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Ranked by user rating × review volume. See all Data Labeling tools →
Average price: 22 products listed
22 Listings in Data Labeling Available
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$0–$39/mo
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What is Sama? Sama is a data annotation AI agent offering managed data annotation for computer vision and generative AI with an ethical workforce. Founded in 2008 and based in San Francisco, California, USA, Sama helps automotive, retail and AI teams automate data annotation work and get results faster. Key capabilities of Sama Image and video annotation 3D point clouds GenAI data Quality guarantees Quality review workflows Model-assisted labeling How Sama works Sama takes image, video and point clouds as input and produces labeled data. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as AWS S3, Google Cloud Storage, Azure Blob and Python SDK, so the agent works inside existing workflows. Who uses Sama? Sama is built for automotive, retail and AI teams. It suits teams that want image and video annotation and 3D point clouds without adding headcount, while keeping people in control of review and final decisions. Sama vs CloudFactory Sama is often compared with CloudFactory. Sama stands out for image and video annotation and GenAI data. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is CloudFactory? CloudFactory is a platform and services company that helps enterprises run AI reliably in production. It combines data preparation, model and agent orchestration, evaluation and human review without replacing your existing models or infrastructure. Key capabilities of CloudFactory Data Foundation: prepares and structures data across clouds and on-premises Model and agent orchestration: fine-tuning and optimization across model providers Trust and oversight: evaluation, guardrails and human review for compliance Enablement: UI, APIs and SDKs for deployment and scaling AI consulting: strategy, design, development and change management How CloudFactory works CloudFactory sits around your current AI stack. It structures the input data, orchestrates models and agents from multiple providers, then routes outputs through evaluation, guardrails and human reviewers so results are auditable. Teams connect through its UI, APIs and SDKs. Who uses CloudFactory? Enterprises moving AI from pilot to production in regulated or quality-sensitive settings. Customers named on its site include Ibotta, Luminar, Expensify, Microsoft, Mitsubishi Electric, Matterport, Sartorius and Nearmap. CloudFactory pricing CloudFactory does not publish prices. Engagements are scoped through sales because they combine platform use with services and human review capacity. CloudFactory alternatives Sama and Surge AI also provide human-powered data and evaluation services, while Argilla, Datasaur and Lightly offer software for labeling and data curation.
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What is Argilla? Argilla is an open-source collaboration platform for AI engineers and domain experts to build high-quality datasets. It targets language model fine-tuning, RLHF and evaluation, and it has joined Hugging Face. Key capabilities of Argilla Expert annotation: domain experts focus on the data that matters Fine-tuning data: datasets for LLM fine-tuning RLHF feedback: collect human feedback for alignment Evaluation sets: build datasets for model evaluation Automation: automates routine tasks from prototype to production API and UI: Python API and web interface for NLP datasets How Argilla works Engineers create datasets through the API, and domain experts review and label records in the web interface. Automation handles routine steps so curated data moves from prototype to production. Who uses Argilla? Research institutions, data science teams and AI engineers curating NLP datasets. A public demo is available and a Discord community supports practitioners. Argilla pricing Argilla is open source and the vendor homepage lists no paid plans. It now sits within Hugging Face, so hosted options follow Hugging Face terms. Argilla alternatives Segments.ai and CVAT are annotation tools, Toloka and Invisible Technologies provide human data services, and Lightly curates image data.
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What is Roboflow? Roboflow is a computer vision development platform for labeling data, training models and deploying them anywhere. It combines annotation, hosted GPU training, low-code workflows and an inference server. Key capabilities of Roboflow AI-assisted annotation: Label images and video with model help. Hosted GPU training: Train models without managing hardware. Flexible deployment: Edge, VPC or API. Low-code Workflows: Chain models and logic visually. Universe: Public datasets and models. How Roboflow works Teams upload images or video, annotate with AI assistance, train models on hosted GPUs and then deploy through a hosted API, an edge device or their own VPC. Low-code Workflows combine models with logic, and the Public plan makes data and models visible on Universe. Who uses Roboflow? Computer vision developers and companies building vision applications use Roboflow, from hobbyists on the free plan to teams on paid tiers. Roboflow pricing Third-party sources report Public (free, with $60 in credits, data public on Universe), Core from $39 per month and Growth at $299 per month with 20 seats. Extra prepaid credits are reported at $4 each and overage at $6 each. Roboflow alternatives Kili Technology and Encord focus on annotation and data operations, CVAT is an open-source annotation tool, and Dataloop manages unstructured data pipelines. Roboflow is distinguished by combining labeling, training and deployment.
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What is Datasaur? Datasaur provides private enterprise AI that runs on customer infrastructure instead of the cloud. It deploys bespoke open-weights models and harnesses behind your firewall so data and models stay in-house. It began as a data labeling platform for NLP and LLMs. Key capabilities of Datasaur Health+: healthcare administration with automatic PII and PHI stripping Legal+: proprietary tools for law firms Finance+: document processing for contracts, invoices and records Chat: private ChatGPT alternative for internal use Sovereign: air-gapped, zero-egress government deployments How Datasaur works Datasaur benchmarks and fits open-weights models to your processes, then deploys them on your own infrastructure so data and models never leave. The company says organizations own the resulting AI outright, avoiding vendor lock-in and pricing changes. Who uses Datasaur? Enterprises with strict privacy needs, including Fortune 500 companies, AmLaw 100 firms and government agencies. Datasaur argues private AI becomes cheaper than frontier subscriptions at about 40 or more agentic developers. Datasaur pricing Datasaur does not publish prices. Engagements are custom scoped and delivered on your infrastructure. Datasaur alternatives Argilla, Lightly and Segments.ai are data labeling and curation tools, while Surge AI and Toloka supply human data services. Datasaur has shifted toward private AI deployments.
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What is Toloka? Toloka is a human data and evaluation AI agent offering human data, expert annotation and evaluation for training and testing AI models. Founded in 2014 and based in Amsterdam, Netherlands, Toloka helps AI labs and enterprises automate human data and evaluation work and get results faster. Key capabilities of Toloka Expert data creation Model evaluation Red teaming Crowd workforce Quality review workflows Model-assisted labeling How Toloka works Toloka takes text, image and audio as input and produces datasets and evaluations. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as AWS S3, Google Cloud Storage, Azure Blob and Python SDK, so the agent works inside existing workflows. Who uses Toloka? Toloka is built for AI labs and enterprises. It suits teams that want expert data creation and model evaluation without adding headcount, while keeping people in control of review and final decisions. Toloka vs Surge AI Toloka is often compared with Surge AI. Toloka stands out for expert data creation and red teaming. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Kili Technology? Kili Technology is a data annotation platform that helps organizations build high-quality datasets for training and evaluating AI models. It supports images, video, text, audio, geospatial data and documents, with collaborative workflows for large annotator teams. Key capabilities of Kili Technology Multi-modal annotation: Label images, video, text, audio, geospatial data and documents. AI-assisted pre-annotation: Automatic first-pass labels to speed up annotators. Quality review workflows: Review steps to check label quality. Role-based access: Task distribution and permissions across projects. Performance tracking: Real-time performance tracking for annotators. How Kili Technology works Teams create projects, load data, and assign tasks to annotators with role-based access. AI pre-annotation proposes labels, annotators correct them, and review workflows check quality before data is exported. The vendor lets you coordinate hundreds of annotators across dozens of projects while keeping project-level data isolation. Who uses Kili Technology? AI and data teams that build training sets use Kili, along with enterprises that need labeling at scale. The vendor names SAP, Louis Vuitton, Capgemini, Michelin and Airbus among its customers. Kili Technology pricing The vendor lists three tiers: a free Starter plan, Pro at $9 per month, and Enterprise with custom terms. Its site says no credit card is required to start a trial. Kili Technology alternatives Labelbox is a widely used labeling platform, SuperAnnotate offers annotation with managed services, and Scale AI sells labeling as a service. Kili is distinguished by its cloud or on-premise options and compliance certifications.
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What is SuperAnnotate? SuperAnnotate is an AI data infrastructure platform for annotating, curating and evaluating training data, positioned as built for production. It combines customizable multimodal editors, expert review workflows and a network of expert annotators. Key capabilities of SuperAnnotate Multimodal annotation editors: image, video, text and audio editors that are customizable Multi-layer workflows: expert review stages for quality AI and agent evaluation: supports RLHF, SFT, RAG and agentic workflows Analytics and quality tracking: real-time views of project quality Data curation and exploration: tools to explore datasets Project management: dashboard for teams and projects How SuperAnnotate works Teams create projects with custom annotation forms, assign them through multi-layer workflows with review, and track quality in analytics. Data comes from cloud storage connections, and compute is allocated through Orchestrate in compute hours. The vendor lists SOC 2 Type II and ISO 27001:2022 certifications. Who uses SuperAnnotate? ML and data teams building computer vision and LLM systems use it, from small projects to high-volume recurring programs. Customer results cited include 2x faster time to model and 3x faster annotation time. SuperAnnotate pricing SuperAnnotate lists Starter, Pro and Enterprise tiers but no dollar prices; contact sales or request a demo. Starter includes 1K compute hours, Pro 2.5K with SSO and a customer success manager, and Enterprise 10K with a solutions engineer. SuperAnnotate alternatives Alternatives include Roboflow for computer vision datasets, Dataloop for data pipelines, Kili Technology for annotation, Appen for managed data services, and Sama for managed labeling.
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What is Segments.ai? Segments.ai is a multi-sensor labeling AI agent offering a labeling platform for images, 3D point clouds and multi-sensor data for robotics and autonomous vehicles. Founded in 2020 and based in Leuven, Belgium, Segments.ai helps robotics and autonomous vehicle teams automate multi-sensor labeling work and get results faster. Key capabilities of Segments.ai 3D point cloud labeling Image segmentation Sensor fusion labeling Model-assisted labeling Quality assurance workflows Workforce management How Segments.ai works Segments.ai takes image and point clouds as input and produces labeled data. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as AWS S3, Google Cloud Storage, Azure Blob and Hugging Face, so the agent works inside existing workflows. Who uses Segments.ai? Segments.ai is built for robotics and autonomous vehicle teams. It suits teams that want 3D point cloud labeling and image segmentation without adding headcount, while keeping people in control of review and final decisions. Segments.ai vs Encord Segments.ai is often compared with Encord. Segments.ai stands out for 3D point cloud labeling and sensor fusion labeling. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Scale AI? Scale AI is a data and evaluation company that supplies labeled training data, model evaluation and a platform for deploying generative AI and agentic applications. Customers include AI labs, enterprises and public-sector agencies. Key capabilities of Scale AI Data Engine: Produces training data for model development. Data labeling and annotation: Human and machine annotation at scale. AI evaluation and benchmarking: Tests models against custom and public benchmarks. Agentic AI for enterprise: Agent solutions that automate enterprise workflows. GenAI platform: Tools to deploy and customize generative AI. How Scale AI works Customers define the data or evaluation they need, and Scale combines human experts and automated tooling to produce labeled datasets, rankings or test results delivered through API or platform. The GenAI platform lets enterprises connect models to their own data. Who uses Scale AI? Frontier AI labs, large enterprises and government agencies building or deploying AI models use Scale. It is sold as a service rather than a self-serve tool. Scale AI pricing Scale AI does not publish pricing. Engagements are priced through custom enterprise agreements negotiated with the sales team. Scale AI alternatives Surge AI provides expert human data, Snorkel AI offers programmatic data development, and Labelbox and Encord provide labeling platforms. Scale is positioned as a large, services-heavy data provider.
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What is Cleanlab? Cleanlab is a platform that detects and fixes errors in AI agent responses. The company was recently acquired by Handshake AI, and its site describes an independent safety layer that works with any AI system. Key capabilities of Cleanlab Real-time detection: Hallucinations and retrieval errors. Policy violations: Flags rule-breaking responses. Malicious use detection: Catches misuse attempts. Documentation gap finding: Shows missing knowledge base content. Human-in-the-loop remediation: Non-technical teams fix issues. Flexible deployment: VPC or SaaS. How Cleanlab works Cleanlab sits as a guardrail layer around an AI application, scores responses and escalates risky ones to humans. Teams then fix knowledge base problems through review workflows. Who uses Cleanlab? Customer support teams and employee-facing AI applications. Cleanlab appeared on Forbes 2024 AI 50 and CB Insights 2024 AI 100. Cleanlab pricing Cleanlab does not publish pricing on its homepage, and its status after the Handshake AI acquisition should be confirmed with the vendor. Cleanlab alternatives Appen and Surge AI provide human data labeling, Kili Technology offers labeling tooling and Sama offers annotation services. Cleanlab focuses on automated detection of AI errors.
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What is CVAT? CVAT is a data labeling suite for computer vision teams, available as open source and as the CVAT Online cloud service. It supports bounding boxes, polygons, masks, keypoints, video tracking and 3D point clouds. Key capabilities of CVAT Multi-format annotation: Boxes, polygons, masks and keypoints. Video object tracking: Track objects across frames. 3D point cloud labeling: Annotate 3D data. AI-assisted labeling: Models such as SAM 2, Ultralytics and Hugging Face. Quality control: Validation and consensus workflows. How CVAT works Teams create projects, upload data or link cloud storage, and annotate manually or with AI assistance. Review and QA tools check the work, and datasets export in many formats for training. Paid plans include API access and Hugging Face model integration. Who uses CVAT? Computer vision teams and solo practitioners use CVAT. Larger organizations adopt the Enterprise plan or self-host the open-source edition. CVAT pricing CVAT Online lists Solo at $33 per month ($23 yearly) and Team at $33 per user per month ($23 yearly), a free plan with limited features, and Enterprise starting at $12,000 per year. CVAT alternatives Roboflow combines annotation with training and deployment, Kili Technology is a commercial annotation platform, and Encord adds data curation. CVAT is distinguished by its open-source heritage.
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Data labeling platforms annotate, label, and curate training data for machine learning, with AI-assisted labeling, human review, and quality control, to build the high-quality datasets models depend on. This guide explains what data labeling software is, how it works, what matters, and how to choose one.
Data labeling platforms annotate, label, and curate training data for machine learning, with AI-assisted labeling, human review, and quality control, to build the high-quality datasets models depend on. This guide explains what data labeling software is, how it works, what matters, and how to choose one.
Data labeling software helps teams annotate data, images, text, audio, video, and more, to create labeled datasets for training and evaluating machine-learning models, increasingly using AI to pre-label and accelerate human annotation.
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See where data labeling fits in a complete stack, with the other software, AI agents and services each business needs.
It spans annotation platforms (with tools for many data types and tasks), managed labeling services (combining software and human workforces), and data-curation and quality tools.
The category is critical to ML and LLM development, where data quality often matters more than model choice. Buyers weigh labeling quality and throughput, supported data types and tasks, workforce options, and data security.
Teams define a labeling task and guidelines; the platform serves data to annotators (and AI pre-labelers), captures labels, runs quality checks and consensus, and exports curated datasets for model training.
Platforms combine annotation tools for various data types, AI-assisted pre-labeling, workflow and workforce management, and QA/consensus and dataset-curation features.
Teams configure tasks, guidelines, and quality thresholds; annotators (in-house, managed, or crowd) label with AI assistance while reviewers ensure quality, and curated data feeds model development.
Tools for image, text, audio, video, 3D, and document labeling across many tasks.
Model-assisted pre-labeling and active learning speed annotation and cut cost.
Review, consensus, and metrics ensure label accuracy and consistency.
Manage tasks, guidelines, and annotators (in-house, managed, or crowd).
Curate, version, and manage datasets, including edge cases and balance.
Access controls, data handling, and compliance for sensitive training data.
Accurate, consistent labels are the foundation of model performance.
AI-assisted labeling and active learning cut annotation time and cost.
Label large datasets with managed or crowd workforces.
Curate balanced, representative datasets and surface edge cases.
Consensus and QA reduce label errors that degrade models.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| Annotation platforms | In-house labeling tools | Any | Control and flexibility | You supply the workforce |
| Managed labeling services | Software plus workforce | Mid-market to enterprise | Scale without hiring | Cost; data sharing |
| AI-assisted/auto-labeling | Model-assisted annotation | Any | Speed and cost savings | Needs human QA |
| Data curation & QA tools | Dataset quality and management | ML teams | Better data, fewer errors | Complements labeling |
Technology: Build training datasets for ML, computer vision, and LLM development.
Automotive: Label sensor and video data for autonomous and ADAS systems.
Healthcare: Annotate medical images and records with strict privacy controls.
Retail & E-commerce: Label product images and text for search and recommendations.
Financial Services: Annotate documents and data for fraud and risk models.
Agriculture: Label imagery for crop, yield, and monitoring models.
Quality is paramount, assess QA, consensus, and accuracy controls for your task.
Confirm support for your data types (image, text, audio, video, 3D) and annotation tasks.
Evaluate model-assisted labeling and active learning for speed and cost.
Decide between in-house tools, managed services, or crowd, and confirm fit.
Verify access controls and compliance, especially for sensitive training data.
Understand per-label, per-seat, or managed-service pricing and how it scales.
AI-assisted and automated labeling are sharply reducing the human effort per label, with humans focusing on QA and edge cases.
Data-centric AI is shifting focus from models to dataset quality and curation.
Synthetic data and active learning are reducing the volume of manual labeling needed.
Buyers should prioritize label quality and QA, data-type and task coverage, AI assistance, and data security.
Data labeling software helps teams annotate data, images, text, audio, video, documents, and more, to create labeled datasets for training and evaluating machine-learning models, increasingly using AI to pre-label and speed up human annotation. It spans annotation platforms, managed labeling services that combine software with human workforces, and data-curation and quality tools.
Models learn from labeled examples, so the accuracy, consistency, and representativeness of labels often matter more than the model architecture itself. Poor labels produce poor models. High-quality, well-curated training data, with strong QA, is foundational to model performance, which is why data labeling and curation are critical to AI development.
Increasingly, yes, model-assisted pre-labeling and active learning automate much of the work, with humans reviewing and correcting, especially edge cases. This cuts time and cost substantially. Fully automated labels still need human QA to avoid propagating errors, so the best workflows combine AI assistance with human review.
It depends on volume, sensitivity, and expertise. Annotation platforms give you control and suit sensitive data you can't share. Managed services provide software plus a workforce to scale without hiring, at higher cost and with data-sharing considerations. Many teams blend both, platform tooling with managed or crowd workforces for scale.
Quality comes from clear guidelines, consensus (multiple annotators), review workflows, and accuracy metrics, plus AI-assisted checks. Evaluate a platform's QA and consensus features and test label accuracy on a sample of your data, since label quality directly drives model performance.
It depends on the deployment and provider. For sensitive data, confirm access controls, data handling, compliance, and whether data is ever used beyond your labeling. Highly sensitive data may warrant in-house labeling or providers with strong security and on-premise or private options.
Common models are per-label/annotation, per-seat for platforms, or managed-service pricing by volume and complexity. Estimate your dataset size, data types, and quality needs, and factor in AI-assistance savings and workforce costs to compare true cost.
Make label quality and QA your top criterion, then confirm support for your data types and tasks, AI-assisted labeling and throughput, workforce options, data security, and pricing. Run a pilot on a sample of your data, measure label accuracy, and assess throughput before committing to a large dataset.
