10 Best AI Data Labeling Tools in 2026

ChatGPT is an AI conversational assistant by OpenAI that can help with writing, coding, analysis, and general questions. Available as web app, mobile app, and API with multiple model options including GPT-4 and GPT-4o.

Label Studio is an open-source data labeling platform designed to help users prepare training data, fine-tune Large Language Models (LLMs), and evaluate AI models. It offers extensive flexibility with configurable layouts and templates that adapt to various datasets and workflows. The platform supports a wide range of data types including GenAI, images, audio, text, time series, and video, catering to diverse machine learning applications. Key features include ML-assisted labeling to accelerate the process, integration with cloud storage like S3 and GCP, and a robust Data Manager for exploring and organizing datasets. It's suitable for data scientists, machine learning engineers, and researchers who need to create high-quality labeled datasets for their AI projects. The platform also supports multiple projects and users, making it a versatile tool for teams. Label Studio provides comprehensive capabilities for LLM fine-tuning (supervised fine-tuning, RLHF), LLM evaluations (response moderation, grading, side-by-side comparison), and RAG evaluation (using Ragas scores and human feedback). It also covers computer vision tasks like image classification, object detection, and semantic segmentation; audio applications such as classification, speaker diarization, and transcription; and NLP tasks including classification, named entity recognition, and sentiment analysis.

Vertex AI is Google's unified platform for machine learning. Train models, deploy endpoints, use pre-trained APIs—ML infrastructure from a company with deep AI expertise. The MLOps capabilities are comprehensive. Pre-trained models are available. The Gemini models are accessible here. Organizations wanting enterprise ML platform choose Vertex AI for Google's ML infrastructure and expertise.

AWS SageMaker is Amazon's comprehensive machine learning platform that enables data scientists and developers to build, train, and deploy ML models at scale. The platform offers a complete set of tools for every step of the ML workflow, from data preparation to model monitoring in production. SageMaker Unified Studio (launched December 2024) provides an all-in-one AI and data analytics environment, integrating ML development with AWS analytics tools like EMR, Glue, and Redshift. SageMaker Canvas offers a no-code visual interface for business analysts to generate ML predictions without coding experience. The SageMaker Data Agent accelerates data querying and model development using AI-powered code generation. The platform supports various instance types from affordable ml.t3 instances for testing (~/bin/zsh.05/hour) to powerful ml.p4d.24xlarge for production training (.69/hour). Managed Spot Training can reduce training costs by up to 90%, while SageMaker Savings Plans offer up to 64% savings with usage commitments.

LandingAI offers a comprehensive Visual AI software platform designed to extract structured data from complex, real-world documents and images. It provides Agentic Document Extraction for turning documents into reliable, auditable data without training, and LandingLens for building and deploying computer vision models with accelerated MLOps. The platform is built for businesses across various industries, including financial services, healthcare, automotive, and manufacturing, seeking to drive efficiency and innovation by leveraging unstructured data. It democratizes AI implementation, allowing companies to scale solutions regardless of technical expertise, and offers integration with Snowflake for data governance and streamlined vision tasks. LandingAI aims to reduce deployment time significantly and boasts high reliability for production-grade deployments.

Cohere provides enterprise AI models and tools for natural language processing, including text generation, embeddings, and retrieval-augmented generation.

Anyscale is the Ray cloud platform for distributed computing and ML workloads. Scale Python applications from laptop to cluster with managed infrastructure.

Labelbox is a modern data factory designed for AI teams to build and scale their AI models. It provides the infrastructure and capabilities necessary for advanced AI development, including data for reinforcement learning, custom evaluations, and robotics data. The platform supports various complex AI tasks, such as multimodal data processing, long-horizon tasks, scientific coding, and industry workflows. The product offers specialized features like Knowledge Work Rubrics for expert-crafted scoring criteria across various domains, Tuned Environments for optimal reward gradients, and Private AGI Benchmarks for assessing frontier capabilities. It also provides tools for robotics data, including full-stack data collection, purpose-built hardware, and an AI-powered diversity engine. Labelbox is trusted by leading AI labs and companies of all sizes, fueling advancements in academic research and practical AI applications. Labelbox also provides access to Alignerr, an expert network of over 1 million knowledge workers across 40+ countries and 200+ domains, including PhDs and licensed professionals, to provide high-quality human intelligence for model training and evaluation. The platform allows users to take interactive product tours to learn how it accelerates data labeling projects and improves human supervision, with options for self-guided tours or live demos.

Roboflow provides a comprehensive platform for developers and enterprises to build and deploy computer vision applications. It offers an integrated workflow builder and deployment infrastructure that streamlines the entire process from data curation to production deployment. Users can explore, visualize, filter, and organize data, leverage AI-assisted annotation tools for collaborative labeling, and train models with optimized infrastructure. The platform is designed for machine learning engineers across various industries, including automotive, retail, healthcare, and manufacturing. It enables users to deploy models via hosted APIs or to edge devices, combining custom models, open-source models, LLM APIs, and pre-built logic. Roboflow also provides tools for model evaluation, performance monitoring, and integration with popular tools and frameworks like AWS S3, Google Cloud, TensorFlow, and PyTorch, accelerating the computer vision development roadmap.

Snorkel AI provides an AI data development platform and expert services to help organizations build, evaluate, and deploy high-performing AI models and agentic systems. It focuses on operationalizing the full AI data loop, from dataset curation and realistic simulations to rubric design and evaluations, ensuring AI models work effectively in real-world scenarios. The platform bridges the gap between raw enterprise data and production AI by connecting data streams and ML platforms to systems that run AI. It enables organizations to evaluate models with domain-specific test sets, develop data pipelines, fine-tuning flows, and RAG/agent evaluations, and deliver expert data with robust quality and governance. Snorkel AI is designed for frontier AI labs and enterprises looking to accelerate their AI development, improve model accuracy, and achieve measurable business outcomes.