Best Free AI Data Labeling Tools in 2026

High-quality training data for AI and ML models

4.1/5 across Trustpilot and G2 · 348 ratings, Aug 2026

Surge AI is a data labeling and human evaluation platform that provides high-quality training data for AI and machine learning models. The platform connects AI companies with a rigorously vetted workforce of annotators who handle tasks like RLHF (reinforcement learning from human feedback), text annotation, code review, image labeling, and model evaluation. Surge serves major AI labs including OpenAI, Google, Anthropic, Microsoft, and Meta, offering both a web interface and Python SDK for project management. The company also publishes research benchmarks like Hemingway-bench for evaluating AI writing quality and EnterpriseBench for testing AI agents.

The most robust annotation tool for building powerful AI with high-quality, trustworthy datasets.

4.7/5 across G2 and Capterra · 54 ratings, Sep 2026

Kili Technology is a comprehensive data platform designed to streamline the labeling, quality review, and iteration processes for AI/ML models. It enables organizations to build and scale high-quality training data up to 10x faster, supporting everything from small Proof of Concepts (POCs) to large-scale data production. The platform caters to a wide range of users, from data scientists in small teams to large enterprises coordinating subject matter experts, business stakeholders, and distributed annotators. The platform offers a complete toolset for annotating various data types, including geospatial imagery, OCR & document layout analysis, natural language processing, image annotation, video annotation, and LLM & RAG evaluation. It provides robust features for workflow configuration, real-time quality control, and seamless collaboration across distributed teams, ensuring consistent quality and efficient execution. Kili also emphasizes enterprise-grade security, offering deployment options on-premise, in the cloud, or via their secure infrastructure, with certifications like SOC2 Type II, ISO 27001, and HIPAA.

The industry-leading open data annotation platform for machine learning.

4.5/5 on G2 · 54 ratings, Oct 2026

CVAT is an open-source data annotation platform designed for machine learning applications, supporting images, videos, and 3D data. It provides a comprehensive suite of annotation tools, including bounding boxes, polygons, points, skeletons, cuboids, and trajectories, to accurately label datasets for computer vision models. The platform integrates AI-powered auto-annotation capabilities and algorithmic assistance, such as intelligent scissors and histogram equalization, to significantly speed up the annotation process. CVAT caters to solo labelers, small teams, and large enterprises, offering flexible deployment options including cloud-based online services and self-hosted enterprise solutions. It features robust data management with cloud storage integration (AWS S3, Google Cloud Storage, Azure Blob Storage), API access for workflow automation, and advanced quality control mechanisms like manual review, ground truth jobs, and honey pots. For enterprise users, CVAT provides enhanced security features like SSO, role-based access controls, and audit logs, along with dedicated support and customization options, making it suitable for organizations prioritizing security, compliance, and control over their data.

The data factory for AI teams building at the frontier, from reinforcement learning to custom evaluations.

4.5/5 on G2 · 48 ratings, Sep 2026

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.

Accelerate AI model development with high-quality, automated data labeling and annotation for various data types.

4.8/5 on G2 · 21 ratings, Sep 2026

Labellerr is a comprehensive data labeling and image annotation software designed to help AI teams prepare high-quality datasets efficiently. It leverages automated annotation, advanced analytics, and smart QA to process millions of images and thousands of hours of video in weeks, significantly reducing the time and cost associated with data preparation for AI models. The platform supports a wide range of data types including images, videos, PDFs, text, and audio, and integrates seamlessly with cloud services like AWS, GCP, and Azure. Labellerr offers features such as prompt-based labeling, model-assisted labeling, active learning, and robust project management with advanced analytics, ensuring 99% accurate labels and a 90% reduction in data preparation time. It also provides enterprise-grade security, MLOps integration, and flexible export formats to streamline the entire AI development pipeline.

Manage your entire AI lifecycle, from data to deployment

4.8/5 on G2 · 14 ratings, Jun 2026

DagsHub provides a comprehensive platform for managing the entire AI lifecycle, from data curation and annotation to experiment tracking and model deployment. It is designed for AI teams and individuals working with multimodal datasets, including vision, audio, and large language model (LLM) data. The platform enables users to transform raw data into high-quality datasets, ensuring better performance for AI models. Key functionalities include robust data versioning and lineage, an annotations workspace with AI-powered human-in-the-loop workflows, and interactive pipelines. It integrates seamlessly with existing ML stacks and open-source tools like MLflow, allowing for efficient experiment tracking, comparison of results, and model version management. DagsHub supports collaborative data science, making it easier for teams to share data and models, and offers deployment options ranging from cloud hosting to on-premise installations for high-scale enterprise workloads.

The most flexible open-source data labeling platform for AI models and LLM fine-tuning.

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.

Data layer for physical AI: visualize, query, transform, and train on multimodal robotics data.

Rerun is an open-source SDK and commercial platform designed as a unified data layer for physical AI, particularly robotics and multimodal sensor data. The open-source SDK provides tools for logging, storing, querying, visualizing, and training on multi-rate, multimodal data, with native APIs in Python, Rust, and C++. It includes an interactive viewer, a declarative visualization framework, SQL and dataframe query capabilities, and a PyTorch dataloader for direct streaming of recordings to GPUs. The columnar .rrd file format enables efficient storage and retrieval of temporal data. The commercial Rerun Hub extends the SDK with a managed catalog, byte-range indexing across object stores, and streaming dataset mixes for training. It provides single-tenant isolation, authentication/SSO, and deployment in the customer's preferred cloud region, all while keeping data in the customer's own S3-compatible buckets. Rerun is built with a focus on open source stewardship, contributing to projects like egui and wgpu.

Curate multimodal datasets and uncover model failures in one workflow

4.5/5 on G2 · 5 ratings, Oct 2026

Voxel51 Multimodal is a platform for building, managing, and improving AI datasets that span multiple data types, with a strong emphasis on visual data like images and video. It provides a unified environment where teams can curate raw data, annotate or transform labels, run models, and analyze predictions all within the same workflow. The platform is designed to help machine learning practitioners move from messy raw data to high-quality production datasets more efficiently. At its core, the platform offers tools for exploring large-scale datasets through custom queries, similarity search, and embedding-based visualizations. Users can compare model outputs across different experiments, identify failure modes, and pinpoint annotation issues before training. A Python SDK and API allow developers to script and automate dataset workflows, integrate with existing ML pipelines, and extend the platform with custom logic. The platform also emphasizes collaboration and reproducibility. Teams can share views, schemas, and evaluation results, making it easier to align around data quality and model performance. With an open-source core and cloud-based collaboration features, Voxel51 Multimodal supports both individual researchers and enterprise AI teams looking to bring structure to the multimodal data lifecycle.