Quick Summary
- Data labeling adds context to raw images, text and video so machine learning models can learn from them.
- The strongest providers now manage the full data lifecycle instead of offering annotation software alone.
- Labelbox ranks first and Scale AI ranks second, with Encord, SuperAnnotate and V7 completing the top five.
- CVAT and Label Studio give teams open-source ways to start labeling data.
- Human-in-the-loop workflows appear across many of the platforms on this list.
- Scale AI’s valuation has grown from about $14 billion in 2024 to more than $29 billion after Meta’s investment.
Data labeling tools help teams turn raw images, text files and videos into training material for machine learning models. Each label tells a model what a piece of data means. Without that context, a model has very little to learn from.
The leading providers now do more than supply software. They build data-centric AI infrastructure that manages the entire data lifecycle. This ranking covers ten companies and looks at their scale, usefulness and the technology behind frontier AI.
How Data Labeling Tools Teach AI to Understand Data
Data labeling means identifying raw data and attaching one or more labels that describe its context. Models use those labels to recognize what they are looking at. Modern platforms also handle curation, quality control and evaluation, which places them near the center of AI development.
Many tools now combine automation with human review. This approach is called human-in-the-loop labeling. Several companies on this list build their products around it.
10. CloudFactory: Managed Workforce Meets Flexible Software
CloudFactory pairs flexible labeling software with a managed human-in-the-loop workforce. Its work supports machine learning projects in computer vision, natural language processing and structured data. The modular platform offers customizable annotation workflows, ongoing quality control and integration with existing ML pipelines. The company reports more than 700 clients and also provides model monitoring across the development lifecycle.
9. Amazon Web Services SageMaker Ground Truth: Native Human Feedback Workflows
Amazon Web Services offers SageMaker Ground Truth, which it describes as a comprehensive set of model customization capabilities. The tool uses human feedback across the machine learning lifecycle. It runs natively inside an organization’s AWS environment and supports automated and human-in-the-loop workflows, from data preparation and labeling to testing, evaluation and model alignment. AWS says organizations can use it to turn generic AI models into customized solutions with unique business value.
8. CVAT: An Open-Source Toolkit for Visual Data
CVAT describes itself as a complete toolkit for scalable data annotation that turns raw visual data into datasets. The project began in 2017 as a tool to help computer vision engineers label training data. After an open-source release on GitHub in 2018 under the name Computer Vision Annotation Tool, it became an independent company in 2022. It can run in the CVAT cloud or on a firm’s own infrastructure, with an option to have the company do the work.
7. Label Studio: Open-Source Labeling for LLM Workflows
Label Studio is an open-source labeling and annotation tool owned by HumanSignal, which was formerly called Heartex. Teams use it for agentic traces, RLHF and fine-tuning, LLM evaluations, RAG and retrieval question answering. Its website says leading AI builders such as Meta, Cloudflare, NVIDIA, IBM and Intel trust it. Native pipelines through an API, a Python SDK and webhooks let developers create projects, stream predictions and trigger training, active learning and evaluation workflows in real time.
6. Dataloop: Managing Unstructured Data
Dataloop helps developers explore and analyze large volumes of unstructured data from many sources. It relies on automated preprocessing and embeddings to find similar items and the specific data a team needs. The platform curates, versions, cleans and routes images, video, audio and text to wherever AI and computer vision models require them. Its listed clients include Google, IBM, Getty Images and NVIDIA.
5. V7: Visual Training Data and Workflow Automation
V7 provides enterprise platforms for managing visual training data, building custom AI models and automating document-heavy workflows with AI agents. Its original product, V7 Darwin, is a labeling and annotation platform that serves customers such as Mars, Bayer, Merck, Genentech and Insitro. Darwin supports RLHF, RLAIF, model-in-the-loop workflows and webhook integration. In 2024 the company released V7 Go, a work automation platform that uses foundation models to learn repetitive tasks.
4. SuperAnnotate: High-Quality Training Data at Scale
SuperAnnotate helps AI and machine learning teams build, fine-tune and scale models by managing high-quality training data. It offers tools for annotation, curation, automation and quality assurance. The company says Databricks and ServiceNow trust it and that NVIDIA, Dell Technologies Capital and Databricks Ventures back it. CEO Vahan Petrosyan says creating high-quality AI data is complex and time-consuming but essential for training, improving and evaluating agents, models and enterprise AI systems.
3. Encord: A Data Layer for Physical AI
Encord describes itself as a data layer for physical AI that turns messy multimodal data into production systems. Those systems include humanoid robots, autonomous vehicles and smart infrastructure. The company says it supports more than 300 teams, including Toyota, Skydio and Maxar. Its offerings include annotation, post-training alignment, data indexing, curation and data agents, along with annotation, data collection and physical AI data services.
2. Scale AI: Data Infrastructure for Frontier Models
Scale AI provides the training data, evaluation frameworks and software tools needed to build, fine-tune and deploy frontier AI models. The company says 90% of the world’s leading generative AI model builders are powered by Scale. Its Scale Data Engine combines expert human-in-the-loop feedback, RLHF, automated red-teaming and model evaluation to turn enterprise data into production-ready intelligence. Other products include the Scale GenAI Platform and Scale Donovan, which offers specialized AI agents for mission-critical workflows.
Scale AI serves enterprise, insurance and global public sector customers. It was valued at about $14 billion in a 2024 funding round. Meta later invested $14.3 billion, which valued the company at more than $29 billion.
1. Labelbox: Built for the Frontier of AI
Labelbox says it partners with more than 90% of leading AI labs in the US and offers everything from environments to custom evaluations. The company positions itself as building for the frontier of AI. Its Horizon product is a reinforcement learning environment with signals tuned for post-training and evaluations in reasoning, tool use and computer use. Terra provides full-stack data projects for robotics foundation models and multimodal annotations, collected with purpose-built hardware and AI-powered diversity engines.
Alignerr supplies real-world grounding signals from expert people that frontier models cannot generate on their own. Hundreds of teams work with Labelbox, including Etsy, Pinterest, Warner Bros, Ancestry, Walmart and Shutterstock. Labelbox also produced the dataset behind Meta’s research paper introducing GIM, a benchmark for evaluating frontier AI reasoning.
Final Thoughts
These ten platforms show how far data labeling has moved beyond simple annotation. Providers now cover curation, evaluation, alignment and quality control across the full data lifecycle. Open-source options such as CVAT and Label Studio sit alongside enterprise leaders such as Labelbox and Scale AI.
Teams comparing options should check current features and pricing on each vendor’s website. Most claims in this list come from the companies themselves. Independent testing is a sensible step before any commitment.
Discover how AI is reshaping technology, business, and healthcare—without the hype.
Visit InfluenceOfAI.com for easy-to-understand insights, expert analysis, and real-world applications of artificial intelligence. From the latest tools to emerging trends, we help you navigate the AI landscape with clarity and confidence
