AI isn’t built on futuristic code alone. Behind every impressive demo are thousands upon thousands of hours spent teaching machines how to understand the world. So, let’s find the best data annotation outsourcing companies to ease this burden.
The stakes are high. When done well, annotation produces high-quality training data that powers smarter decisions, leaner operations, and the faster deployment of AI and machine learning solutions.
Done poorly, it leads to biased models, wasted budgets, and AI systems that fail under real-world pressure.
In this article, we’ll break down the top outsourcing companies, what sets them apart, and how to choose a partner that delivers not just accurate labeling, but also scalability, security, and business impact.
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Why Data Annotation Outsourcing Matters for AI Success
Imagine training a world-class athlete but feeding them a sloppy diet. No matter how gifted they are, their performance won’t improve. The same logic applies to artificial intelligence: without top-tier training data, even the most advanced AI and machine learning models stumble.
That’s where data annotation services come in.
Annotation is the behind-the-scenes work that teaches AI systems to see, hear, and read with precision. Think of bounding boxes on cars in an image, sentiment analysis on customer reviews, or 3D point cloud labeling in autonomous driving.
The catch? Annotation is slow, repetitive, and, when done poorly, downright dangerous for your AI/ML models. Inconsistent labels lead to biased models, which in turn result in poor decisions.
So, why do companies outsource data annotation instead of building in-house teams?
- Scalability without the headache. Managing thousands of annotators internally is difficult at best. Outsourcing providers already have the necessary workforce, tools, and quality assurance systems in place.
- Cost efficiency. Specialized vendors can cut annotation costs by up to 70% compared to doing it internally. That’s money you can reinvest in R&D or deployment.
- Domain expertise. From medical imaging to Natural Language Processing (NLP), outsourcing firms often maintain niche teams who understand your data types better than a generic in-house hire.
- Data security. Reputable providers back up their claims with certifications like ISO 27001, SOC 2, or GDPR compliance. For sensitive audio data or healthcare imagery, strong information security management systems aren’t optional; they’re non-negotiable.
- Faster time-to-market. Providers already have annotation workflows, validation layers, and reporting in place. They can turn your raw data into labeled datasets without slowing your project roadmap.
The data annotation outsourcing market is also becoming increasingly competitive. Analysts project that the image annotation services market alone will grow to approximately $8.5 billion by 2033, driven by industries such as healthcare, automotive, and robotics.
At its heart, annotation isn’t about labeling pictures or documents. It’s about fueling AI projects with the relevant data they need to function reliably in the real world. And like fuel, quality and consistency matter more than sheer volume.
READ MORE: Data Annotation vs Data Labeling: Key Differences, Use Cases, and Why It Matters
The Best Companies Offering Data Annotation Services
Now that we have a better understanding of data annotation and its importance, here are our top nine picks for outsourcing partners.
Disclaimer: These nine companies, chosen for their reputation, service range, global reach, and quality in data annotation across various data types, represent leading providers trusted by enterprises worldwide.
1. Scale AI
Best For: Enterprises that need massive, complex data annotation services for autonomous driving, computer vision, and generative AI projects.
Scale AI is one of the most recognized names in the data annotation outsourcing market. They offer AI data annotation services for a diverse range of applications, including self-driving cars and large language models.
Scale annotates data (video, text, imagery, 3D sensor streams) with speed, volume, and precision, using human intelligence and automation. It’s a partner for enterprises needing scalability and accuracy.
Standout Features: Expertise in bounding boxes, semantic segmentation, object detection, 3D point cloud, and NLP tasks.
Company Rating: 3.3/5 (Glassdoor Verified)
Pros:
- Proven partner for Fortune 500 companies and government AI initiatives.
- Known for speed, scalability, and advanced computer vision model support.
- Offers strong tooling and APIs for seamless integration into enterprise AI projects.
Cons:
- Premium pricing is likely overkill for startups or mid-market companies.
- Concerns about neutrality due to partnerships with big AI labs.
2. 1840 & Company
Best For: U.S. companies that want a partner to build, manage, and scale global annotation teams with end-to-end workforce support.
At 1840 & Company, we take a different approach to data annotation outsourcing. With a global presence in over 150 countries, we provide access to vetted professionals who can handle image, video, audio, and text annotation, while also managing payroll, compliance, and workforce scalability.
We operate as an industry-agnostic annotation company supporting various AI applications, from NLP to 3D point cloud labeling. Our AI-powered Talent Cloud swiftly connects businesses with suitable professionals, typically within two weeks, offering high-quality training data and long-term workforce solutions.
Standout Features: Proprietary AI-powered Talent Cloud for rapid vetting and matching. Expertise across all data types, including image annotation services, video data, audio data, and document processing.
Company Rating: 4.8/5 (Clutch Verified)
Pros:
- Access to vetted annotation professionals in over 150 countries.
- Reduces costs by up to 70% while maintaining quality assurance and accurate labeling.
- Handles payroll, compliance, and HR, in addition to annotation tasks.
Cons:
- Not a niche-only annotation provider that relies on staffing flexibility rather than one annotation platform.
- May not appeal to companies seeking a pre-built, tool-only annotation solution, such as Labelbox.
3. Sama
Best For: Companies that want reliable data annotation outsourcing while supporting ethical, socially responsible supply chains.
Sama (formerly Samasource) is one of the most recognizable names in annotation outsourcing services, blending technology with a social impact mission. As a Certified B-Corp, they create employment opportunities in underserved regions, particularly in Africa.
Their teams handle image annotation, video annotation, audio annotation, and text annotation, with particular strengths in computer vision projects, including object detection, object tracking, and 3D point cloud labeling.
Standout Features: Expertise in video annotation services, image annotation services, and AI data annotation services for high-growth industries like autonomous vehicles and robotics.
Company Rating: 4.2/5 (Comparably Verified)
Pros:
- Strong reputation in ethical outsourcing and workforce development.
- Capable of scaling data annotation projects across multiple data types.
- Good fit for enterprises that value both performance and social responsibility.
Cons:
- Past scrutiny over working conditions for certain content moderation services and annotation tasks.
- Pricing may be higher than that of some low-cost annotation companies, reflecting their commitment to ethical standards and investments in training and development.
4. iMerit
Best For: Enterprises in highly regulated or precision-focused sectors such as healthcare, autonomous driving, and financial services.
iMerit is one of the most established names in the data annotation outsourcing industry. With teams trained in a range of services, including image, video, text, and audio annotation, they have established a reputation for handling sensitive and regulated industries.
Think medical imaging, autonomous vehicles, and financial services, areas where quality assurance, compliance, and accurate labeling are paramount.
Standout Features: Offers advanced annotation for computer vision models, 3D point clouds, and semantic segmentation, making it a trusted partner for complex AI projects.
Company Rating: 3.7/5 (Glassdoor Verified)
Pros:
- Proven track record with Fortune 500 companies and tech leaders.
- Specializes in high-stakes annotation where labeling accuracy directly impacts safety or compliance.
- Offers scalable solutions across multiple data types with global delivery centers.
Cons:
- Pricing sits at the higher end, reflecting their specialization and quality standards.
- It may be more infrastructure-heavy than what early-stage startups need for smaller data annotation projects.
5. Label Your Data
Best For: Companies in regulated industries that require high-quality training data with strict information security management systems in place.
Label Your Data is a boutique option that focuses on providing secure, scalable, and customizable annotation services. With an emphasis on compliance, they are trusted to handle sensitive raw data, including healthcare imagery, financial records, and government information.
Their offerings cover image, video, text, and audio annotation, making them versatile across industries. What sets them apart is their commitment to data security, ensuring that clients’ information is protected throughout the annotation pipeline.
Standout Features: Emphasis on security and compliance, including adherence to GDPR regulations, and provides tailored workflows for AI data annotation services.
Company Rating: 5/5 (Clutch Verified)
Pros:
- Strong compliance standards (ISO/IEC 27001, PCI DSS, GDPR).
- Flexible engagement models suitable for different data annotation projects.
- Capable of delivering secure annotated datasets across multiple data types.
Cons:
- May lack the scale of larger annotation outsourcing giants, such as Appen or iMerit.
- Best suited for projects where security and precision matter more than high-volume cost-cutting.
6. KeyMakr
Best For: Businesses with highly specialized image annotation and video annotation projects that demand precision over sheer volume.
KeyMakr is a specialized choice that delivers precise labeling for computer vision projects. Unlike large-scale generalist vendors, they emphasize customization and quality, handling tasks such as semantic segmentation, bounding boxes, object detection, and video annotation with a human-in-the-loop approach.
They’re often chosen by companies that require high-quality training data for AI/ML models in industries such as retail, smart home technology, and security, where fine details make all the difference.
Standout Features: Strong customization capabilities for complex data types, from 3D point cloud labeling to pixel-level segmentation.
Company Rating: 4.9/5 (Software Finder Verified)
Pros:
- Known for meticulous, accurate labeling and annotation services.
- An excellent choice for smaller-scale, yet detail-heavy, AI data annotation tasks.
- Offers flexibility in designing annotation workflows to match client needs.
Cons:
- Limited scalability compared to enterprise-level providers like Scale AI or iMerit.
- Focused primarily on computer vision models, making it less suitable for heavy natural language processing or audio annotation tasks.
7. Playment (TELUS Digital)
Best For: Enterprises requiring high-volume image annotation and video annotation projects, especially in autonomous driving and retail.
Playment, now part of TELUS Digital, is a strong player in data annotation outsourcing. The company built its reputation by providing image and video annotation services, as well as 3D point cloud labeling, for industries such as autonomous vehicles, robotics, and retail analytics.
Since its acquisition, Playment has gained access to TELUS International’s global workforce and infrastructure, making it a more scalable option for enterprises that need high-quality training data delivered quickly.
Standout Features: Expertise in object detection, object tracking, semantic segmentation, and bounding boxes. The integration with TELUS International provides clients with a global presence, as well as enterprise-grade security and compliance.
Company Rating: 4.9/5 (Clutch Verified)
Pros:
- Strong capabilities in computer vision models and 3D annotation.
- Backed by TELUS International’s scale and infrastructure.
- Good balance between precision and scalability for large AI projects.
Cons:
- As part of a larger corporation, it may feel less flexible for smaller clients.
- Focused heavily on vision-related annotation services, with less emphasis on text or audio data.
8. Turing
Best For: Companies that need domain experts for complex or sensitive data annotation projects, rather than high-volume, generic annotation.
Turing is best known as a global talent marketplace for remote engineers. But it has expanded into providing annotation outsourcing solutions as demand for AI data annotation services has skyrocketed.
Instead of operating like a traditional data annotation company, Turing connects businesses with vetted annotation professionals who can handle complex tasks. This approach provides enterprises with direct access to skilled human talent, rather than relying solely on managed services.
Standout Features: Talent-first approach that prioritizes domain expertise and skill matching. Strong network for annotation tasks beyond simple image annotation services.
Company Rating: 5/5 (Clutch Verified)
Pros:
- Access to vetted experts for specialized AI data annotation needs.
- A flexible model that helps clients scale annotation teams up or down quickly.
- Well-suited for annotation tasks requiring human intelligence and domain-specific knowledge.
Cons:
- Less of a turnkey annotation services provider compared to Sama or iMerit.
- Best fit for enterprises willing to manage workflows directly rather than fully outsourcing them.
9. Surge AI
Best For: AI labs and NLP-focused startups that need reliable annotation services for text-heavy projects and natural language processing.
Surge AI has quickly become a go-to partner for building large language models and conversational AI systems. Unlike generalist vendors, Surge specializes in text annotation, keyword tagging, and reinforcement learning from human feedback (RLHF).
These processes fine-tune how chatbots, virtual assistants, and generative AI systems respond to user input. Their talent pool is trained to handle linguistic nuance, making them especially effective in sentiment analysis and entity recognition tasks.
Standout Features: Specialization in text annotation and NLP labeling, including advanced RLHF for AI/ML models.
Company Rating: 4/5 (Glassdoor Verified)
Pros:
- Deep expertise in natural language processing NLP and conversational AI.
- Strong partner for companies developing generative AI and large language models.
- Quick to scale annotation teams for text annotation and review projects.
Cons:
- Primarily focused on language tasks, so less suited for large-scale image annotation services or video annotation services.
- Reliance on contractor networks has raised questions about long-term workforce consistency.
How to Choose the Right Data Annotation Outsourcing Partner
Everyone promises scalability, accuracy, and “cost-effective solutions,” but not all can deliver on these promises once the project begins. The wrong match means wasted budgets, delayed timelines, and AI models that underperform.
Here are the key factors to keep in focus when you’re deciding which data annotation outsourcing company deserves your signature on the contract:
1. Quality Control Over Speed
It’s tempting to prioritize volume, but speed without quality assurance is a false economy.
Look for providers that utilize multi-step validation, employ gold-standard checks, or conduct model-assisted reviews. High labeling accuracy translates directly into more accurate predictions and a lower risk of biased AI and ML models.
2. Security Isn’t Optional
When your datasets contain sensitive video data, audio data, or healthcare images, you need more than a promise of safety.
Verify the presence of an information security management system (ISO 27001), SOC 2 compliance, GDPR adherence, and reliable encryption for data in transit and at rest. Those who skimp on data security expose you to regulatory risk and reputational damage.
3. Domain Expertise Matters
Not all data types are created equal. Medical imaging requires different annotation skills than those needed for sentiment analysis in customer reviews or object tracking in retail analytics.
The right partner will have proven domain expertise, with annotators explicitly trained in the needs of your industry.
4. Technology and Tools
Even with human annotators, tools play a crucial role in the process. Evaluate whether the provider offers efficient platforms for bounding boxes, semantic segmentation, point cloud, and text annotation.
A good vendor won’t just label data. They’ll utilize technology to minimize errors, expedite workflows, and ensure seamless integration with your ML/Ops pipeline.
5. Transparency in Pricing and Reporting
Executives hate surprises, especially the expensive kind. Look for straightforward and cost-effective pricing models, as well as regular reporting. The best vendors provide visibility into progress, error rates, and annotated datasets, so you’re never left guessing.
6. Scalability Without Sacrificing Quality
Small pilot projects are easy. The challenge comes when your data annotation projects balloon to millions of images, documents, or hours of audio annotation.
The right vendor should demonstrate the ability to scale while maintaining consistency across every annotation pass.
FAQs About Data Annotation Outsourcing
Before we wrap up this look at outsourced data annotation services, let’s answer some of the most popular questions about the topic.
Is Data Annotation All Coding?
No, data annotation isn’t all coding. It’s human-powered, primarily involving the labeling of images, text, audio, and video to create labeled datasets that train and improve AI models.
What Are the 5 C’s of Data Analytics?
The 5 C’s of data analytics are Clean, Curate, Contextualize, Communicate, and Comply. This ensures data is accurate, organized, meaningful, well-presented, and aligned with security and compliance standards for reliable AI and machine learning models.
Can You Use AI for Data Annotation Tech?
Yes, AI can assist in data annotation through automation tools for bounding boxes, semantic segmentation, and text annotation, but human oversight ensures accuracy, context, and high-quality training data.
Final Thoughts
The takeaway? No two annotation companies are alike. Some excel in volume and scalability, others in domain expertise, and some, stand out for combining annotation services with end-to-end workforce support, compliance, and global reach.
For executives, the decision isn’t simply about finding someone who can label data. It’s about identifying a partner who ensures your AI projects are fueled by precise labeling, robust security, and the flexibility to scale as your ambitions grow.
If you want a partner who can deliver vetted professionals, manage data security and compliance, and help scale your workforce globally, 1840 & Company is here to help.
With our AI-powered Talent Cloud and global presence in 150+ countries, we’ll connect you with the right experts to transform your raw data into the high-quality data your AI/ML models need to succeed. Schedule your consultation today!
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