Overview of Image Annotation Companies in 2025
As artificial intelligence (AI) continues to evolve, image annotation stands out as a foundational component in building accurate and robust computer vision models. From autonomous driving and medical diagnostics to retail analytics and precision agriculture, AI systems depend on massive volumes of well-labeled visual data – which is only possible through high-quality data annotation services.
The quality, speed, and scalability of annotation directly influence model performance. This makes the choice of an image annotation partner a mission-critical decision. In this article, we explore the top 10 image annotation companies in 2025, compare their capabilities, and offer guidance on managing cost, timelines, and quality when outsourcing.
Why Choosing the Right Image Annotation Vendor Matters
Behind every high-performing AI model is a foundation of reliable training data—and that foundation is only as strong as the annotation partner behind it. A misaligned vendor doesn’t just slow things down; it throws off label consistency, stretches timelines, and puts entire initiatives on shaky ground.
The right partner, on the other hand, brings more than just annotation accuracy. They step in as a true operational ally – helping teams ramp up efficiently, lock in data quality from the outset, and move from prototypes to production without missing a beat. This becomes especially critical in high-stakes use cases like medical imaging, machine learning pipelines, or vision systems where both precision and pace are non-negotiable.
That’s why this curated roundup of the top image annotation companies in 2025 isn’t just a checklist. It brings together vendors – both seasoned players and rising contenders – who’ve proven they can deliver under pressure, at scale, and across industry lines.
These providers don’t simply fill a functional role. They plug into internal workflows, smooth out operational friction, and give organizations tighter control over data pipelines from day one. Whether you’re looking for bespoke tools, full-service platforms, or custom-built workflows, these teams are equipped to carry AI efforts through every phase – from first test to full-scale deployment.
Learn more about what data annotation really means and why it matters in the context of building high-performance AI systems.
Top 10 Image Annotation Companies in 2025
1. LTS Global Digital Services (LTS GDS)
LTS GDS
LTS Global Digital Services, a subsidiary of LTS Group founded in 2016, has rapidly become a premier provider of data annotation, robotic process automation (RPA), and BPO services. Serving clients across Asia, North America, and Europe, LTS GDS specializes in high-accuracy training data for AI models in autonomous driving, coding, sport, healthcare, retail and agriculture.
Website: https://www.gdsonline.tech/
Headquarters: Hanoi, Vietnam
Why choose data annotation services from LTS GDS?
- Unmatched accuracy & experience: Over 500 completed annotation projects and 5 million data units processed with diverse techniques including image, video, text, audio, coding data, time series, and multimodal inputs, achieving up to 99% accuracy
- Robust 4-layer quality assurance: Self-check by vetted annotators, peer-cross review, vertical QA by leads, and final inspection by project managers or client reviewers, resulting in 97% customer satisfaction
- Scalable & customized workforce: Agile deployment with a large pool of multilingual annotators and reviewers, enabling rapid scaling for both small and enterprise-grade projects
- Flexible tech integration: Support in-house annotation tools and seamless integration with client platforms like CVAT, Label Studio, and Supervisely; offers custom tool development to fit client requirements
- Competitive pricing: Project-based or hourly pricing with flexible capacity; customized packages tailored for long-term and bulk annotation projects, optimized through Vietnam’s favorable personnel costs and tax incentives
Key clients:
Global enterprises and innovative startups in autonomous driving, healthcare, retail, and agriculture sectors. Notable clients include technology leaders and emerging AI ventures
Customer feedback:
LTS GDS holds a strong reputation on Clutch with a 4.8/5 rating, praised for professionalism, accuracy, and timely delivery
Certifications & recognition:
- ISO 27001;
- ISO 9001:2015;
- Vietnam Top ICT Companies; recognized as a leading annotation outsourcing provider by Vietnam Software and IT Services Association (VINASA)
Pricing model:
Flexible project-based or hourly pricing (time and materials) with transparent, competitive rates designed to maximize ROI without compromising quality
Best for:
Companies, startups, global enterprises seeking scalable, quality-first image annotation outsourcing companies
2. Sama
Sama
Sama, formerly Samasource, is a social enterprise focused on ethical AI data services with a mission-driven outsourcing model. Sama offers world-class data annotation outsourcing with up to 99.5% accuracy.
Website: https://www.sama.com/
Headquarters: San Francisco, USA & Nairobi, Kenya
Differentiations:
- Emphasis on social impact and inclusivity in workforce
- Human-in-the-loop annotation with robust QA achieving up to 99.5% accuracy
- Transparent impact metrics and ESG alignment
Certifications & recognition:
- B Corp certified;
- ISO 9001;
- ISO 27001;
- Featured in Deloitte’s Tech for Good
Pricing model:
Custom project-based pricing with detailed SLA agreements
Best for:
Organizations committed to ESG goals requiring ethical, high-quality annotation services.
3. Cogito Tech
Cogito Tech
Cogito Tech, founded in 2014, is known for image annotation for machine learning and sensor-heavy applications. Beyond typical domains like autonomous driving, they break into high-stakes areas such as healthcare, fintech, and industrial automation – where accuracy and context are deal-breakers.
Website: https://www.cogitotech.com/
Headquarters: New York, USA
Differentiations:
- High-volume capabilities in video, image, audio, and sensor data annotation
- Centers of excellence for computer vision and NLP
- API-based delivery with dedicated project management
Key clients:
Midsize to large AI companies and enterprises in healthcare, finance, autonomous driving, and technology sectors
Customer feedback:
Cogito Tech maintains a 5.0/5 rating on Clutch, praised for expertise, quality, and reliable communication
Certifications & recognition:
- ISO 9001;
- ISO 27001;
- Recognized in AI industry reports for annotation excellence
Pricing model:
Volume-based pricing with flat rates for enterprise accounts
Best for:
Mid-size to large AI firms requiring scalable and flexible annotation services.
4. Clickworker
Clickworker
Clickworker is a crowdsourced microtask platform managing over 6 million contributors worldwide, offering flexible image and text annotation.
Website: https://www.clickworker.com/
Headquarters: Essen, Germany
Differentiations:
- Large global crowdsourcing network for fast annotation
- Embedded QA and multilingual capabilities
- Cost-effective solutions for high-volume, lower-complexity tasks
Certifications & recognition:
- GDPR compliant;
- ISO-certified infrastructure
Pricing model:
Per-task pricing with upfront cost estimates
Best for:
Cost-conscious clients handling bulk, non-sensitive data annotation.
5. Ossisto
Ossisto
Ossisto, founded in 2017, provides digital outsourcing and virtual assistance alongside image annotation services, targeting startups and SMEs.
Website: https://ossisto.com/
Headquarters: New Jersey, USA (operations in India)
Differentiations:
- Flexible workforce model combining virtual assistants and technical staff
- Cost-effective packages tailored for smaller annotation budgets
- Emphasis on client support and fast turnaround
Key clients:
Small to mid-sized businesses across various industries, providing virtual assistant services to enhance sales, social media presence, and overall operational efficiency.
Customer feedback:
Ossisto holds a 5.0/5 rating on Clutch, praised for professionalism, accuracy, and timely delivery.
Certifications & recognition:
- GDPR compliant;
- Recognized as a leader in virtual assistance in emerging markets
Pricing model:
Hourly and task-based pricing suited for startups and SMEs
Best for:
Small to midsize businesses seeking affordable, scalable annotation services.
While this list highlights top performers in 2025, choosing the right partner depends on your specific use case, budget, and data needs. Read more on how to choose the best data annotation outsourcing company to make an informed, strategic decision.
6. Appen
Appen
Founded in 1996, Appen has firmly established itself as a frontrunner in delivering human-annotated data tailored for AI and machine learning projects. With over 25 years of industry experience, Appen powers diverse AI applications spanning natural language processing and computer vision for Fortune 500 companies.
Beyond its customized data annotation services, Appen offers a comprehensive range of off-the-shelf AI training datasets, curated from a vast global crowd workforce. These ready-made datasets are designed to help organizations quickly kickstart their AI model training and reduce time spent on data collection and preparation.
Website: https://www.appen.com/
Headquarters: Chatswood, New South Wales, Australia
Differentiations:
- Global crowdsourcing network with 1 million+ contributors across 170+ countries and 180 languages
- Proprietary annotation platform supporting text, image, audio, and video data
- Strong ethical AI commitments including fair pay and data privacy
Certifications & recognition:
- ISO 27001;
- Listed on the ASX;
- Recognized by Gartner and Forrester in AI training data markets
Pricing model:
- Volume-based pricing tiered by task complexity and quality control layers
- Flexible project-based quotes without minimum budget requirements
Best for:
Enterprises requiring large-scale data annotation with high accuracy and global reach.
7. iMerit
iMerit
Founded in 2012, iMerit is a social enterprise focused on impact sourcing, providing data annotation services globally with emphasis on healthcare, autonomous vehicles, and geospatial sectors.
Website: https://imerit.net/
Headquarters: Kolkata, India
Differentiations:
- Workforce trained in partnership with NGOs, emphasizing social impact
- Expertise in complex annotations including medical imaging and LiDAR
- Proprietary workflow platforms combined with multi-tiered quality assurance
Certifications & recognition:
- ISO 9001;
- ISO 27001;
- Recognized as an Impact Sourcing Champion by IAOP
Pricing model:
Custom project-based pricing tailored for complex and regulated domains
Best for:
Organizations needing high-quality annotation in sensitive domains such as healthcare and autonomous driving.
8. Scale AI
Scale AI
Scale AI, headquartered in San Francisco, is known for its advanced tooling and API-first approach, serving enterprise and defense clients with rapid annotation services.
Website: https://scale.com/
Headquarters: San Francisco, USA
Differentiations:
- High-speed annotation powered by AI-assisted pre-labeling and human-in-the-loop QA
- Pre-labeled datasets and synthetic data generation capabilities
- Trusted by OpenAI and U.S. Department of Defense for security-sensitive projects
Certifications & recognition:
- SOC 2;
- ISO 27001;
- “Tech Pioneer” by the World Economic Forum
Pricing model:
- Usage-based pricing with pay-as-you-go and enterprise volume commitments;
- Pricing per data unit labeled
Best for:
Advanced AI image annotation tasks. High-speed, large-scale image annotation projects in regulated or security-sensitive sectors.
9. CloudFactory
CloudFactory
CloudFactory, founded in 2008, combines managed workforce solutions with AI and data annotation services, focusing on scalability and rapid onboarding.
Website: https://www.cloudfactory.com/
Headquarters: Kathmandu, Nepal
Differentiations:
- Hybrid workforce model blending skilled in-house teams with distributed workers
- Seamless integration with enterprise systems via robust APIs
- Advisory services to optimize annotation strategies
Certifications & recognition:
- ISO 27001;
- Recognized by Deloitte and Forbes for ethical outsourcing
Pricing model:
Subscription and usage-based pricing depending on SLA levels and project scale
Best for:
Companies require rapid scaling of annotation projects with workforce flexibility and quality assurance.
10. Labelbox
Labelbox
Labelbox is a U.S.-based data annotation platform company offering enterprise-grade tools for internal teams or third-party annotation operations.
Website: https://labelbox.com/
Headquarters: San Francisco, USA
Differentiations:
- End-to-end data training infrastructure including model-assisted labeling and analytics
- Plugin system enabling ML model feedback loops
- SaaS and on-premise deployment options with cloud integrations (AWS, Azure, GCP)
Certifications & recognition:
- SOC 2 Type II certified;
- Recognized in Gartner’s Hype Cycle for Data Science
Pricing model:
Tiered SaaS pricing with seat licenses and usage-based compute/storage fees
Best for:
Enterprises with internal ML teams seeking robust, scalable annotation toolchains.
How Long Does an Image Annotation Project Take?
The timeline for an image annotation project can vary significantly depending on several factors, including the volume of data, the complexity of annotations required, and the type of AI model being trained.
In general, a typical image annotation project can take anywhere from a few days to several weeks, or even months, depending on the project’s scale.
Explore the three primary types of image segmentation here to understand their implications on project duration and resource allocation.
Key determinants
- Data complexity: Intricate annotations (e.g., polygonal segmentation or 3D bounding boxes) demand greater precision and longer durations compared to simpler tasks like bounding boxes.
- Quality assurance: Industries with zero-tolerance for errors (such as healthcare or autonomous vehicles) require multiple validation cycles, extending project timelines.
- Team expertise & size: Highly specialized annotation requires experienced professionals, which can influence throughput and deadlines.
- Technological tools: Adoption of semi-automated annotation platforms can accelerate progress, though human oversight remains indispensable for quality control.
What’s the Cost of Outsourcing Image Annotation?
Outsourcing image annotation can be a cost-effective solution, especially when managing large volumes of data, but understanding the pricing models and key cost drivers is essential for optimizing value. Check out the table below for details:
Major cost drivers
- Annotation complexity: Advanced tasks like semantic segmentation or 3D annotation incur premium rates due to specialized skills and increased effort.
- Domain specificity: Medical, scientific, or autonomous vehicle data annotation commands premium pricing due to specialized knowledge and quality controls.
- Data volume: Larger datasets offer opportunities for volume discounts but require greater upfront investment.
- Turnaround speed: Accelerated timelines typically attract surcharges reflecting resource intensification.
- Geographic labor costs: Outsourcing to regions with lower operational expenses often reduces costs without compromising quality.
How to Optimize Cost Efficiency?
To optimize cost efficiency in an image annotation project, consider the following:
- Develop precise, comprehensive annotation guidelines to minimize revisions.
- Leverage automation tools where feasible to expedite routine tasks.
- Select outsourcing partners in cost-competitive regions with proven expertise.
- Balance quality control rigor to prevent unnecessary overhead while maintaining high standards.
Best Practices for Collaborating with an Image Annotation Provider: A Step-by-step Guide
Successfully partnering with an image annotation provider requires clear communication, thorough planning, and a mutual understanding of project goals. Following a structured approach ensures that your image annotation projects are completed on time, within budget, and to the highest standards of quality. Below is a step-by-step guide for optimizing collaboration with your image annotation provider.
Step 1: Conduct an initial consultation
Before kicking off any project, it is essential to have an in-depth conversation with your provider to align expectations. During this consultation, discuss your project goals, the specific type of annotations required, the volume of images, and any unique challenges your industry faces.
Action points
- Explain the context and purpose of the annotations (e.g., computer vision, AI training, or medical imaging).
- Define the project scope, including the number of images, type of annotations, and any deadlines.
- Discuss any specific industry requirements or standards (e.g., healthcare regulations or autonomous vehicle standards).
Why it matters
Establishing clear project parameters upfront minimizes the risk of misunderstandings and ensures that both parties are aligned on deliverables and timelines.
Step 2: Create detailed annotation guidelines
Clarity is key to ensuring consistent and accurate annotations. Providing your provider with detailed and comprehensive annotation guidelines will streamline the process and reduce the likelihood of errors or revisions.
Action points
- Create visual examples and definitions for each annotation type (e.g., bounding boxes, segmentation masks, image classification).
- Provide rules on how specific objects or features should be identified and labeled.
- Include information on the acceptable quality standards and any specific tools or software preferences.
Why it matters:
Well-defined guidelines prevent misinterpretation and reduce the number of revisions needed, ensuring a higher quality and faster turnaround time.
Step 3: Select the right provider for your needs
Choosing the right image annotation provider depends on the complexity of your project and the level of expertise required. For example, if you are working in a highly specialized field like medical imaging or autonomous vehicles, ensure that the provider has experience in your sector.
Action points
- Review the provider’s past projects, client testimonials, and industry-specific experience.
- Ensure the provider’s team is equipped with the right skills for the complexity of the task (e.g., advanced techniques like semantic segmentation or 3D bounding boxes).
- Consider their experience with every competent image annotation tool that aligns with your project’s needs.
Why it matters:
A provider with the right expertise will deliver faster, more accurate results, and help you avoid potential delays or quality issues.
Step 4: Establish regular communication and progress tracking
Ongoing communication throughout the project is essential to ensure that everything is proceeding according to plan. Setting up regular check-ins and progress updates helps to identify and resolve issues before they escalate.
Action points
- Schedule periodic meetings to review project milestones, assess progress, and discuss any challenges or concerns.
- Use project management tools or platforms to track real-time progress and monitor key performance indicators (KPIs), such as turnaround time and accuracy.
- Set clear expectations for turnaround times on revisions or additional requests.
Why it matters:
Regular communication helps to keep the project on track, provides an opportunity for immediate feedback, and ensures that the provider remains aligned with your evolving needs.
Step 5: Monitor quality assurance and feedback loops
Quality assurance (QA) is a crucial part of any image annotation project. To ensure the annotations meet your standards, implement a multi-tiered review process and provide feedback to your provider regularly.
Action points
- Define your QA process, which may include multiple rounds of reviews (e.g., self-checks, cross-reviews, and final inspections).
- Encourage iterative feedback during the project, especially if complex annotations are involved.
- Track error rates and work with the provider to correct discrepancies promptly.
Why it matters:
A robust QA process ensures that the final product meets your expectations for accuracy and consistency, while also reducing the need for costly revisions later in the project.
Check out LTS GDS’s QA process here
Step 6: Provide constructive feedback and foster continuous improvement
Once the annotations are delivered, it’s important to review the work thoroughly and provide detailed feedback. Constructive feedback helps your provider refine their approach and ensures that future projects meet your quality expectations.
Action points
- Review the annotated images for accuracy, consistency, and alignment with the initial guidelines.
- Provide specific, actionable feedback on any discrepancies or areas for improvement.
- If necessary, hold a debrief meeting to discuss lessons learned and optimize processes for future collaborations.
Why it matters:
By fostering a culture of continuous improvement, you ensure that both you and your provider can evolve together, making future collaborations more efficient and successful.
Step 7: Evaluate the completed project and plan for future work
Upon project completion, it’s crucial to assess the overall success of the partnership and determine any adjustments for future collaborations. This evaluation can provide valuable insights into areas of improvement and new opportunities for collaboration.
Action points
- Review the final deliverables against the initial goals and expectations.
- Assess the provider’s adherence to timelines, cost, and quality standards.
- Determine if there are any additional needs for future image annotation projects and discuss long-term partnerships.
Why it matters:
Evaluating the project’s success allows both parties to make data-driven decisions for future collaborations, ensuring that the relationship remains productive and aligned with your business goals.
Final Thoughts on Selecting An Image Annotation Services Provider
Choosing the right image annotation partner is crucial for the success of your project. The quality, accuracy, and timelines of your annotations directly impact the performance of your AI and machine learning models. By following best practices such as clear communication, providing detailed guidelines, and ensuring regular quality checks, you can maximize the success of your collaboration.
At LTS Global Digital Services, we specialize in providing high-quality, efficient, and secure image annotation services tailored to meet your unique business needs. Whether you are looking for a comprehensive quote, a pilot project, or further information on how we can help streamline your image annotation processes, contact us today.
Let us help you achieve your business goals with precision and expertise!
FAQs About Image Annotation Companies
What are the benefits of hiring an image annotation company?
Hiring a professional image annotation company can significantly enhance your AI projects.
- Access to specialized expertise and proven annotation workflows
- Faster turnaround times due to trained staff and automated quality checks
- Cost savings by avoiding investment in in-house annotation infrastructure and training
- Ability to meet industry-specific compliance and accuracy standards, especially in regulated fields
How do companies ensure quality control in annotation?
Quality control is critical to maintain the accuracy and reliability of annotated data.
- Implement multi-layered quality checks including self-reviews, peer reviews, and expert audits
- Use AI-driven automated tools to validate data consistency
- Follow standardized annotation guidelines and conduct regular team calibration sessions
- Provide accuracy guarantees, often ranging from 95% to 99.9%, supported by continuous client feedback
How much do image annotation services cost?
The cost of annotation services depends on several factors related to project scope and complexity.
- Pricing varies by annotation complexity, volume, and project scope
- Common pricing models include per-image, hourly rates, or fixed project fees
- Basic 2D annotation typically costs between $0.05 and $5 per image
- More complex tasks, such as 3D annotation or medical labeling, can range from $1 to over $10 per image
What tools do annotation companies use?
For internal pipelines, businesses can leverage free image annotation tools like CVAT or Label Studio. To scale efficiently, consider image annotation outsourcing companies with flexible SLAs and quality control.
Popular tools include:
- Labelbox – Flexible, cloud-based tool for computer vision workflows.
- V7 – Known for automated workflows and AI-assisted labeling.
- CVAT (Computer Vision Annotation Tool) – Open-source and customizable, often used for bounding boxes and segmentation.
- SuperAnnotate – Integrates AI-assisted labeling, model management, and online image annotation support.
- Supervisely – Useful for 3D annotation, semantic segmentation, and team management.
