Top 10: Data Labelling Tools
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Published Time: 2025-09-17T09:00:04Z
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Data & Analytics
Top 10: Data Labelling Tools
September 17, 2025
9 mins
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Data labelling is a largely underappreciated aspect of modern AI infrastructure, but without it AI as we know it could not exist
Data labelling is the backbone of all modern AI models and this week we shine a light on some of the most important tools on the market such as V7 & Appen
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Top 10AITechnologyDataData Labelling ToolsMetadataProvenanceAutomationAnalytics
High quality, meticulously labelled data is one most critical and often underestimated components of the AI revolution.
Far from a mundane back-office task, data labelling is the foundational process that imbues AI models with their capacity for perception, reasoning and interaction.
It is the digital equivalent of teaching a child to recognise a cat by pointing to one and saying its name.
The principle of “garbage in, garbage out” remains an immutable law of machine learning (ML); even the most sophisticated algorithms are rendered useless by poorly prepared data.
Reflecting its criticality, the global data labelling market is undergoing explosive growth, projected to expand from US$4.87bn in 2025 to more than US$29bn by 2032, with some forecasts predicting a market size of US$119bn by 2034.
This sector has evolved from manual annotation to sophisticated, AI-assisted platforms that are indispensable for developing autonomous vehicles, advancing medical diagnostics and personalising retail experiences.
The leading companies are no longer just providing tools, but are building the critical data-centric AI infrastructure that manages the entire data lifecycle, becoming deeply embedded in the world's most advanced AI development pipelines.
- Roboflow
Company founded: 2019
Based in: Des Moines, Iowa, USA
CEO: Joseph Nelson
Notable feature: Roboflow Universe, a massive open-source repository of over 1,000,000 datasets and 250,000 pre-trained models.
Roboflow's data labelling tools work perfectly in automated manufacturing lines | Credit: Roboflow
Roboflow has carved out a strong niche for itself by focusing relentlessly on the developer experience, providing an end-to-end workflow for computer vision that covers everything from annotation and augmentation to one-click model training and deployment.
Its key differentiator is Roboflow Universe, its own vast public library of datasets and pre-trained models that helps to democratise access to high-quality training materials.
This strategy has helped the firm to cultivate a strong community and a powerful network of users, empowering individual developers and small teams to build and deploy production-ready vision applications with remarkable speed.
- iMerit
Company founded: 2012
Based in: San Jose, California, USA
CEO: Radha Basu
Notable feature: An expert-in-the-loop workforce for complex, domain-specific annotation in sectors like medical AI and autonomous mobility.
iMerit prides itself on its "service first" approach to data labelling services | Credit: iMerit
iMerit distinguishes itself through its “service-first” approach, combining its Ango Hub platform with a highly skilled, in-house workforce trained for complex tasks.
This model is tailored for industries where deep domain expertise is non-negotiable, such as annotating medical DICOM imagery for diagnostic AI or labelling complex LiDAR sensor fusion data for autonomous vehicles.
Rather than competing on pure software, iMerit delivers high-quality, reliable data as a complete solution, managing the entire pipeline for clients with mission-critical AI applications.
- Sama
Company founded: 2008
Based in: San Francisco, California, USA
CEO: Wendy Gonzalez
Notable feature: A mission-driven, ethical AI approach, being the first AI data labelling company to be a certified B Corporation.
Sama can be used to great effect in supermarkets and retail | Credit: Sama
Sama has built its brand on the foundation of ethical AI, combining a high-performance data annotation platform with a commitment to social impact through its “impact sourcing” model.
The platform itself is robust, offering a suite of services to manage the full data lifecycle and guaranteeing an impressive 99% first-batch acceptance rate.
By providing a fully vetted, in-house workforce, Sama appeals to businesses that not only need quality and security, but also a socially responsible and ethical supply chain.
- Dataloop
Company founded: 2017
Based in: Herzliya, Israel
CEO: Avi Yashar
Notable feature: A comprehensive, end-to-end AI development platform with a focus on MLOps and automating the entire data-to-model pipeline.
Dataloop offers users a great deal of freedom when it comes to customisation | Credit: Dataloop
Dataloop positions itself not merely as a labelling tool but as a complete AI development platform, designed to manage the entire project lifecycle from data management to production deployment.
Its key strengths lie in its data-agnostic approach, supporting a wide range of unstructured data and its powerful orchestration layer that allows teams to build custom AI pipelines using either a drag-and-drop interface or a Python SDK.
This focus on automation and MLOps makes it a strong choice for enterprises looking to streamline and scale their AI operations efficiently.
- V7
Company founded: 2018
Based in: London, UK
CEO: Alberto Rizzoli
Notable feature: The Darwin platform, which heavily integrates AI-assisted labelling, including model-in-the-loop and automated annotation features like Auto-Annotate.
V7 has received huge investments for its data labelling models | Credit: V7
V7’s Darwin platform is engineered for speed and accuracy, with a strong emphasis on AI-assisted and automated labelling to accelerate the creation of ground truth data.
It supports a wide array of data types, including complex medical formats like DICOM and NIfTI, making it highly versatile for specialised applications.
Features such as automated object tracking in video and customisable, multi-stage review workflows empower ML teams to build sophisticated and reliable AI models faster, moving beyond manual annotation to a more intelligent, human-in-the-loop system.
- Encord
Company founded: 2020
Based in: London, UK
CEO: Eric Landau
Notable feature: An active learning platform designed for complex, multimodal data, with specialised capabilities for regulated industries like healthcare.
Encord has rapidly emerged as a leading platform for teams working with high-stakes, complex data.
Its key differentiator is an integrated active learning engine, which helps teams intelligently identify and prioritise the most valuable data to label, thereby improving model performance while optimising costs.
With robust support for multimodal data, including specialised DICOM and SAR imagery, as well as a strong focus on regulatory compliance (HIPAA, SOC2), Encord is the go-to solution for advanced applications in medical AI, autonomous vehicles and geospatial intelligence.
- SuperAnnotate
Company founded: 2018
Based in: Sunnyvale, California, USA
CEO: Vahan Petrosyan
Notable feature: An enterprise-grade, end-to-end platform with advanced workflow customisation, data management and integrated quality control systems.
SuperAnnotate provides a very versatile service which excels in image, video, text and 3D data labelling | Credit: SuperAnnotate
SuperAnnotate provides one of the most comprehensive enterprise platforms on the market, covering the entire ML pipeline from annotation to MLOps.
It excels in handling high-volume, high-complexity projects across multiple data types, including image, video, text and 3D data.
Its biggest strengths lie in its highly customisable workflows, robust quality assurance tools and deep enterprise security integrations (SOC2, ISO 27001, HIPAA), making it a preferred choice for large organisations that require a scalable, secure and unified platform to manage their diverse AI data operations.
- Appen
Company founded: 1996
Based in: Kirkland, Washington, USA (US HQ) / Chatswood, NSW, Australia (Corporate HQ)
CEO: Ryan Kolln
Notable feature: A massive, global crowd of over one million contributors, providing unparalleled scale and linguistic coverage in over 200 languages.
Appen has more experience than most data labelling services and has worked with huge tech companies like Nvidia and Microsoft to deliver customised solutions and products | Credit: Appen
With more than 25 years of experience in the field, Appen is a titan of the data annotation industry, built on the power of its vast and diverse global workforce.
The company excels at massively scalable, human-in-the-loop projects, making it the trusted partner for Fortune 500 companies and tech giants like Microsoft that have enormous, multilingual data needs.
While newer, platform-first competitors have emerged, Appen's unparalleled ability to source culturally and linguistically nuanced data at scale –bolstered by strategic acquisitions like Figure Eight –remains a formidable competitive advantage, particularly for training the next generation of global large language models.
- Labelbox
Company founded: 2018
Based in: San Francisco, California, USA
CEO: Manu Sharma
Notable feature: A leading data-centric AI platform for building a “data factory,” with advanced tools for model evaluation and Reinforcement Learning from Human Feedback (RLHF).
Labelbox represents the pinnacle of the modern, software-centric approach to AI data. It provides a sophisticated “data factory” platform that empowers teams to manage the entire data lifecycle, from annotation and curation to model evaluation and error analysis.
With strong backing from premier venture capital firms like Andreessen Horowitz and SoftBank, Labelbox has invested heavily in features for the Gen AI era, including advanced tooling for Reinforcement Learning from Human Feedback (RLHF) and multimodal model comparisons.
It is the platform of choice for AI-native companies that require granular control, powerful automation and deep integration into their MLOps pipelines.
- Scale AI
Company founded: 2016
Based in: San Francisco, California, USA
**CEO:**Jason Droege
Notable feature: A dominant hybrid model combining a powerful “Data Engine” platform with a vast managed workforce, serving high-stakes clients like OpenAI and the US Department of Defense.
Scale AI has been on such an impressive trajectory in recent years that Meta acquired a 49% stake in the company, while also securing the services of its CEO Alexandr Wang for its own AI ambitions | Credit: Scale AI
Scale AI began with Alexandr Wang, who became the youngest self-made billionaire at 24.
Now,Alexandr has moved to be head of Mark Zuckerberg’s “superintelligence team”with the ambition of creating smarter-than-human AI.
In his place, Jason Droege, the company’s Strategy Chief, will become CEO, according to CNBC.
Scale AI has achieved a remarkably dominant market position by masterfully combining a technologically advanced platform with the operational excellence of a managed workforce, creating a powerful hybrid solution.
Its Scale Data Engine provides the core infrastructure for managing the entire ML lifecycle, trusted by industry leaders from General Motors to OpenAI for everything from autonomous driving to fine-tuning generative models.
Bolstered by a staggering US$29bn valuation and strategic investments from tech giants like Meta, Amazon and Nvidia, Scale AI is quickly becoming a fundamental piece of infrastructure for the most ambitious and important AI projects in the world.
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