opportunities

• North America (44%): Largest regional share. High AI investment, skilled workforce, and robust infrastructure make this the primary hub for AI labeling outsourcing and in-house platforms.
• Asia Pacific (31%): India, China, and South Korea are emerging leaders in offshore data labeling due to cost-efficiency and scaling capacity. Growth sectors include mobility, fintech, and smart city initiatives.
• Europe (17%): The EU’s GDPR compliance and focus on ethical AI drive the demand for secure labeling services. Key markets include Germany, France, and the Nordics.
• Latin America (5%): Brazil and Mexico are exploring AI for fintech and e-commerce use cases, creating a modest but growing need for local language labeling.
• Middle East & Africa (3%): Emerging market with growing interest in AI for surveillance, public infrastructure, and healthcare digitization.

Global Growth Insights unveils the top List Global Data Labeling Solution and Services Companies:

Company	Headquarters	Past Year Revenue	CAGR (2024)
Lotus Quality Assurance	Hanoi, Vietnam	USD 3.8 Million	7.2%
Mighty AI, Inc.	Seattle, USA	USD 6.2 Million	5.6%
Steldia Services Ltd.	Nicosia, Cyprus	USD 2.5 Million	4.1%
Trilldata Technologies Pvt Ltd	Bengaluru, India	USD 3.1 Million	6.7%
Heex Technologies	Paris, France	USD 2.9 Million	5.3%
Crowdworks, Inc.	Seoul, South Korea	USD 8.6 Million	6.5%
Playment Inc.	Bengaluru, India	USD 9.1 Million	7.4%
Yandez LLC	Moscow, Russia	USD 5.7 Million	4.3%
Labelbox, Inc.	San Francisco, USA	USD 22.4 Million	8.9%
Scale AI	San Francisco, USA	USD 50.1 Million	11.2%
Amazon Mechanical Turk, Inc.	Seattle, USA	USD 12.3 Million	6.0%
Appen Limited	Sydney, Australia	USD 82.5 Million	3.7%
Tagtog Sp. z o.o.	Warsaw, Poland	USD 1.8 Million	3.9%
CloudApp	Lehi, Utah, USA	USD 4.6 Million	5.1%
Explosion AI GmbH	Berlin, Germany	USD 3.2 Million	4.6%
Cogito Tech LLC	New York, USA	USD 16.9 Million	6.8%
Deep Systems, LLC	Kyiv, Ukraine	USD 2.7 Million	5.5%
edgecase.ai	Austin, USA	USD 5.9 Million	6.2%
Clickworker GmbH	Essen, Germany	USD 11.5 Million	5.4%
Shaip	Louisville, USA	USD 10.4 Million	5.9%
Alegion	Austin, USA	USD 7.3 Million	4.7%
CloudFactory Limited	Reading, UK	USD 18.6 Million	6.1%

Company Profile: Scale AI

Scale AI has emerged as a leading data labeling platform for enterprise-grade AI models. The company processed over 1.2 billion annotations in 2024, with autonomous vehicle projects contributing 38% of the total volume. Its government contracts for defense and geospatial AI labeling expanded significantly, including partnerships with U.S. federal agencies.

Its Document AI and Reinforcement Learning with Human Feedback (RLHF) solutions gained momentum, contributing to a 22% increase in demand from the finance sector. Scale AI serves clients across 5 continents, with 62% of its total revenue generated in North America, followed by strong adoption in the UK and Japan.

Company Profile: Appen Limited

Appen Limited is one of the oldest players in the human-annotated data domain, with deep roots in linguistics, speech, and NLP labeling. In 2024, Appen supported over 235 languages, enabling cross-border AI deployments. The company handled over 350 enterprise clients, including major tech platforms, through its managed crowdsourcing model.

Appen’s customer base includes 65% U.S.-based firms, with increasing contracts from telecoms and e-learning sectors in Europe. With investments in automation, Appen’s hybrid labeling solutions (automated + HITL) saw a 17% rise in project speed efficiency.

Company Profile: Labelbox, Inc.

Labelbox provides an ML-focused labeling infrastructure platform allowing enterprises to manage their data pipeline end-to-end. In 2024, more than 70% of users integrated the platform with cloud-native tools like AWS SageMaker, GCP Vertex, and Azure ML.

The company saw a 46% increase in labeled 3D point cloud data, driven by automotive, robotics, and drone-based clients. Labelbox expanded its operations in Europe through dedicated data residency support. Over 55% of its revenue originates from North America, and the rest is spread across Europe and the APAC region.

Company Profile: CloudFactory Limited

CloudFactory uses a managed workforce model to provide scalable data labeling with ethical sourcing. The company operates labeling hubs in Nepal, Kenya, and the Philippines, offering low-latency turnaround for global AI projects.

In 2024, it executed over 700 AI labeling projects, with healthcare AI making up 25% of its use cases. CloudFactory's enterprise platform saw an 18% increase in API-based integrations with third-party AI model training environments. North America and Europe contribute nearly 78% of total revenue.

Company Profile: Cogito Tech LLC

Cogito provides human-in-the-loop data annotation for sentiment analysis, insurance automation, healthcare AI, and facial recognition. It handled over 900 million data tags in 2024, including image, audio, and video labeling.

Cogito’s strengths lie in multilingual data labeling, with projects covering more than 40 languages. Over 80% of its clients are U.S.-based, particularly in the BFSI and customer support automation sectors. In the past year, it also reported a 35% increase in medical imaging labeling contracts from clients in Europe and the Middle East.

Company Profile: Clickworker GmbH

Clickworker offers crowd-based data annotation services, including text, image, video, and categorization tasks. With a network of 4.5 million registered crowdworkers, it processed over 500 million annotations for clients in e-commerce, travel, and publishing in 2024.

The company noted strong growth in sentiment tagging and intent classification, particularly for German and French language models. Around 61% of its revenue comes from Europe, with U.S. and APAC making up the remainder.

Company Profile: Amazon Mechanical Turk (MTurk)

Amazon MTurk is widely used for microtask-based labeling projects across industries. It supported over 2 million tasks daily in 2024, mainly for AI researchers and developers. Common use cases include image classification, sentiment tagging, and entity recognition.

With 74% of active requesters located in the U.S., MTurk continues to be the go-to platform for small-scale or experimental data labeling tasks. It’s also used by academic institutions and startups aiming to build quick MVPs.

Company Profile: Shaip

Shaip specializes in AI training data across healthcare, finance, and legal domains. In 2024, the company processed over 180 million medical image and speech annotations, helping clients develop HIPAA-compliant AI models. Its secure platform saw a 29% growth in conversational AI labeling for voice assistants and chatbots.

Approximately 68% of its revenue originates from the U.S., followed by clients in the UK and the Middle East. Shaip’s real-world audio dataset solutions for healthcare AI enabled 24% of U.S. telemedicine platforms to improve diagnostic accuracy.

Company Profile: Alegion

Alegion provides enterprise-level video and image annotation solutions, especially for autonomous systems. In 2024, it facilitated over 1.5 billion labeled frames, largely for self-driving vehicles, drones, and industrial robotics. It also supports object tracking and segmentation at scale.

The company saw a 33% increase in frame-based annotations, driven by aerospace and defense sectors. North America accounts for 81% of Alegion’s revenue, with emerging partnerships in Japan and Germany.

Company Profile: CloudApp

CloudApp offers a visual communication platform that integrates data capture and real-time annotation. In 2024, it was used by more than 70% of remote-first startups for product support and content labeling tasks. CloudApp saw a 22% growth in annotated visual data usage, especially for product training and UI/UX optimization.

The company’s clients are mostly located in North America (over 85% of total revenue), while it is expanding into the UK and Australia with enterprise-level product tours and AI-powered support documentation.

Company Profile: Playment Inc.

Playment, acquired by Telus International, focuses on 3D point cloud annotation, semantic segmentation, and video labeling for autonomous technologies. In 2024, Playment managed over 600 million 3D annotations, with clients across automotive and robotics.

India remains its key operational hub, delivering cost-efficient, scalable annotation solutions to clients in the U.S., Japan, and Germany. Around 70% of its client revenue still originates from the U.S. and Canada.

Company Profile: Trilldata Technologies Pvt Ltd

Trilldata provides text and audio labeling services for sentiment analysis, voice bots, and NLP model training. It processed over 100 million labeled utterances in 2024, spanning regional Indian languages, Arabic, and Spanish.

The company saw a 44% increase in demand for annotated conversational datasets, particularly for retail and BFSI use cases. India accounts for its operations, while clients are mostly from the U.S. and Europe (78% export share).

Company Profile: Heex Technologies

Heex Technologies offers smart data labeling tools for ADAS and autonomous vehicles. Its proprietary "Smart Data Streaming" allows teams to label only relevant scenarios. In 2024, Heex processed over 450,000 smart driving sequences, leading to 35% annotation time savings for clients.

Its clientele includes mobility firms across France, Germany, and the U.S. Nearly 60% of its revenue came from the European market, where GDPR-aligned labeling is a growing requirement.

Company Profile: Deep Systems, LLC

Based in Ukraine, Deep Systems focuses on NLP and image annotation for research and commercial models. Despite geopolitical disruptions, the company maintained continuity and processed over 15 million data points in 2024.

With clients in the EU (47%) and U.S. (41%), Deep Systems specializes in low-cost, high-precision annotation for academic institutions and mid-tier tech developers.

Company Profile: Lotus Quality Assurance

Lotus Quality Assurance is one of Vietnam's emerging data labeling providers, offering text, audio, and image annotation services. In 2024, it supported over 50 AI startups across Southeast Asia, contributing to a 41% rise in regional labeling projects.

The company focuses on affordability and linguistic expertise in Vietnamese, Thai, and Khmer datasets. Around 75% of its clients are international, with strong demand from the U.S., Japan, and South Korea.

Company Profile: Mighty AI, Inc.

Before its acquisition by Uber ATG, Mighty AI specialized in image and video annotation for autonomous vehicles. Though its branding has since transitioned, its core capabilities remain active within Uber’s mobility AI labs.

In 2024, the team handled over 120 million street-level bounding box annotations. North America represented over 90% of the client base, with continued research collaboration in San Francisco and Pittsburgh.

Company Profile: Steldia Services Ltd.

Steldia is a Cyprus-based data labeling firm known for its work in content moderation and e-commerce. In 2024, it provided annotation services to over 75 fashion and consumer brands, processing over 8 million tagged SKUs for visual search engines.

The company supports multilingual labeling in Greek, Russian, and Arabic. About 60% of its revenue originates from European Union countries, while the rest comes from boutique retailers in the Middle East and North Africa.

Company Profile: Crowdworks, Inc.

Crowdworks is a South Korean company offering NLP, image, and document labeling with a distributed workforce model. In 2024, it reported a 32% increase in labeled Korean-language datasets, supporting voice assistants, banking chatbots, and AI tutors.

Crowdworks operates with over 300,000 crowd contributors, and more than 80% of its clients are based in South Korea and Japan, with emerging interest from U.S. education tech platforms.

Company Profile: Explosion AI GmbH

Based in Berlin, Explosion AI is the developer of spaCy, a widely used open-source NLP library. It offers annotation tools through Prodigy, enabling researchers and developers in 65+ countries to label and train custom models efficiently.

In 2024, Prodigy processed over 20 million annotations, largely across academic institutions and research labs. Around 52% of clients are based in Europe, with North America accounting for 35% of sales.

Company Profile: Yandez LLC

Yandez (not to be confused with Yandex) operates in data labeling for Russian and Slavic languages. It supported over 12 major linguistic AI projects in 2024, focusing on regional compliance and dialectical text annotation.

The company processed over 7 million language pairs, helping improve translation and chatbots across Central and Eastern Europe. Russia and CIS countries make up 87% of its client base, with exploratory pilots in Germany and Israel.

Company Profile: Tagtog Sp. z o.o.

Tagtog is a Poland-based text annotation tool for biomedical and legal datasets. In 2024, over 200 institutions used Tagtog for entity tagging, contract review, and academic corpus creation.

It offers both cloud and on-premise solutions, aligning with EU data regulations. Nearly 70% of Tagtog’s revenue comes from European universities, pharma companies, and law firms.

Regional Insights & Opportunities in Data Labeling Solution and Services

1. North America (44% Market Share)

North America continues to lead the global data labeling market, driven by large-scale AI adoption, enterprise AI investments, and advanced infrastructure.

• Over 71% of U.S.-based tech companies outsource or in-house label data for AI development.
• 45% of labeled datasets globally originate from U.S. and Canadian projects.
• The U.S. defense sector accounts for 12% of total North American labeling volume, including image intelligence and drone data.
• 38% of enterprise AI teams in North America prefer hybrid labeling platforms (human + AI-assisted).
• Healthcare, autonomous driving, and financial services are the top three verticals consuming 74% of labeled data demand in this region.

Opportunity Highlight: Growth in autonomous systems, government contracts (DoD, DHS), and healthcare diagnostics will expand need for privacy-compliant, real-time annotation workflows.

2. Asia Pacific (31% Market Share)

Asia Pacific is the fastest-growing region for labeling services, primarily due to its cost advantages, large workforce, and AI innovation hubs in India, China, and South Korea.

• Over 58% of global outsourcing for labeling tasks goes to India, Philippines, and Vietnam.
• India alone handles 36% of the world’s image and video labeling tasks for computer vision.
• South Korea leads in local language NLP tasks, accounting for 11% of APAC’s labeling activity.
• In China, 62% of AI companies use in-house labeling teams, driven by data protection regulations.
• AI in retail, automotive, and education drives over 70% of the regional demand.

Opportunity Highlight: Rise in local language AI models, robotics, and smart city infrastructure are driving multi-domain labeling needs.

3. Europe (17% Market Share)

Europe is a compliance-first market focused on GDPR and ethical AI, driving demand for secure, explainable labeling platforms and on-premise solutions.

• 42% of European enterprises require GDPR-compliant annotation workflows.
• Germany, France, and the UK together account for 79% of Europe’s total labeling demand.
• Use of AI in legaltech and healthcare drives 28% of project volume.
• More than 55% of European research institutions use open-source or licensed annotation tools.
• Language-specific needs have led to a 24% rise in demand for multilingual text labeling.

Opportunity Highlight: Significant potential lies in legal, pharmaceutical, and public-sector labeling services across EU nations with strict privacy regulations.

4. Latin America (5% Market Share)

Latin America is in the early adoption phase but shows growing demand for labeled data in fintech, e-commerce, and logistics sectors.

• Brazil and Mexico represent 74% of the regional demand for data labeling.
• Over 60% of Latin American AI initiatives involve computer vision for e-commerce product tagging.
• Mobile-first banking apps drive a 31% increase in audio/text NLP annotations.
• 22% of startups in the region now use labeling platforms for product recommendation models.

Opportunity Highlight: Bilingual labeling services (Spanish/Portuguese) for finance, logistics, and regional NLP models show strong upward momentum.

5. Middle East & Africa (3% Market Share)

MEA is an emerging market for data labeling, largely government- and enterprise-led, with a focus on smart cities, surveillance, and healthcare digitization.

• UAE, Saudi Arabia, and South Africa account for over 80% of the region’s demand.
• AI surveillance and security applications make up 39% of labeling activities.
• 26% of healthcare facilities in Gulf countries now use AI-based diagnostics requiring labeled medical data.
• Arabic language labeling demand grew by 34% year-over-year.

Opportunity Highlight: Growth in Arabic NLP, AI-based healthcare, and defense applications will increase the need for region-specific, privacy-respecting labeling capabilities.

 Summary Table: Regional Market Share (2025)

Region	Market Share	Key Industries	Major Opportunity
North America	44%	Defense, Healthcare, Finance	Secure & real-time labeling (HITL + cloud)
Asia Pacific	31%	Retail, Robotics, Education	Language AI, smart mobility, outsourcing scale
Europe	17%	Legal, Pharma, Public Sector	GDPR-safe, on-premise, multilingual platforms
Latin America	5%	Fintech, Logistics, E-Commerce	Localized NLP and visual tagging
Middle East & Africa	3%	Surveillance, Healthcare, Smart City	Arabic NLP and AI diagnostics labeling

Conclusion: Outlook for Data Labeling Solution and Services Companies in 2025

The global data labeling solution and services market in 2025 is a cornerstone of AI development, empowering models across industries with clean, structured, and annotated datasets. As enterprises accelerate AI integration, the demand for accurate, domain-specific labeled data has surged dramatically.

• Over 61% of global AI deployments depend on externally labeled or partially labeled datasets.
• Manual labeling is now supplemented in 47% of enterprise projects with AI-assisted automation tools, increasing throughput and reducing error rates.
• Ethical data sourcing has become critical, with 39% of enterprises requiring traceable and audit-friendly labeling workflows.
• 32% of companies in regulated industries (e.g., healthcare, finance, legal) now mandate compliance-ready labeling platforms that can meet data privacy and localization mandates.

As AI use cases diversify—from self-driving cars to legal document processing—companies offering data labeling services are evolving from commodity service providers to strategic AI partners. Firms that provide platform flexibility, quality assurance frameworks, and multilingual support are seeing a clear competitive edge.

Strategic Opportunities for 2025 and Beyond

1. Specialization in High-Value Sectors

• Medical imaging, autonomous mobility, and legal AI present high-margin opportunities.
• 28% of future labeling contracts are expected to come from these sectors, driven by demand for precision and accountability.

2. Shift Toward Platform + Services Models

• Companies that offer annotation tools plus trained labor or managed workflows are securing longer-term enterprise contracts.
• Hybrid models that allow in-house teams to collaborate with external annotators will be critical.

3. Geopolitical and Data Localization Factors

• Over 43% of multinational companies now require regionally compliant labeling centers.
• Firms with distributed operations in the U.S., EU, and APAC will benefit from jurisdictional flexibility and faster procurement cycles.

4. Growing Role of HITL and Explainability

• Human-in-the-loop (HITL) labeling remains vital for sensitive tasks such as biometric ID, hate speech detection, and clinical diagnostics.
• AI explainability and fairness auditing will demand annotated datasets that reflect diversity in language, tone, and context.

Final Takeaway

In 2025, data labeling is no longer just a preparatory step in AI—it is a critical enabler of trustworthy, compliant, and scalable artificial intelligence. The companies leading this market are those that combine scalability, domain expertise, privacy readiness, and platform adaptability.

Global competition is rising, but so is global demand. U.S.-based tech giants, European compliance-driven firms, and Asia’s scalable annotation hubs are shaping the next frontier of AI readiness. Data labeling service providers are now indispensable to every stage of the AI lifecycle—from ideation to deployment.