The top human data labeling providers in 2026 are Scale AI, Surge AI, Labelbox, Snorkel AI, Appen, Sama, iMerit, Telus Digital, Toloka, and SuperAnnotate. The data collection and labeling market reached $4.89 billion in 2025 and is projected to hit $17.10 billion by 2030, growing at a 28.4% CAGR, according to Grand View Research (Nov 2024). That means the companies supplying labeled training data - and the people doing the labeling - are becoming critical infrastructure for every AI system on the planet.
This guide breaks down each provider's workforce model, pricing structure, and specialties so recruiters and hiring managers can identify the right partner for their AI training data needs. Whether you're staffing an in-house annotation team or evaluating vendors, understanding who does what (and how they hire) gives you a serious edge. For a broader look at who's hiring in this space, see our guide to the AI data annotation hiring landscape.
TL;DR: The data labeling market hit $4.89B in 2025 (Grand View Research) and is growing 28.4% annually. Scale AI and Surge AI dominate at the top, while specialists like Sama and iMerit win on quality. RLHF demand is shifting hiring from gig workers to domain experts earning $30-$100+/hr.
Why Does the Data Labeling Market Matter for Recruiters?
The global data collection and labeling market grew from $0.8 billion in 2022 to $4.89 billion in 2025 - a 6x increase in three years (Grand View Research, Nov 2024). For recruiters, that growth translates directly into hiring demand. Every dollar spent on data labeling creates roles: annotators, QA reviewers, project managers, and increasingly, domain experts in medicine, law, and software engineering.
The talent gap is real. AI industry veteran Liu Renming estimated the global shortfall of RLHF-qualified workers at roughly 30 million in late 2024 - a figure that reflects how quickly demand has outpaced supply. That's because the work has changed. Early annotation was simple: draw boxes around objects, tag sentiment in text. Today's frontier AI models need humans who can evaluate complex reasoning, rank nuanced outputs, and spot errors that require genuine domain knowledge. For more on how the annotation hiring landscape is shifting, see our full industry breakdown.
That shift has split the labor market in two. Entry-level annotators earn $15-$20/hr, while domain experts in medical, legal, and coding evaluation earn $50-$100+/hr, according to Glassdoor and ZipRecruiter (2025-2026). Recruiters who understand which tier a provider operates in - and what kind of talent they need - can place candidates more accurately and close roles faster.
If you're hiring annotation talent directly (rather than outsourcing to a vendor), AI-powered sourcing tools can help you find domain experts at scale. Pin's AI sourcing searches 850M+ profiles to surface candidates with niche expertise - useful when you need radiologists willing to do annotation work or software engineers for code evaluation tasks.
Who Are the Top Full-Service Human Data Labeling Providers?
These providers offer managed annotation services with their own workforces. You send data, they return labels. They're the right fit when you need volume, consistency, and don't want to build an in-house team.
If you also need contact enrichment and profile intelligence, compare these profile data APIs for recruiting.
1. Scale AI
Scale AI is the largest data labeling operation in the industry, with 240,000+ gig contractors across its Remotasks and Outlier platforms. The company hit a $1.5 billion annualized run rate in 2024, up 97% year-over-year, according to Sacra. Scale handles everything from computer vision labeling to LLM fine-tuning via RLHF.
The company's landscape shifted dramatically in 2025. Meta acquired a 49% stake for $14.3 billion in June 2025, valuing Scale at $29 billion (CNBC, Jul 2025). Founder Alexandr Wang departed to become Meta's Chief AI Officer. The fallout was significant: OpenAI and Google both reduced or ended their contracts with Scale, citing competitive data concerns. Scale subsequently cut 200 full-time employees - 14% of its workforce.
Best for: Enterprise-scale annotation, government/defense AI projects, autonomous vehicle data. Pricing: Enterprise-only, custom contracts. Caveat: The Meta ownership change has created instability. Buyers outside Meta's ecosystem should evaluate whether Scale's neutrality concerns affect their use case.
2. Surge AI
Surge AI focuses exclusively on premium RLHF and LLM fine-tuning data. Its 50,000 expert contractors handle preference ranking, reasoning evaluation, and chain-of-thought annotation - the high-skill work that frontier AI labs need most. The company generated $1.2 billion in revenue in 2024 while remaining fully bootstrapped, according to Bloomberg (Jul 2025).
Surge initiated its first external fundraise in mid-2025, seeking $1 billion at a $25 billion valuation. Its client list reads like a who's who of AI: OpenAI, Google, Anthropic, and Microsoft all use Surge for model training data. The company's capital efficiency is remarkable - 130 full-time employees supporting $1.2B in revenue.
Best for: Frontier AI labs needing expert-level RLHF data. Pricing: Custom contracts, premium pricing. Caveat: Tiny internal team for a company at this scale. Not suited for basic annotation work - their focus is expert-tier only.
3. Appen
Appen was once the default choice for large-scale annotation. The ASX-listed company built a global workforce of 30,000+ contractors spanning dozens of languages. But 2024 was rough: Google terminated an approximately $82.8 million annual contract in March 2024 (Staffing Industry Analysts), causing total revenue to fall 14% to $235.7 million. The company closed offices, laid off LLM specialists, and saw multiple C-suite departures.
On the positive side, Appen's net loss narrowed by $98 million in FY2024, and the company still operates one of the most multilingual annotation workforces available.
Best for: Multilingual annotation at scale, search quality rating. Pricing: Managed service, custom enterprise contracts. Caveat: Structural decline since the Google exit has raised questions about workforce quality and retention. Financial trajectory is improving but still uncertain.
4. Sama
Sama operates a different model: 5,000+ on-staff data experts (not gig contractors) working from Kenya and Uganda, with 50% women representation. The company is B Corp certified and re-certified in June 2025 with an improved impact score. Sama has raised $84.84 million total, including a $70 million Series B in 2022. Its client list includes Google, GM, Ford, Walmart, and NVIDIA.
Sama specializes in image, video, LiDAR, and multimodal annotation, making it strong for autonomous vehicle and robotics data. In February 2025, the company launched its "Agentic Capture" framework for multimodal AI agent data acquisition.
Best for: Autonomous systems, complex multimodal data, companies with ESG procurement requirements. Pricing: Custom managed service contracts. Caveat: The impact-first model limits workforce scalability compared to crowdsourced platforms. Primarily Africa-based workforce may create timezone coordination challenges for some teams.
5. Telus Digital
Telus Digital (formerly TELUS International) brings massive scale: 79,000 employees globally, a community of 1 million+ annotators and linguists, and a throughput of 2 billion+ labels per year. The company was recognized as an Everest Group Leader in Data Annotation and Labeling Solutions for 2024.
The scale comes with turbulence, though. Meta ended its content moderation contract with Telus in April 2025, sending approximately 2,000 Barcelona-based employees home with pay (Globe and Mail). The parent company dropped 3,300 net jobs across 2024.
Best for: Enterprise-volume annotation, multilingual projects (100+ languages), turnkey managed service. Pricing: Enterprise managed service, custom contracts. Caveat: BPO heritage means less AI specialization than pure-play annotation firms. The Meta contract loss created significant operational disruption.
Which Platform-Based Data Labeling Providers Stand Out?
These providers either offer software platforms (you bring your own labelers) or specialized services targeting specific annotation niches. They're the right fit when you need tooling, programmatic labeling, or deep domain expertise.
6. Labelbox
Labelbox is a SaaS platform for enterprise AI data operations - teams bring their own data and labelers but use Labelbox for workflow management, tooling, quality control, and model evaluation. The company has raised $189 million total, including a $110 million Series B in January 2022 led by SoftBank. More than 80% of the top US AI labs use Labelbox, and the platform achieved HIPAA compliance by early 2025.
Best for: Enterprise AI teams that want to manage their own annotation workforce with professional tooling. Pricing: SaaS subscription, enterprise tier with custom contracts. Caveat: Platform-only model means customers must source their own labelers - Labelbox doesn't provide the workforce.
7. Snorkel AI
Snorkel AI takes a different approach entirely: programmatic data labeling. Instead of paying humans to label every data point, Snorkel uses "weak supervision" to generate labels at scale, dramatically reducing manual annotation time. The company raised $100 million in a Series D round in May 2025, reaching a $1.3 billion valuation (Business Wire, May 2025). Total funding: $237 million.
Snorkel serves five of the top ten US banks, along with major enterprises like BNY Mellon and Memorial Sloan Kettering. Its platform handles model evaluation, agent diagnostics, and RAG (retrieval-augmented generation) quality assessment.
Best for: Enterprise teams with ML expertise that want to reduce manual labeling costs through automation. Pricing: Enterprise SaaS, custom pricing. Caveat: Requires ML sophistication to implement effectively. Not a plug-and-play managed service.
8. iMerit
iMerit employs approximately 7,000 people with a 91% retention rate and 50% women representation. The company's philosophy is quality over volume - and in July 2025, it launched the "Scholars" program: a vetted network of 4,000+ cognitive domain experts for GenAI fine-tuning, with plans to scale to 10,000 (TechCrunch, Jul 2025).
The Scholars network includes specialists in medical imaging, autonomous systems, NLP, and complex domain annotation. iMerit has raised just $24.3 million total - growing organically on cash reserves rather than burning venture capital.
Best for: Healthcare AI, autonomous systems, high-precision domain annotation where accuracy matters more than throughput. Pricing: Managed service, custom pricing. Caveat: Modest funding compared to peers. Not designed for high-volume commodity tasks.
9. Toloka (Nebius Group)
Toloka was originally Yandex's internal annotation system, spun out in 2024 when Yandex split its Russian and international assets. Now a subsidiary of Nasdaq-listed Nebius Group, Toloka raised $72 million in May 2025 led by Bezos Expeditions, with Shopify CTO Mikhail Parakhin participating (PYMNTS, May 2025).
The platform operates in 100+ countries with 20,000+ monthly contributors generating 80 million annotations per week. Toloka's Mindrift platform connects clients with domain experts - physicists, lawyers, software engineers - for high-skill annotation tasks. Its pay-as-you-go pricing model makes it accessible for teams that don't want annual contracts.
Best for: Multilingual NLP, complex reasoning tasks, teams wanting flexible pay-as-you-go pricing. Pricing: Pay-as-you-go (most accessible pricing model on this list). Caveat: Yandex origin may create geopolitical perception concerns for some enterprise buyers. Less brand recognition than Scale or Appen in the US market.
10. SuperAnnotate
SuperAnnotate is an AI-native annotation platform offering both SaaS tooling and managed annotation services. The company raised $50 million in a total Series B - $36 million led by Socium Ventures in November 2024, plus $13.5 million from Dell Technologies Capital (SuperAnnotate, Nov 2024). Strategic investors include NVIDIA and Databricks Ventures. SuperAnnotate won the 2025 Databricks ISV Customer Impact Partner of the Year award.
Best for: Teams wanting both platform tooling and managed annotation in one package, especially those in the Databricks/NVIDIA ecosystem. Pricing: Tiered SaaS subscription with a free tier available; managed service pricing on request. Caveat: Less established than Scale or Appen for large enterprise procurement. Smaller contractor network than managed-service-only providers.
How Do These Human Data Labeling Providers Compare?
| Provider | Model | Workforce Size | Best For | Starting Price |
|---|---|---|---|---|
| Scale AI | Managed service | 240,000+ contractors | Enterprise-scale, defense AI | Custom (enterprise-only) |
| Surge AI | Managed service | 50,000 experts | RLHF, LLM fine-tuning | Custom (premium) |
| Appen | Managed service | 30,000+ contractors | Multilingual annotation | Custom (enterprise) |
| Sama | Managed service | 5,000+ on-staff | Autonomous systems, ESG buyers | Custom (managed) |
| Telus Digital | Managed service | 79,000 employees; 1M+ community | High-volume, multilingual | Custom (enterprise) |
| Labelbox | SaaS platform | N/A (bring your own) | Enterprise AI data ops | Custom (SaaS tiers) |
| Snorkel AI | SaaS platform | N/A (programmatic) | Reducing manual labeling | Custom (enterprise SaaS) |
| iMerit | Managed service | ~7,000 employees | Healthcare, precision annotation | Custom (managed) |
| Toloka | Platform + crowd | 20,000+ monthly contributors | Multilingual NLP, flexible pricing | Pay-as-you-go |
| SuperAnnotate | SaaS + managed | Not disclosed | All-in-one tooling + service | Free tier available |
Honorable Mentions
Three providers didn't make the top 10 but deserve attention for specific use cases:
- Prolific: Research-focused platform with 200,000+ vetted, pre-profiled participants. Strong for RLHF preference data and academic-quality AI evaluation. Raised 25 million GBP in a Series A (2023). Good for teams that need high-precision behavioral and linguistic data.
- CloudFactory: 7,000+ trained analysts with strength in healthcare, geospatial, and finance verticals. Total funding of $78 million (as of 2024). Good for regulated industries needing consistent, trained workforces rather than gig labor.
- Humans in the Loop: Social enterprise employing 1,000+ conflict-affected people across 12+ countries. Boutique scale, but a strong ethical sourcing differentiator for ESG-focused enterprise procurement.
What Annotator Roles Are Companies Hiring For?
The salary spread in data labeling is enormous - and it keeps widening. Entry-level annotators earn $15-$20/hr, while senior LLM evaluators with domain expertise command $50-$100+/hr, according to Glassdoor and ZipRecruiter (2025-2026). The average full-time data annotation salary in the US sits at $64,556/yr.
Here are the four main tiers companies are hiring for:
- General annotators ($15-$20/hr): Image tagging, basic text classification, sentiment labeling. High-volume, lower-skill work typically staffed through gig platforms.
- Lead annotators and QA specialists ($28-$40/hr): Quality review, edge case resolution, training new annotators. These roles require judgment and consistency.
- Technical domain experts ($30-$50/hr): Code evaluation, software bug annotation, algorithm output ranking. Surge AI and iMerit's Scholars program specifically target this tier.
- Regulated-domain experts ($50-$100+/hr): Medical imaging evaluation, legal document review, financial compliance annotation. Board-certified professionals doing annotation as a side gig or career pivot.
The top tier is where recruiting gets hardest. You can't find radiologists willing to annotate medical AI training data on gig platforms. That's a sourcing problem - and it's why AI labs are rethinking how they hire for these roles. Specialized sourcing tools that search deep candidate databases are becoming essential for this niche.
Which Human Data Labeling Provider Fits Your Needs?
Not every provider fits every use case. The right choice depends on three factors: what you're labeling, how much you need, and whether you want to manage the workforce yourself.
If you need massive volume with minimal management: Scale AI or Telus Digital. Both can handle millions of labels across image, text, and video. Scale's Meta ownership is a factor to weigh; Telus brings BPO-grade operations but less AI specialization.
If you're training frontier AI models: Surge AI is the clear choice for RLHF and LLM fine-tuning. Their expert-tier contractors handle the preference ranking and reasoning evaluation that commodity platforms can't touch.
If you want to manage your own annotation team: Labelbox gives you the platform tooling; you source and manage the labelers. Combine it with an AI-powered sourcing tool to find domain-specific annotators. For practical sourcing strategies, see our guide to finding human data labelers.
If quality and ethics matter for procurement: Sama (B Corp, impact sourcing) and iMerit (91% retention, Scholars program) both prioritize workforce quality over volume. They're slower to scale but deliver higher-accuracy labels.
If you want to reduce manual labeling entirely: Snorkel AI's programmatic approach can replace large portions of human annotation with automated labeling. Best for teams with ML engineering capacity.
If you need flexible, small-batch annotation: Toloka's pay-as-you-go model and SuperAnnotate's free tier both let you start small without committing to enterprise contracts.
How Can Recruiters Source Data Labeling Talent?
Many of the providers above are actively hiring - and so are the AI companies that use them. Whether you're placing candidates at annotation vendors, staffing in-house teams, or recruiting domain experts for RLHF work, the sourcing challenge is real. Traditional job boards barely scratch the surface for this talent pool.
Here's what works:
- AI-powered candidate databases: Tools that search across hundreds of millions of profiles can surface candidates with niche domain expertise - the medical professionals, lawyers, and engineers who make ideal high-tier annotators. Pin searches 850M+ profiles with 100% coverage in North America and Europe, which matters when you're looking for a radiologist in Ohio who's open to annotation work.
- University networks: PhD students and postdocs in linguistics, computer science, and domain sciences are prime candidates for annotation project work. They have the expertise and often need supplemental income.
- Freelance platforms with AI focus: Upwork, Freelancer, and specialized platforms like Prolific and Remotasks all have annotation-capable talent, though quality varies enormously.
- Professional communities: Medical annotation roles? Target radiology forums and professional associations. Legal annotation? Reach out to bar association networks. The domain expertise is the hard part - annotation skills can be trained.
Recruiters who've adopted AI-powered sourcing for niche roles report strong results. Nick Poloni, President at Cascadia Search Group, closed over $1M in billings during his first four months using this approach: "The sourcing data is incredible, scanning 850M+ profiles with recruiter-level precision to uncover perfect-fit candidates I'd never find otherwise."
For a deeper dive into recruiting for AI training roles, see our guides on recruiting AI tutors and AI candidate sourcing strategies.
The data labeling market is growing at 28.4% annually toward a projected $17.10 billion by 2030. Every provider on this list is hiring, and the companies that use them need even more annotation talent. The shift from commodity gig work to expert-level RLHF roles means higher-paid, harder-to-fill positions - exactly the kind of placement where specialized sourcing pays for itself.
Find annotation specialists with Pin's AI sourcing - free to start
Frequently Asked Questions
What is a human data labeling provider?
A human data labeling provider supplies trained workers who annotate, classify, and tag raw data so AI models can learn from it. The global market for these services reached $4.89 billion in 2025, according to Grand View Research. Providers range from massive managed services like Scale AI (240,000+ contractors) to specialized platforms like Snorkel AI that reduce manual labeling through automation.
How much do data labeling services cost?
Most enterprise providers don't publish pricing - Scale AI, Surge AI, Labelbox, and Snorkel AI all require custom contracts. Toloka offers the most accessible model with pay-as-you-go pricing, while SuperAnnotate provides a free tier for smaller teams. The underlying labor costs range from $15/hr for entry-level annotators to $100+/hr for medical and legal domain experts (Glassdoor, 2025).
What is RLHF and why does it matter for data labeling?
RLHF (Reinforcement Learning from Human Feedback) is the process of having humans rank and evaluate AI outputs to improve model quality. It's how ChatGPT, Claude, and other large language models get fine-tuned. RLHF requires higher-skilled annotators than traditional labeling - domain experts who can evaluate reasoning, not just tag images. This demand has pushed top-tier annotator pay to $50-$100+/hr.
Which data labeling provider is best for RLHF?
Surge AI is the most focused RLHF provider, with 50,000 expert contractors serving OpenAI, Google, Anthropic, and Microsoft. The company generated $1.2 billion in 2024 revenue almost entirely from frontier AI lab RLHF work (Bloomberg, Jul 2025). iMerit's Scholars program (4,000+ vetted domain experts) is another strong option for teams needing high-precision evaluation.
How can recruiters find data annotation talent?
Traditional job boards don't reach most annotation talent. The fastest approach is using AI-powered sourcing tools that search across large candidate databases - Pin's database includes 850M+ profiles with 100% coverage in North America and Europe. University research networks, freelance platforms like Prolific and Upwork, and domain-specific professional communities (radiology forums, bar associations) are also productive channels.
