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.
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
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
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.
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
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
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
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.
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
VIEW NOWTaskUs
VIEW NOWSama
VIEW NOW1. 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
Pros:
Cons:
Score: 4.6/5
View Now
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
Pros:
Cons:
Score: 4.6/5
View Now
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
Pros:
Cons:
Score: 4.5/5
View Now
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
Pros:
Cons:
Score: 4.4/5
View Now
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
Pros:
Cons:
Score: 4.2/5
View Now
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
Pros:
Cons:
Score: 4/5
View Now
Comparison: Best Data Labeling Companies
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.
2. Industry Expertise
Labeling medical scans isn’t the same as scoring chatbot responses. Choose a provider familiar with your domain to reduce misinterpretation.
3. Scalability & Flexibility
Whether you’re labeling 1,000 images or 10 million, the vendor should scale with your needs and timeline.
4. Tech & Tools
Look for AI-assisted labeling, workflow management, and format support. These save time and reduce errors.
5. Data Security & Compliance
If your data is sensitive, you need airtight controls.
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.