High-quality training data for AI models
Scale Data Engine for data management and labeling
Scale GenAI Platform for full-stack Generative AI
Scale Donovan for AI-powered decision-making
AI model evaluation and red teaming
RLHF (Reinforcement Learning from Human Feedback)
Sponsored by
PoYo.AI
Sponsored by Skywork.
Best 16 Data Labeling Tools in 2026
People For AI, Innovatiana, Label Studio, BasicAI Cloud, Scale AI, Dioptra AI Redlining, LayerNext, CloudFactory Computer Vision Wiki, Surge AI, Unitlab are the best paid / free Data Labeling tools.
Skywork
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End
What is Data Labeling?
Data labeling is the process of identifying and assigning meaningful labels or tags to raw data, such as text, images, or videos. It is a crucial step in preparing data for machine learning and artificial intelligence applications, as labeled data is used to train and validate AI models. Data labeling helps machines understand and interpret data in a way that is useful for specific tasks, such as image classification, sentiment analysis, or object detection.
What is the top 10 AI tools for Data Labeling?
Core Features
|
Price
|
How to use
| |
|---|---|---|---|
Scale AI | Scale AI offers various products and services. You can explore their Scale Data Engine for data training, Scale GenAI Platform for generative AI, and Scale Donovan for AI-powered decision-making. You can also leverage their evaluation tools for AI models and applications. Contact them for a demo or to discuss your specific needs. | ||
Label Studio | Support for multiple data types (images, audio, text, video, time series) |
Community Edition Free to use
| Label Studio can be installed via PIP, Brew, Git, or Docker. After installation, you can launch the tool, import data, create projects, and start labeling using customizable tags and templates. |
Surge AI | Data labeling for GenAI | To use Surge AI, you can sign up on their website to access their data labeling platform. You can integrate their services directly with native APIs and SDKs or partner with their expert data team for a managed service. They offer tools and an elite workforce to build powerful datasets. | |
Innovatiana | Data Labeling for Computer Vision | To use Innovatiana's services, you can request a quote by discussing your project needs. They will then study your requirements, propose a customized solution, conduct a free test, and mobilize a team of data labelers to process your data. They offer flexible pricing based on the task and deliver the prepared data securely. | |
PromptLoop | AI-powered text transformation, extraction, and summarization in Google Sheets and Excel |
Free $0 /mo Explore on your own with two workflows, unlimited edits, access to features with rate limits and daily usage limits, PromptLoop Google Sheets™ and Microsoft Excel™ plugin, guides and templates, limited to one user.
| Users define the specific datapoints they need, then upload data (e.g., spreadsheets of websites, companies, or leads) or connect their CRM. PromptLoop then runs AI research flows on thousands of inputs at a time, leveraging prebuilt flows and templates to extract and format the desired information. The process is designed for quick setup, often taking less than 15 minutes, by simply dragging and dropping spreadsheets. |
BasicAI Cloud | AI-powered annotation tools | New users can access BasicAI Cloud for free with 50 seats, 100GB storage, and 1,000 model calls. Use the AI-powered annotation tools to label data, manage teamwork, and scale projects. | |
Lettria | GraphRAG for enterprise GenAI | Use Lettria to build ontologies from data, create GraphDBs from raw text, build private GPT chatbots, and leverage GraphRAG for enhanced knowledge retrieval. The platform offers no-code solutions for various NLP tasks and knowledge management. | |
Dioptra AI Redlining | Redlining | 1) Download Word Addin 2) Tell the assistant to compare, research, redline, draft, all in Microsoft word | |
Unitlab | AI-powered data annotation |
Free Free Unlimited Workspace, Unlimited Project, 3 Members, 5K Source Images, 1K Auto-Labeling / monthly
| Use Unitlab AI to automate data annotation with advanced auto-labeling tools. Integrate your own AI models, collect raw data, and streamline collaboration to deliver highly accurate labels with advanced QA tools. Manage projects, datasets, and teams within the platform. |
CloudFactory Computer Vision Wiki | Comprehensive glossary of Computer Vision terms and concepts | The Computer Vision Wiki can be used by navigating the table of contents to find specific topics, such as Computer Vision tasks, model architectures, metrics, loss functions, optimizers, augmentations, and deployment strategies. Each topic provides explanations, practical contexts, and code examples. It is recommended to start with the introductory CV lecture series by Joseph Redmon for beginners. |
Newest Data Labeling AI Websites
Data labeling platform for training generative AI models with human feedback and expert data teams.
Large Language Models (LLMs)
AI API
AI Developer Tools
AI Research Tool
Ethical data labeling outsourcing for AI models with a focus on quality and impact.
AI Models
Large Language Models (LLMs)
AI Image Recognition
AI Text Classifier
AI OCR
AI Document Extraction
Platform blending AI with collaborative creativity, offering easy access to various AI models.
AI Chatbot
AI Models
Prompt Engineering
AI Art Generator
Large Language Models (LLMs)
Data Labeling Core Features
Annotating data with relevant labels or tags
Categorizing data into predefined classes or categories
Identifying key features, objects, or entities within data
Assigning sentiment or intent to text data
Segmenting images or videos into distinct regions or objects
What is Data Labeling can do?
In healthcare, data labeling is used to annotate medical images, such as X-rays or MRIs, to train AI models for disease diagnosis and treatment planning.
In autonomous vehicles, data labeling is used to annotate video footage and sensor data to train AI models for object detection, lane tracking, and navigation.
In e-commerce, data labeling is used to tag product images and reviews to improve search relevance, recommendation systems, and personalization.
In customer service, data labeling is used to categorize and route customer inquiries and feedback based on topic, sentiment, or urgency.
Data Labeling Review
Data labeling platforms and services have received generally positive reviews from users, who praise their ease of use, flexibility, and ability to streamline the labeling process. However, some users have noted challenges in managing large-scale labeling projects, ensuring consistent quality across annotators, and dealing with complex or ambiguous data. Overall, data labeling is recognized as a critical but often time-consuming and resource-intensive task in AI development.
Who is suitable to use Data Labeling?
A user uploads a collection of product images and assigns relevant labels, such as 'electronics', 'clothing', or 'home goods', to each image for an e-commerce recommendation system.
A user tags social media posts with sentiment labels, such as 'positive', 'negative', or 'neutral', to train a sentiment analysis model.
A user annotates medical images with labels indicating the presence or absence of specific conditions or abnormalities.
How does Data Labeling work?
To implement data labeling, follow these steps: 1. Define the labeling scheme and guidelines based on the specific AI task and requirements. 2. Select a representative sample of data to be labeled. 3. Choose a data labeling tool or platform that suits your needs, such as Amazon SageMaker Ground Truth, LabelBox, or Supervisely. 4. Recruit and train human annotators to label the data accurately and consistently. 5. Perform quality control measures to ensure the accuracy and reliability of the labeled data. 6. Iterate and refine the labeling process as needed based on model performance and feedback.
Advantages of Data Labeling
Enables machines to understand and learn from raw data
Improves the accuracy and performance of AI models
Allows for the creation of high-quality training datasets
Facilitates the development of domain-specific AI applications
Saves time and effort in manual data processing and analysis
FAQ about Data Labeling
- What is data labeling?
- Why is data labeling important for AI?
- What are some common types of data labeling?
- How much data needs to be labeled for AI?
- Can data labeling be automated?
- What are some best practices for data labeling?
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