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Data Labeling

6 Best Data Labeling Companies (2025)

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Averroes
Jul 24, 2025
6 Best Data Labeling Companies (2025)

You already know you need labeled data. 

What you don’t need is another vague list with “top picks” that all sound the same. Some companies are built for scale. Others nail accuracy. A few just talk a big game. 

We’ll break down who’s really good and what makes them worth betting on.

Our Top 3 Picks

Appen

Appen

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TaskUs

TaskUs

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Sama

Sama

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1. Appen

Best for enterprise AI teams with massive datasets, multilingual needs, and strict compliance requirements

If your AI project involves multiple data types, multiple languages, and multiple continents, Appen is probably already on your radar. 

With more than 25 years of experience and over a million contributors worldwide, they specialize in massively scalable, human-in-the-loop data annotation.

What sets them apart is their ability to pair robust quality assurance with massive operational scale. From audio transcription in Swahili to bounding boxes in satellite imagery, they’ve done it, and probably at a scale most teams can’t imagine.

Features

  • Multi-Modal Annotation – text, audio, image, video, and geospatial labeling
  • AI-Human Hybrid – machine pre-labeling with human refinement
  • Custom Schemas + Ontologies – supports domain-specific annotation rules
  • Enterprise Workflow Controls – consensus scoring, gold benchmarks, project tracking
  • Built for Scale – API/SDKs, cloud infrastructure, and export formats for ML integration

Pros:

  • Battle-Tested for Enterprise: Trusted by Fortune 500s across automotive, tech, and healthcare
  • Massive Global Workforce: Over a million contributors for linguistic/cultural coverage
  • Gold-Standard QA: Multilayer quality checks and annotation reviews
  • Strong Compliance: Robust security, privacy standards, and trained contributor base

Cons:

  • Not Transparent on Price: Pricing is custom and geared toward large clients
  • Too Heavy for Small Teams: Best suited for enterprise workflows
  • Scaling QA Is Tough: Consistency can vary when working with massive distributed teams

Score: 4.6/5

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2. TaskUs

Best for enterprise teams seeking scalable, high-accuracy annotation with advanced AI support and AV-specific services

TaskUs helps some of the world’s biggest tech companies train, test, and fine-tune AI systems at scale. With over 45,000 employees and 18 million images processed monthly, TaskUs blends human accuracy with AI-enhanced workflows to deliver end-to-end model support across industries like AV, LLMs, and content safety.

What sets them apart is the scope of their services. From simulation testing for autonomous vehicles to adversarial stress testing for LLMs, TaskUs goes beyond tagging to help clients build more robust, compliant, and production-ready models.

Features

  • Full-Spectrum Labeling: Image, video, text, audio annotation – tailored to AV, NLP, and more
  • AV-Specific Services: Includes simulation testing, scenario curation, live fleet support
  • LLM & AI Model Enhancement: Response scoring, classifier training, adversarial bias testing
  • Global Workforce at Scale: Over 45K trained annotators, freelancers, and QA teams
  • High Accuracy Standards: QA scores exceeding 98%, with consistent 97%+ annotation accuracy
  • Enterprise-Ready Security: Strong compliance protocols across all client data

Pros:

  • AI Lifecycle Coverage: Supports not just labeling, but model training, testing, and optimization
  • Proven Track Record: Trusted by 120+ global clients and recognized by Everest Group as a market leader
  • Annotation at Scale: Rapid processing capacity across modalities with consistent quality
  • Strong in AV & LLM Domains: Offers specialized support for cutting-edge use cases
  • Human + Tech Hybrid Model: Combines trained human reviewers with automation and QA tooling

Cons:

  • Outsourcing Tradeoffs: Less suited for clients needing in-house-only workflows or ultra-sensitive data control
  • Custom Pricing Only: No public pricing tiers; cost may be less accessible for small orgs
  • Complex Project Setup: Advanced projects may require deep coordination upfront

Score: 4.6/5

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3. TELUS International

Best for global-scale labeling across languages, modalities, and regulated industries

With over 1 million annotators, 500+ supported languages, and billions of annotations under its belt, TELUS International isn’t just playing in the data labeling space. It’s helping define the enterprise standard. 

Backed by its proprietary GT Studios platform, TELUS offers a full-stack solution for teams building AI that spans borders, languages, and complex data types.

Whether you’re labeling data for medical imaging, autonomous vehicles, or conversational AI, TELUS has the platform, workforce, and reputation to handle it. 

It’s particularly well-suited for enterprise clients that need labeling at scale, with strong compliance and customization.

Features

  • GT Studios Platform: AI-assisted labeling, workflow orchestration, integrated analytics, and custom pipelines
  • Multi-Modal Annotation: Supports text, audio, image, video, sensor, and geospatial data
  • Massive Global Community: Over 1M annotators and linguists across 500+ languages and dialects
  • Enterprise-Ready Security: Designed for sensitive data and regulatory environments
  • Analytics + QA Tools: Built-in dashboards for performance tracking and quality control
  • High Throughput Capacity: Over 2 billion labels/year, across petabytes of data

Pros:

  • Truly Global Reach: Annotators from across the world ensure cultural and linguistic accuracy
  • Proven Track Record: Recognized by IDC and Everest Group as a top-tier data labeling provider
  • Versatile Use Cases: Supports AI across healthcare, robotics, automotive, and more
  • AI + Human-in-the-Loop: Reduces manual effort while improving annotation precision
  • Project Oversight Tools: Embedded quality controls and dashboards streamline delivery

Cons:

  • Optimized for Scale: May not suit very small or one-off projects

Score: 4.5/5

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4. Centific

Best for enterprises needing full-stack AI data labeling, governance, and model lifecycle management

If you’re doing more than labeling data (training, fine-tuning, evaluating, deploying AI models at scale) Centific might be the powerhouse partner you’re looking for. 

Headquartered in Seattle with global reach and a team of over 1.8 million domain experts, Centific offers more than annotation: it delivers an enterprise-grade AI data foundry.

What makes it stand out is the Frontier AI Data Foundry Platform. A full AI lifecycle system built for serious AI teams. 

From custom fine-tuning to synthetic data sourcing, and from safe AI governance to multi-cloud deployment, Centific is purpose-built for high-stakes, multi-stage AI development.

Features

  • AI Workflow Orchestration: Automate model training, benchmarking, deployment, and RAG pipelines
  • Human-in-the-Loop Oversight: Improve data quality and model accuracy with real-time expert feedback
  • Multi-Cloud + On-Prem Deployments: Flexible infrastructure for security, performance, and control
  • AI Governance & Risk Tools: Assess model safety and prevent vulnerabilities like jailbreaking
  • Data Marketplace Access: Source proprietary, third-party, and synthetic datasets on demand
  • Domain-Specific Capabilities: Supports entity extraction, vector store creation, speech/image/text tasks

Pros:

  • Built for the Full AI Lifecycle: Not just annotation – model training, governance, and deployment too
  • Massive Expert Network: 1.8M domain experts, 150+ PhDs, and 4,000+ AI engineers on tap
  • Scalable & Secure: Infrastructure options for any enterprise, with governance baked in
  • Vertical-Specific Expertise: Strong track record in healthcare, retail, manufacturing, and tech
  • Trusted by Giants: Serves over 100 Global 500 companies with enterprise-grade AI solutions

Cons:

  • Steep for Small Teams: Platform complexity may be overkill for early-stage or resource-light teams
  • Integration Overhead: Tightly integrated platform may require dev lift for ML stack compatibility
  • No Public Pricing: Enterprise pricing model with limited transparency

Score: 4.4/5

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5. Sama

Best for teams who value quality, transparency, and social responsibility in their AI data pipeline

Sama is one of the most mission-driven companies in the space. Built on the principle of impact sourcing, Sama has provided ethical, dignified employment to tens of thousands in underserved regions, while delivering high-accuracy annotations and model validation to AI leaders like Google, GM, and Microsoft.

What makes Sama different isn’t just its 95%+ accuracy guarantees or its support for advanced data types like LiDAR and generative AI. It’s the way it combines rigorous quality control with a transparent, human-centered business model. 

If your company cares about both model performance and ethical AI development, Sama is in a league of its own.

Features

  • Sama Annotate & Validate: Human-labeled image, video, sensor fusion, and LiDAR data with expert QA
  • Sama GenAI: Red teaming, model evaluation, and generative AI dataset curation
  • Five-Step QA Pipeline: Built-in continuous improvement and A/B testing
  • AI-Powered Data Selection: Prioritizes data that improves model performance
  • Enterprise-Grade Security: No data reuse or unauthorized access; client retains ownership

Pros:

  • Ethical by Design: A Certified B Corp creating fair, dignified jobs in developing countries
  • High Accuracy, Human Verified: 95%+ accuracy backed by real QA processes
  • Used by Fortune 50 Giants: Trusted by Ford, Google, Microsoft, and more
  • Robust Platform: Covers everything from data prep to labeling to validation and delivery
  • Generative AI Capabilities: Supports red teaming, prompt evaluation, and fine-tuning

Cons:

  • Regional Limitation: Delivery centers are still concentrated in a few countries
  • Premium Pricing Model: Higher cost vs. fully crowdsourced alternatives

Score: 4.2/5

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6. Akkodis

Best for large organizations needing integrated AI, analytics, and engineering solutions with their data annotation

Akkodis isn’t your go-to if you’re simply looking to annotate some training data. But if you’re leading a full-blown smart industry transformation with AI, advanced analytics, cloud infrastructure, and talent all in the mix, Akkodis delivers on an entirely different level.

Formed from the merger of Modis and AKKA, Akkodis brings together deep expertise in engineering R&D, IT, and digital analytics. 

They’re trusted by major players across automotive, aerospace, healthcare, defense, and banking for solving tough data challenges, from predictive maintenance to fraud detection to AI-powered decision systems.

Features

  • Smart Industry Platform: Combines engineering, data science, and IT into connected, sustainable systems
  • Modern Analytics Infrastructure: Pre-built BI templates, dashboards, and knowledge mining accelerators
  • End-to-End Services: Consulting, platform deployment, AI solutions, talent acquisition, and training
  • Global Talent Pool: Over 50,000 experts, including 2,000+ AI/data analytics professionals
  • Domain-Specific Capabilities: Tailored offerings for complex sectors like manufacturing, defense, logistics, and life sciences

Pros:

  • Enterprise-Scale Expertise: Perfect for companies needing data annotation as part of a broader AI roadmap
  • Rapid Deployment: Analytics platform enables production-ready setups in under 12 weeks
  • Sustainability + Innovation Focus: Supports clean energy and green mobility use cases
  • One-Stop Partner: Offers everything from system design to training and upskilling
  • Trusted by Global Leaders: Extensive track record with Fortune 500 clients

Cons:

  • Not a Dedicated Labeling Vendor: No specific focus on image/video annotation as a standalone service
  • Complex for Smaller Teams: High-level solutions may be overkill for projects that don’t need full-stack support
  • Enterprise-Grade Investment: Cost, integration effort, and onboarding favor large organizations

Score: 4/5

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Comparison: Best Data Labeling Companies

Feature / Company Appen TaskUs TELUS Intl. Centific Sama Akkodis
Supports 2D & Video Annotation ✔️ ✔️ ✔️ ✔️ ✔️ ❌
AI-Assisted Pre-Labeling ✔️ ✔️ ✔️ ✔️ ✔️ ❌
Human-in-the-Loop QA ✔️ ✔️ ✔️ ✔️ ✔️ ❌
Multi-Modal Data Support ✔️ ✔️ ✔️ ✔️ ✔️ ✔️
Labeling + Full AI Workflow ❌ ✔️ ❌ ✔️ ✔️ ✔️
Traceability / Manufacturing Context ❌ ❌ ❌ ✔️ ❌ ✔️
Enterprise-Ready Security ✔️ ✔️ ✔️ ✔️ ✔️ ✔️
Focus on Ethical Sourcing ❌ ❌ ❌ ❌ ✔️ ❌
Specialized Industry Solutions ✔️ ✔️ ✔️ ✔️ ✔️ ✔️

How To Choose?

Here’s what to look for, why it matters, and how the top companies stack up:

1. Quality Assurance

Poor labels lead to poor models. Strong QA processes (like inter-annotator agreement, anomaly detection, and manual review loops) ensure your data is accurate and usable.

  • Best for QA: Sama, Appen, and TaskUs all have advanced pipelines.
  • Less ideal: Akkodis doesn’t focus on annotation QA directly, and Centific may require extra configuration for QA oversight.

2. Industry Expertise

Labeling medical scans isn’t the same as scoring chatbot responses. Choose a provider familiar with your domain to reduce misinterpretation.

  • Best for domain expertise: Averroes (manufacturing), TaskUs (AV and LLMs), and Centific (retail, healthcare).
  • Less specialized: TELUS and Appen offer breadth over deep vertical focus.

3. Scalability & Flexibility

Whether you’re labeling 1,000 images or 10 million, the vendor should scale with your needs and timeline.

  • Best for scale: Appen, TELUS, and TaskUs handle massive data volumes with global teams.
  • Better for small teams or focused needs: Averroes (SaaS), Sama (project-level visibility).
  • Less flexible: Akkodis focuses on full digital transformations, not agile annotation projects.

4. Tech & Tools

Look for AI-assisted labeling, workflow management, and format support. These save time and reduce errors.

  • Standouts: Averroes (Vision Repo), TELUS (GT Studios), Centific (AI data foundry).
  • More service-heavy than tech-led: Sama and TaskUs rely on robust teams over flashy platforms.

5. Data Security & Compliance

If your data is sensitive, you need airtight controls.

  • Best-in-class: Appen, TELUS, Sama, and TaskUs all meet enterprise-grade standards.
  • Watch for gaps: Smaller vendors or generic platforms may lack industry-specific compliance.

Frequently Asked Questions

What is the difference between manual and automated data labeling?

Manual labeling is done entirely by humans, while automated labeling uses AI to pre-label data, which is then verified or corrected by humans. Many providers now use a hybrid approach to improve efficiency without sacrificing accuracy.

How long does it take to label a dataset?

It depends on dataset size, complexity, and the provider’s workflow. Small projects can take days, while enterprise-scale datasets may span weeks. Look for vendors that offer clear SLAs and scalable teams.

Can I use multiple labeling providers for the same project?

Yes, but it adds coordination complexity. Some companies do this to compare quality or split workloads, though it’s more efficient to choose a vendor with strong QA and scalability baked in.

What file formats do labeling tools typically support?

Most platforms support common formats like JSON, CSV, COCO, and Pascal VOC. Always check if your required export formats are supported to avoid integration issues later.

Conclusion

Every data labeling provider brings different strengths to the table. The best choice depends on what you’re optimizing for.

Appen is built for scale, with global coverage and years of enterprise experience. TaskUs shines when you need high-accuracy labeling plus advanced AI support, especially for AV and LLMs. 

TELUS International offers a solid mix of language support, compliance, and multi-modal tools, while Centific is ideal if your project goes beyond annotation into full AI pipeline orchestration. 

Sama brings serious QA and mission-driven ethics, making it a great fit for teams that value transparency and social impact. Akkodis, meanwhile, is best suited to companies embedding annotation into larger digital transformation efforts. 

Choose the one that aligns with your stack, scope, and standards, and you’ll be setting your AI up for a much smoother run.

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