Label Your Data vs CloudFactory: Data-Backed Comparison for ML Teams

What You’re Choosing Between and How to Avoid the Wrong Vendor

You’ve narrowed your annotation vendor search to Label Your Data and CloudFactory. Now you need to make the final call. Both offer managed annotation services with strong quality claims, but the differences matter more than you might expect.

Here’s what actually separates these vendors: 

• Quality guarantees backed by SLAs versus systematic QA processes
• Transparent pricing versus enterprise sales cycles
• 55+ language NLP support versus CV-focused capabilities
• Tool-agnostic flexibility versus proprietary platforms
• No-commitment flexibility versus annual contracts

We’ve analyzed customer reviews, case studies, and verified metrics to answer your decision-critical questions: Which delivers more consistent accuracy? Who handles complex CV tasks better? Where’s the pricing clarity? Which onboards faster?

First, understand what you’re actually comparing: managed annotation services with human teams, not pure self-serve platforms like Labelbox or SuperAnnotate. Both Label Your Data and CloudFactory provide managed human annotation teams plus technology, but their operational models differ fundamentally.

Label Your Data

A specialist annotation provider with 1,000+ annotation experts globally. Offers both a self-serve data annotation platform for computer vision and managed data annotation services. 

Label Your Data is tool-agnostic; the team can easily integrate with your existing stack or any annotation tool. Provides transparent per-object/hour data annotation pricing with no minimums and a free pilot.

Trusted by 100+ customers including ML engineers, data scientists, and academic researchers who prioritize quality consistency and deployment speed.

CloudFactory

An enterprise-scale annotation provider with 7,000+ global analysts across Nepal, Kenya, US, and UK. Assigns dedicated teams to clients long-term (often years) to build institutional memory. 

CloudFactory offers hybrid platform (“Accelerated Annotation” with AI tools) plus managed workforce. Built for organizations needing massive sustained volume capacity: particularly autonomous driving, large-scale geospatial mapping, and multi-year annotation programs.

By the end of this article, you’ll have the data you need to confidently recommend one vendor to your team and avoid the costly mistake of choosing the wrong fit for your machine learning workflow.

CloudFactory is strong when you need large volumes labeled consistently. For complex or specialized annotation, Label Your Data delivers higher-detail results, especially when tasks require custom guidelines or deeper subject-matter expertise.

Nate Nead CEO, LLM.co

Just Want the Key Differences? Quick Label Your Data vs CloudFactory Comparison

To help you evaluate both data annotation vendors quickly, here’s a side-by-side breakdown of the key differences that determine whether your ML deployment stays on schedule or gets derailed by poor labels:

The differences in QA methodology, language support, and contract flexibility directly impact your project timeline and machine learning algorithm (and model) performance.

Here’s what each distinction means for your workflow.

Which Vendor Delivers Better Annotation Quality?

How Label Your Data ensures accuracy

Quality process:

• Multi-layer review: Annotator → Reviewer → QA specialist
• Ratio: 2-3 QA reviewers per 10-20 annotators
• 98%+ accuracy benchmark using inter-annotator agreement (IAA), Cohen’s Kappa, F1 scores
• Quality SLAs: “Accuracy & deadlines or you don’t pay”

Real customer results:

• “Label Your Data stood out as the most impressive. They were the first to process the sample during our pilot and achieved 100% accuracy after a brief call to clarify our requirements.”
• “We decided to move forward with higher quality annotation results [from Label Your Data] which we saw after the pilot... now we have a trained team which returns consistent annotations.”
• Nodar autonomous driving case: 60,000 polygon annotations with “no rework” after pilot alignment

Label Your Data uses free pilots to align on guidelines before production, assigns dedicated teams (not rotating workers), and provides domain experts for specialized tasks. We’re confident enough to guarantee accuracy contractually.

How CloudFactory handles quality control

Quality process:

• 100% QA guarantee (every task reviewed)
• Multi-layer QC: Gold standard checks, sample reviews, consensus labeling, automated IoU checks
• 2-tier review process with team leads overseeing annotators

Real customer results:

• “>99% accuracy on 1,000 images per hour” for geospatial client
• Matterport Senior ML Engineer: “proactive approach in ensuring the delivery of high-quality results”
• Nearmap: Weekly corner case discussions with team to refine labels until “as perfect and pristine as possible”

CloudFactory’s quality is strong but requires active client collaboration, especially early on. One G2 reviewer notes: “reliance on outsourcing may introduce challenges related to communication... especially for complex tasks requiring close collaboration.” Their distributed workforce means you need clear communication channels to maintain consistency.

What customer reviews say about quality

Poor annotations corrupt machine learning datasets and waste training time. See what real clients say:

Label Your Data (4.9 on G2 and 5 on Clutch):

• “Incredible Quality" with 98% guarantee referenced consistently
• “Minimal client corrections needed”
• Multiple clients report switching after previous vendors “couldn’t get quality right”

CloudFactory (4.5 on G2):

• “Reliable,” “Accountable,” “dedication to maintaining high-quality standards”
• Quality praised when communication is strong
• Some variability noted on complex projects requiring tight oversight

Quality vs scale

Employee satisfaction as quality indicator:

• Label Your Data: 4.8 on Glassdoor, 98% would recommend
• CloudFactory: 3.6 on Glassdoor, reviews note “low pay and turnover”

Happy, well-compensated annotators produce better work. This explains the consistency patterns in customer reviews.

Bottom line: If precision is non-negotiable, and you need minimal internal QA, Label Your Data shows stronger consistency. If you have internal QA capacity and need massive volume, CloudFactory’s systematic processes can deliver quality at scale, with proper oversight.

Computer Vision or NLP? See Which Vendor Handles Your Tasks Better

The vendor that excels at one data type may struggle with another. Here’s where each vendor’s strengths actually lie.

2D, 3D, video & medical imaging annotation

Label Your Data:

• 2D image recognition tasks: Bounding boxes, polygons, semantic segmentation, instance segmentation
• 3D: Point clouds, LiDAR annotation, 3D annotation (bounding boxes)
• Video: Object tracking, action recognition, frame-by-frame labeling
• Medical imaging: HIPAA-compliant, DICOM files, specialized clinical annotation
• Geospatial: Satellite imagery, aerial photography analysis
• Real cases: Nodar delivered 60,000 polygon masks; Nex validated 15,000 skeletal images

CloudFactory:

• 2D: Standard object detection, keypoints, image classification, segmentation
• 3D: LiDAR available via “Workforce Plus” option (LineVision case: 1cm precision, 66% faster turnaround)
• Video: Frame annotation, object tracking
• Geospatial: Strong track record (Nearmap: 300,000+ hours processed)
• Medical: HIPAA-certified but not a noted specialty

For complex CV requiring precision (autonomous vehicles, medical AI, robotics), Label Your Data shows clear specialization. For high-volume standard CV pipelines, CloudFactory handles scale efficiently.

Text, entity & sentiment annotation

Label Your Data:

• Named entity recognition (NER), sentiment analysis, intent classification
• 55+ languages with native speakers for multilingual projects
• Domain-specific NLP (legal, financial, medical)
• Linguists for nuanced language understanding

CloudFactory:

• Text classification, entity extraction, sentiment analysis
• English-focused; limited multilingual support
• Partners with Datasaur for text/audio annotation

The NLP gap is significant. If your project involves multilingual data or domain-specific language, Label Your Data’s 55+ language coverage and linguistic expertise matter. For English-only standardized NLP, both are capable.

Once you add domain nuance — medical text, financial risk signals, or dense computer vision detail — Label Your Data consistently performs better because they use smaller teams of experienced experts with tighter human-in-the-loop QA.

Kevin Baragona Founder, Deep AI

Label Your Data vs CloudFactory Pricing Transparency: What You’ll Actually Pay

Label Your Data pricing structure

Model: Per-object (CV tasks) or per-hour (NLP/complex workflows). You choose what fits best.

Transparency:

• Most projects: $3,000-$10,000 range (Clutch data)
• Cost calculator available online for estimates
• No minimum order requirements
• Free pilot to validate quality and cost
• No hidden fees reported in reviews

Client feedback: “We found the cost per object pricing to be the most fitting for us” (retail AI user). Rated 4.9/5 on cost, with reviews noting “competitive pricing, good value for cost” and “no surprise fees.”

Contract terms: Flexible engagement (on-demand, short-term, or long-term). No long-term lock-in. Standard Net-30 payment terms.

The best advantage in this industry is the no-commitment part, as most platforms require high annual licenses. Label Your Data lets you start, pause, or scale without being locked into multi-year contracts.

CloudFactory pricing model

Model: Per-object (CV) or per-hour (NLP/data processing).

Two packages:

• Accelerated Annotation: AI-assisted CV labeling (pay per use, up to 5x faster)
• Workforce Services: Traditional managed teams (CV, NLP, data processing)

Transparency:

• Less transparent upfront (requires sales engagement for quotes)
• Annual contracts available with monthly billing
• Volume discounts for large projects
• G2 explicitly states: “Pricing details... isn’t currently available. Visit vendor’s website”

Client feedback: “Some businesses may find the pricing structure... less transparent or predictable.” One reviewer noted CloudFactory can be “a bit expensive compared to other platforms” though you’re getting QA oversight for that price.

Contract terms: Yearly agreements for ongoing needs (predictability for enterprise). Can start smaller but expect scoping calls.

Hidden costs to consider

Beyond per-label pricing, factor in:

• Internal QA time: If vendor quality varies, you’ll spend engineer hours fixing errors
• Onboarding delays: Longer ramp = delayed model deployment = missed deadlines
• Rework cycles: Poor initial quality = multiple revision rounds
• Total cost formula: (Price per label × volume) + (Internal QA hours × hourly rate) + (Time-to-production delay cost)

Bottom line: Label Your Data’s transparent pricing and free pilot eliminate budget surprises. CloudFactory requires sales engagement but may offer volume efficiency for massive ongoing programs. 

The cheapest per-label price isn’t the lowest total cost when quality consistency matters.

Onboarding Speed: How Fast Can You Start Labeling?

When your ML deployment has a hard deadline, onboarding speed directly impacts whether you ship on time or miss your window.

Label Your Data setup timeline

Week 1:

• Initial consultation to understand requirements
• Free pilot starts (sample batch)
• Team asks clarifying questions, produces initial annotations

Week 1-2:

• Pilot results delivered
• Feedback loop: refine guidelines based on your input
• Dedicated AM assigned

Time-to-first-batch: Days, not weeks. One client notes: “[Label Your Data] managed to finish the pilot test in one week.”

Scaling: Straightforward after pilot approval. No lengthy contracts to start. Client quote: “They can also work on weekends when necessary” to hit tight deadlines.

CloudFactory implementation process

Here’s the process overview:

• Phase 1: Requirements gathering, scoping discussions (Solutions Consultant/PM)
• Phase 2: Team assembly or designation to your project
• Phase 3: Workforce training on your specific task (especially for proprietary tools or unusual annotation types)
• Phase 4: Platform setup if using CloudFactory’s tools

Time-to-first-batch: Weeks for complex projects, with customers noting the onboarding phase as the longest part of the process.

Benefit: Once onboarding complete, the team is deeply trained. Nearmap example: weekly corner case discussions initially, then smooth operation. Case study: “Accelerated time-to-market by 6 months” (Zeitview) after ramp-up.

Time-to-first-batch comparison

Need to start quickly? Label Your Data’s pilot-first approach means faster validation and production start. 24/7 availability means work continues around the clock.

Complex multi-team setup? CloudFactory’s structured onboarding handles organizational complexity but requires upfront time investment.

Today, in fast-moving AI markets, faster annotation = faster model deployment = competitive edge:

• Label Your Data gives you two paths: self-serve CV platform for immediate start, or managed services for fast onboarding
• CloudFactory optimizes for long-term efficiency after initial ramp

CloudFactory shines in scale and speed, while Label Your Data often wins in depth and domain nuance. Choose the vendor whose workflow improves your model’s downstream performance, not just labeling throughput.

Ankit Sachan CEO, AI Monk Labs

Which Data Annotation Vendor Should You Choose: Label Your Data vs CloudFactory

Choose Label Your Data when

• Complex CV is critical: 3D/LiDAR, medical imaging, video tracking (Nodar: 60k polygons, no rework)
• Quality can't be compromised: 98%+ accuracy with SLAs, minimal client QA needed
• Transparent pricing matters: Clear per-object/hour rates, no minimums, free pilot
• Multilingual NLP required: 55+ languages with native speakers
• Fast deployment needed: Pilot in ~1 week, 24/7 availability, rush batches in 24 hours
• Tool flexibility important: Works in your existing stack (not vendor lock-in)
• Financial data involved: PCI DSS Level 1 certified

Choose CloudFactory when

• Massive scale required: 7,000+ analysts, 300,000+ hours for single clients
• High-volume, repetitive tasks: Standard CV pipelines at millions of images
• Full AI platform needed: Beyond annotation—training, deployment, monitoring
• Internal QA available: Can handle some quality variability with oversight
• Enterprise procurement fits: Annual contracts, longer sales cycles acceptable
• Long-term partnership: Dedicated pods build institutional memory over years

Still unsure? Start your free pilot now

Test before you commit. Label Your Data’s free pilot lets you validate quality on your actual data — no cost, no commitment. For precision-critical projects, testing is the only way to compare real performance.

About Label Your Data

If you choose to delegate data annotation, run a free data pilot with Label Your Data. Our outsourcing strategy has helped many companies scale their ML projects. Here’s why:

No Commitment

Check our performance based on a free trial

Flexible Pricing

Pay per labeled object or per annotation hour

Tool-Agnostic

Working with every annotation tool, even your custom tools

Data Compliance

Work with a data-certified vendor: PCI DSS Level 1, ISO:2700, GDPR, CCPA

FAQ

Can I test both vendors before committing?

Label Your Data offers a free pilot with no forced commitment. CloudFactory may offer paid pilots — negotiate upfront. Always test on your actual data before signing contracts.

What’s the real cost difference for a typical project (e.g., 10,000 images)?

Label Your Data offers transparent per-object pricing with quotes provided upfront. CloudFactory requires sales quotes and can be higher due to bundled QA/PM overhead. Factor in internal QA time if quality varies: cheapest per-label doesn’t mean the lowest total cost.

How long does it actually take to get my first annotated batch?

Label Your Data delivers pilot results in ~1 week; production starts immediately after. CloudFactory needs weeks for team onboarding/training but scales efficiently once ramped. If your deadline is tight, onboarding speed matters.

Which vendor handles 3D/LiDAR annotation better: Label Your Data vs CloudFactory?

Label Your Data specializes in 3D/LiDAR with proven case studies like Nodar autonomous systems. CloudFactory offers it via “Workforce Plus” but it’s not a core focus. For complex 3D work, test both on your point cloud data.

What happens if annotation quality doesn’t meet my standards?

Label Your Data offers SLAs: “Accuracy & deadlines or you don’t pay.” CloudFactory guarantees 100% QA review but requires active collaboration to maintain quality. Both will rework errors, but contractual guarantees reduce risk.

Do I need to use their annotation tools or can I use my own?

Label Your Data is tool-agnostic — works in your stack (CVAT, Labelbox, proprietary tools). CloudFactory has its own platform but can integrate with yours. If avoiding vendor lock-in matters, tool agnosticism is key.