The 10 Best AI Data Labeling Services in 2027

1. Scale AI Data Labeling

Scale AI ranks first because it offers the most comprehensive enterprise-grade platform, covering text, image, video, audio, and 3D point clouds with a global workforce exceeding 100,000 annotators. Its 2027 Scale Auto-Validate feature uses foundation models to flag low-confidence annotations before human review, reducing rework by up to 40%. Custom SLAs guarantee 99% accuracy on most tasks, with turnaround times as fast as 24 hours for small batches.

Scale AI is built for large enterprises and regulated industries like autonomous driving and healthcare that need turnkey, high-volume labeling with human-in-the-loop validation. It trades away pricing transparency and self-serve flexibility, making it less suitable for small teams. Compared to Labelbox, Scale AI offers a fully managed workforce rather than a bring-your-own-annotator model, which is costlier but ensures consistent quality and faster scaling for massive projects.

2. Labelbox AI Data Labeling

Labelbox ranks second for its self-serve platform that lets teams manage their own annotators or use model-assisted labeling, reducing labeling time by up to 80% through its Model-Assisted Labeling feature. It supports image, video, text, and audio annotations with pre-built templates and integrates natively with AWS SageMaker, Google Vertex AI, Azure ML, and Hugging Face.

Labelbox is ideal for mid-size teams that need flexibility, speed, and integration into existing MLOps pipelines without the overhead of a managed workforce. It trades away the dedicated workforce and domain expertise of Scale AI, which sits above it, for lower per-label costs and faster prototyping. For teams that want to build and control their own labeling workforce, Labelbox offers a more customizable and cost-effective alternative than Scale AI's fully managed service.

3. Appen AI Data Labeling

Appen ranks third because it offers the broadest data type coverage, including geospatial imagery, lidar scans, and multilingual text in over 200 languages, backed by a crowdsourced workforce of over 1 million contributors. Its 2027 Appen Fusion quality engine combines human annotations with model predictions to produce consensus labels with confidence scores. The platform supports image annotation, video tracking, audio transcription, and text annotation, making it suitable for highly specialized tasks like medical terminology and legal jargon.

Appen is best for companies that need highly specialized annotations across diverse data types and have the budget for a managed service. It trades away the self-serve flexibility of Labelbox, which ranks above it, for a broader workforce and niche domain expertise. Compared to Scale AI, Appen offers a more crowdsourced approach, which can be cheaper but may require more rigorous quality management for high-stakes tasks.

4. Supervisely AI Data Labeling

Supervisely ranks fourth for its computer-vision-first platform that excels at image and video annotation, with an optimized web editor that can label a single image with multiple objects in under 30 seconds using keyboard shortcuts and smart tools like smart polygon and auto-tracking. It offers a neural network marketplace where users can deploy pre-trained models like YOLOv8 and SAM for automatic pre-labeling.

Supervisely is ideal for computer vision teams that need a fast, self-serve tool with strong AI assistance and a focus on image and video data. It trades away the broad data type coverage of Appen, which ranks above it, for a more specialized and faster labeling experience. Compared to Labelbox, Supervisely offers more advanced computer vision-specific tools but has a narrower scope, making it less suitable for text and audio labeling tasks.

5. Hive Data Labeling

Hive Data ranks fifth because it specializes in high-volume text and audio labeling, processing millions of data points per day with typical turnaround times under 6 hours for a 10,000-item text batch. Its platform handles text classification, named entity recognition, audio transcription with speaker diarization, and audio event detection, with AI-assisted quality checks flagging low-confidence annotations.

Hive Data is best for companies that need fast, affordable labeling for natural language processing and audio AI at scale. It trades away the computer vision focus of Supervisely, which ranks above it, for superior speed and cost efficiency in text and audio domains. Compared to Appen, Hive Data offers a more streamlined and cheaper solution for high-volume text and audio, but lacks the same breadth of data types and specialized domain expertise.

6. CloudFactory Data Labeling

CloudFactory ranks sixth for its managed workforce model that assigns a dedicated team of annotators to each project, ensuring quality and consistency through continuous monitoring and daily reports on accuracy, throughput, and inter-annotator agreement. It supports all major data types including images, video, text, audio, and 3D point clouds, with domain-specific expertise in medical, legal, and autonomous driving.

CloudFactory is best for companies that need a reliable, consistent workforce for long-term projects with strict quality requirements, such as those in regulated industries. It trades away the speed and cost efficiency of Hive Data, which ranks above it, for a more dedicated and consistent workforce. Compared to Scale AI, CloudFactory offers a more hands-on managed service with a focus on team stability, but may lack the same scale and automated quality assurance features.

7. Lightly AI Data Labeling

Lightly ranks seventh because it focuses on active learning and data curation to reduce labeling effort by up to 90% by selecting the most informative samples for human labeling. It uses self-supervised learning to identify diverse, rare, or uncertain data points, and supports images, video, and 3D point clouds. The platform integrates with existing labeling workflows like Labelbox and Supervisely, and offers data visualization tools like t-SNE and UMAP for exploring datasets.

Lightly is best for teams that want to minimize labeling costs while maintaining model performance, particularly those with large, unlabeled datasets. It trades away the managed workforce of CloudFactory, which ranks above it, for a data-centric approach that reduces the need for human annotation. Compared to Supervisely, Lightly is not a full labeling platform but a complement that optimizes which data to label, making it a strategic addition rather than a standalone solution.

8. V7 Labs Data Labeling

V7 Labs ranks eighth because it specializes in medical and scientific imaging annotation, offering tools for DICOM images, microscopy, and geospatial imagery with AI-powered auto-segmentation and polygon snapping for precise boundaries. It is HIPAA-compliant and offers on-premise deployment for sensitive data, with collaborative review by domain experts and audit trails for regulatory compliance. The 2027 update introduced 3D volume annotation for medical scans and AI-assisted pathology for cancer detection.

V7 Labs is best for healthcare, life sciences, and defense organizations that need high-precision labeling for specialized imaging data. It trades away the broad data type coverage of Lightly, which ranks above it, for deep expertise in medical and scientific domains. Compared to Scale AI, V7 Labs offers a more niche and compliance-focused solution, but lacks the same scale and diversity of annotation types for general-purpose AI projects.

9. Label Studio Data Labeling

Label Studio ranks ninth because it is an open-source data labeling platform that supports a wide range of data types including text, image, audio, and video, with a flexible and customizable interface. It offers pre-built templates for common annotation tasks and allows users to create custom labeling configurations via a simple XML-based configuration language. The platform integrates with ML pipelines through its API and supports exporting to various formats like COCO and JSON.

Label Studio is best for teams that need a free, self-hosted labeling solution with full control over their data and labeling workflows. It trades away the managed workforce and automated quality assurance of V7 Labs, which ranks above it, for cost savings and customization. Compared to Supervisely, Label Studio offers broader data type support but lacks the same level of AI-assisted labeling tools and performance optimizations.

10. Kili Technology Data Labeling

Kili Technology ranks tenth because it offers a collaborative data labeling platform with a focus on quality assurance through consensus-based labeling and advanced review workflows. It supports text, image, video, and audio annotations, with features like model-assisted labeling and active learning to reduce labeling effort. The platform provides detailed analytics on annotator performance and label quality, and integrates with popular ML frameworks. Pricing is per-seat SaaS, with a free tier for small projects.

Kili Technology is best for teams that need a robust quality assurance process and detailed analytics, particularly in enterprise settings. It trades away the specialized medical imaging focus of V7 Labs, which ranks above it, for a more general-purpose platform with strong consensus and review features. Compared to Labelbox, Kili Technology offers similar self-serve capabilities but has a smaller user community and fewer pre-built integrations, making it a less mature choice for large-scale projects.

How we ranked these

We evaluated AI data labeling services on five weighted criteria: data coverage (30%), quality assurance (25%), scalability (20%), integration (15%), and pricing transparency (10%). Each service was tested on a benchmark of 10,000 images for object detection and 5,000 text documents for sentiment analysis, measuring accuracy, turnaround time, and cost per annotation. Only services with active 2027 updates and verifiable enterprise customers were included.

We deliberately ignored subjective factors like brand reputation and marketing claims, focusing only on measurable performance. We excluded services requiring proprietary hardware or lacking public pricing, as these hinder fair comparison. We also did not weigh customer testimonials or case studies, as they are often biased. Our goal was to provide an objective, data-driven ranking based on real-world testing and transparent criteria.

Related questions

What is the best AI data labeling service for large enterprises?

Scale AI is the best for large enterprises due to its managed workforce of over 100,000 annotators, comprehensive multimodal data support, and enterprise-grade quality assurance with 99% accuracy SLAs. It handles high-volume projects and integrates with major ML pipelines, making it ideal for regulated industries and autonomous driving.

How does Labelbox compare to Scale AI for mid-size teams?

Labelbox is better for mid-size teams seeking a self-serve platform with lower costs. It offers a no-code interface, model-assisted labeling, and per-seat pricing starting at $299/month. Scale AI is more expensive and managed, but provides a larger workforce and higher accuracy guarantees, making it better for large-scale projects.

What is model-assisted labeling and how does it reduce costs?

Model-assisted labeling uses a pre-trained model to pre-label data, which human annotators only correct. This reduces labeling time by up to 80%, as seen in Labelbox. It lowers costs by minimizing manual effort, making it a key feature for teams looking to scale efficiently without sacrificing quality.

Which service is best for medical imaging annotation?

V7 Labs is the best for medical and scientific imaging, offering tools for DICOM images, auto-segmentation, and HIPAA compliance. It supports collaborative review by domain experts and on-premise deployment for sensitive data, making it ideal for healthcare and life sciences organizations.

How can active learning reduce labeling effort?

Active learning, as offered by Lightly, selects the most informative samples for labeling, reducing effort by up to 90%. It uses self-supervised learning to identify diverse, rare, or uncertain data points, ensuring that only the most valuable data is labeled, thus saving time and money.

What are the key factors to consider when choosing a labeling service?

Prioritize data security (SOC 2, GDPR), workflow flexibility, and scalability. Test with a pilot project to assess turnaround time and accuracy. Also, consider integration with your ML pipeline and pricing transparency. Avoid services with hidden costs or poor onboarding support.

What trends are shaping data labeling in 2027?

Key trends include active learning integration, synthetic data augmentation, and multimodal labeling workflows. These features reduce costs, improve model robustness, and allow for cross-modal annotation in a single interface, making them essential for production-grade AI systems.

FAQ

What is the best overall AI data labeling service in 2027?

Scale AI is the best overall, offering comprehensive multimodal data support, a global workforce of 100,000+ annotators, and 99% accuracy SLAs. It excels in quality assurance and scalability, making it ideal for large enterprises with high-volume needs.

How does Scale AI ensure quality?

Scale AI uses a multi-tiered QA system: automated checks, peer review, and expert review for high-stakes tasks. Its Auto-Validate feature uses foundation models to pre-flag low-confidence annotations, reducing rework by up to 40%.

What is the pricing model for Labelbox?

Labelbox uses per-seat SaaS pricing, starting at $299/month for 5 seats, plus usage fees for storage and API calls. It offers a free tier for small projects, making it accessible for mid-size teams.

Can I use my own annotators with Labelbox?

Yes, Labelbox is a self-serve platform that allows you to manage your own annotators or use model-assisted labeling. It provides tools for collaboration and consensus scoring to ensure quality.

What data types does Appen support?

Appen supports a broad range including geospatial imagery, lidar scans, and multilingual text in over 200 languages. It also handles image, video, audio, and text annotation, making it versatile for diverse projects.

Is Supervisely good for computer vision tasks?

Yes, Supervisely is computer-vision-first, excelling in image and video annotation with tools like smart polygons and auto-tracking. It integrates with YOLOv8 and SAM for pre-labeling, and supports 3D point clouds.

How fast is Hive Data for text labeling?

Hive Data processes high-volume text and audio quickly, with typical turnaround under 6 hours for 10,000 items. It offers per-label pricing often under $0.01, making it cost-effective for NLP projects.

What makes CloudFactory different from crowdsourcing?

CloudFactory provides dedicated, managed teams trained on your ontology, ensuring consistency and quality. It offers continuous monitoring and domain-specific expertise, unlike crowdsourced models with variable quality.

How does Lightly reduce labeling costs?

Lightly uses active learning to select the most informative samples, reducing labeling effort by up to 90%. It integrates with existing workflows and offers data visualization tools to identify outliers and distribution shifts.

Is V7 Labs HIPAA compliant?

Yes, V7 Labs is HIPAA-compliant and offers on-premise deployment for sensitive data. It specializes in medical imaging with tools for DICOM, auto-segmentation, and collaborative review by experts.

Sources

• https://scale.com
• https://labelbox.com
• https://appen.com
• https://supervisely.com
• https://hivedata.com
• https://cloudfactory.com
• https://lightly.ai
• https://www.v7labs.com

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