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Written by Anika Ali Nitu
Outsource labeling without growing your in-house team.
Data annotation outsourcing helps businesses turn raw data into AI-ready datasets without building large in-house labeling teams. This guide explains what tasks can be outsourced, how the process works, what it costs, and how to choose the right annotation service provider for your project.
Almost all useful AI technologies have a lot behind the scenes that goes unnoticed by end users.
A chatbot requires both good and bad examples of responses. The self-driving technology requires annotated images of pedestrians, cars, road signs, lanes, etc. A medical AI requires annotated medical images. Even recommendation engines require annotated data to be fed into them and this process is called data annotation.
With the adoption of AI becoming more widespread, firms are analyzing and generating more data than can be labeled within their organization. The Stanford 2026 AI Index reveals that AI adoption by organizations was up to 88%.
However, hiring a huge team of annotators from within the organization is not feasible. Firms would need to find annotators, train them, develop guidelines, check for accuracy, and balance the workload. This is where data annotation outsourcing comes in.
Rather than controlling all aspects of the process on their own, a company could collaborate with an external firm that provides everything from annotators to project management and other services that will enable the creation of a labeled dataset.
This article is designed to give you insights into data annotation outsourcing, tasks that can be outsourced, the costs involved, and how to select the right service provider.
Outsourcing of data annotation is the process by which an outside firm or team is employed to label or categorize data used in artificial intelligence and machine learning applications.
The raw data may include:
For instance, when an AI firm creates a computer vision model, it will give thousands of pictures depicting roads to its annotation vendor. Annotators can create bounding boxes for cars, pedestrians, bikes, and signs.
When it comes to natural language processing, annotators can detect customer intention, sentiment, named entities, or topics in text.
The resulting annotated dataset can be used to train, tune, test, or validate an AI model.
In brief:
Raw data → Annotation → Quality control → Training dataset → AI model
Here, “outsourcing” implies that the process of annotation is done by an external team rather than the in-house staff of the firm.
Image tagging looks simple until the scale grows and complexity becomes involved.
Tagging a couple hundred images by hand could prove possible for an in-house team. Tagging hundreds of thousands or millions of images presents another problem.
Here are just some of the reasons that companies resort to outsourcing.
The increase in the popularity of AI is resulting in an increasing need for structured training data.
The global AI data labeling market was estimated to be around $1.45 billion in 2024 and is expected by Grand View Research to reach around $13.11 billion by 2033.
More businesses will continue creating computer vision, NLP, generative AI, robotics, and other technologies, which means a greater need for labeled data.
An internal team could suffice for small projects, but it may create a bottleneck when there is an unexpected influx of work.
An outsourcing company would have the ability to hire more annotators, reviewers, or even additional shifts depending on project needs without the business having to go through the process of hiring again.
This could be especially useful for an AI startup that is working on its product release.
Not all annotation projects require the same type of expertise. For example, there is a vast difference between image classification and medical image annotation.
Experienced companies will have teams with different levels of specialization and even create a project-specific training process and quality control process.
Running an annotation process entails more than task allocation.
Someone needs to:
The burden of the process can be outsourced.
Typically, data scientists and machine learning engineers have better things to do than annotating thousands of data records.
Delegation of the more mundane annotation task helps the in-house team concentrate on other important tasks such as model development and testing.
Data annotation is not exclusive to AI startups. There are a variety of industries that leverage annotated data to develop or enhance machine learning algorithms.
AI-based solutions in the field of medicine can utilize x-rays, CTs, MRIs, pathology images, and other types of clinical data as well as medical records.
Annotation could assist in finding areas of interest, classifying images, structuring clinical data, etc.
According to the World Health Organization, AI is already being utilized in such fields as diagnosis and clinical care, drug discovery, disease surveillance, and management of health systems.
In the case of sensitive information, priority should be given to privacy, security, the qualifications of annotators, and compliance with statutory regulations.
Driverless cars and advanced driver assistance systems depend greatly on labeled data in terms of visual and sensor information.
Types of labeling could include:
Data annotation could help retailers in optimizing the following aspects of their operations by means of product images and customer data:
Banking institutions and fin-tech companies may employ annotation for document processing, fraud detection, customer service system management, risk assessment, and financial text classification.
Companies developing chatbots, search engines, recommendation engines, generative AI solutions, and other AI-powered software applications may need large quantities of annotated text and multimedia.
A capable outsourcing partner can support several data types and annotation techniques.
Text annotation can include:
Common image annotation methods include:
These techniques are widely used for computer vision applications.
Video annotation involves labeling objects or events across video frames.
It can support applications such as autonomous driving, surveillance, sports analytics, retail analytics, and activity recognition.
Audio annotation may involve:
This is especially relevant for self-driving cars, robotics, map-making, and other technologies that require an understanding of three-dimensional spaces.
In a business context, annotations can be made on invoices, forms, receipts, contracts, and other documents. The annotation process may vary depending on the requirements of the AI models.
The decision is not simply about whether outsourcing is cheaper.
It is about comparing the total operational burden of both approaches.
In-house teams can make sense where annotation is a long-term, strategically important process and where the necessary infrastructure is already in place.
Outsourcing may make more sense if annotation needs vary and a company wants to start an AI project without setting up a new department.
There is no set formula of outsourcing data annotation. It depends on the scale of the projects and degree of control by the company.
In this type, the service provider manages the process for a specific dataset or annotation project.
It is suitable for businesses that have a definite volume and deadline.
Under this arrangement, the client is provided with a dedicated team that works tirelessly on their projects.
This is suitable for organizations which require long-term project execution.
In this model, the service provider takes care of almost all the aspects, such as hiring, managing processes, and providing quality reports.
It suits organizations which do not want to run the annotation process themselves.
It involves keeping sensitive or highly specialized work within the organization and outsourcing other types of annotation work.
There is no single best provider since the choice is highly dependent on your needs for annotation, types of data, amount of data, required security level, budget, etc.
These are several providers that you may want to evaluate, starting with GigaBPO as a more realistic option in terms of outsourcing annotation for your business.
GigaBPO offers outsourced data annotation services through the BPO approach that includes text, image, audio, video, and multimodal data. Additionally, GigaBPO offers data cleansing, conversion, and processing.
If your business wants a managed approach to outsourcing rather than hiring and managing a team of data annotators internally, then GigaBPO can be considered as one of the options.
Currently, GigaBPO offers remote annotation teams starting at 4-8 per hour, as well as 24/7 availability and 7 days risk-free money-back guarantee. The pricing on a particular project varies according to its complexity, amount, quality and urgency.
It would be reasonable to request a sample or a pilot project during your evaluation of outsourcing annotation providers.
Appen offers enterprise image, text, video, and audio annotation solutions. Currently, the provider offers solutions such as image classification, object detection, sentiment analysis, transcription, and video action recognition. The company claims that its programs are available for over 80 languages and utilize quality processes including calibration, review, and inter-annotator agreement measurement.
iMerit specializes in managed AI data solutions and provides annotation of images, videos, 3D point clouds, audio, text, LiDAR, DICOM, and PDF files. Its current solution includes managed staffing and multi-step quality assurance.
TELUS Digital offers human-driven data annotation via the global AI workforce and Ground Truth Studio platform. According to the provider, there are more than a million AI specialists in its community and it delivers over two billion labels per year. The company offers annotation of text, images, audio, video, and geospatial data.
Scale AI provides enterprise data annotation through its Data Engine, supporting image, video, 2D, 3D, and other multimodal datasets. The platform combines human experts with automated workflows for data collection, curation, annotation, and model evaluation. Scale AI also emphasizes quality control through task-level review, dataset evaluation, and contributor assessment.
The main thing to note is that you shouldn’t pick a provider based solely on its presence in the “top companies” list. Your project needs will help you narrow down your choice.
The least costly option can turn out to be costly if you end up re-annotation your data due to low-quality labels.
Check out the following things before hiring the partner.
Ask how the vendor ensures accuracy.
Inquire about the use of:
Inquire how fast the vendor will be able to scale up its workforces in case the amount of your data doubles.
Some providers can cope with 10,000 images well, it does not mean that they will have no problems with 5 million.
In case you work on some specialized data, such as medical, financial, legal, automotive, etc., inquire whether the vendor has necessary domain experts.
Inquire where your data will be stored, who has an access to it and how access is guaranteed.
Always make sure that sensitive datasets go to safe places only.
Cheap prices will be of no use if there is any delay in the schedule of your model development.
Set your expectations about the turnaround time and escalations before the start of your project.
The ideal annotation partner should ensure that your team can easily convey issues with ambiguity, guideline changes, and evaluation of the project performance.
The best way to test the annotation partner is by initiating a pilot project.
Give various vendors the same dataset and check their performance according to:
This approach provides you with better data than a mere sales pitch.
If two vendors seem equally good, I’d usually go with the one that asks more thoughtful questions about the dataset. A good annotation partner won’t just follow instructions blindly. They’ll point out unclear cases early, before those small issues turn into hundreds or thousands of labeling mistakes.
There is no fixed price for data annotation outsourcing.
The price depends on different parameters such as:
Some companies provide hourly pricing; others charge per task, image, record, project, etc.
For instance, GigaBPO offers annotated teams starting from 4-8 per hour right now, although that price does not reflect average industry prices.
Also, the market estimates can greatly differ due to the fact that research companies use different definitions of data annotation and outsourcing markets. So, for instance, one 2025 market estimate suggested that the market of data annotation outsourcing services will reach $1.19 billion, while another 2026 market report predicted $3.85 billion in 2025.
It is important to remember that difference while comparing market statistics and vendor prices since those reports may have different definitions.
For your project ask for the detailed pricing based on a sample of your dataset.
While outsourcing saves time, there is always a risk of making mistakes.
Mistake 1: Deciding Solely on the Price
When outsourcing is cheap, but labels come with mistakes in your training data, the result will not be useful.
Mistake 2: Giving Poor Instructions
The instructions must be clear enough for the data annotators to label data consistently.
Provide instructions on how to annotate data and provide samples of both correct and wrong labels.
Mistake 3: Skipping Pilot Testing
Pilot testing allows you to spot possible issues before working with a bigger dataset.
Mistake 4: Not Paying Attention to Quality
You cannot assume that annotated data has already been checked.
Build some review steps into the process.
Mistake 5: Scaling Without Ensuring Quality First
More people do not mean better quality.
First, determine the annotation standard, check the workflow, find out what is the quality, and only after that you can scale.
Mistake 6: Neglecting Data Security
Data security is essential because the dataset might contain sensitive information about customers, finance, health, or business.
Discuss security needs before transferring data.
Mistake 7: Not Planning for Edge Cases
Examples from the real world do not fit perfectly into the annotation guidelines provided in the document.
Develop a procedure where the annotators can report difficult edge cases rather than guessing the answers.
The outsourcing of data annotation has become an important process in the AI development lifecycle.
The demand for accurate training data is steadily increasing due to the adoption of Artificial Intelligence (AI) across various sectors such as healthcare, automotive, retail, banking, software, and robotics. Market research indicates robust growth in the annotation industry.
For many organizations, the core question may not be whether they should annotate their data; rather, the real challenge lies in generating high-quality labels or tags at the necessary scale without creating unnecessary operational complexities.
This can be achieved through outsourcing, which enables organizations to ensure flexibility, specialized expertise, scalability, and quality control.
For businesses looking at the BPO-based model for data annotation, some of the companies you could consider include GigaBPO in addition to some of the top data annotation companies.
Data annotation outsourcing involves contracting with a third-party entity for the labeling and structuring of raw data that includes text, images, audio, video, documents, or sensor data for applications in AI and machine learning.
Businesses would choose to outsource their data annotations because they get access to skilled staff, scale their operations, lessen their workload, manage costs, and enable their AI teams to work on other activities.
These include text, images, videos, audio, documents, 3D point clouds, LiDAR, geospatial data, and multichannel datasets.
There is no definitive answer here as the cost involved will vary. The cost depends on the costs associated with hiring, training, managing, infrastructure, difficulty, quality needs, and volume of projects.
Analyze the quality of annotation, subject matter expertise, scale capacity, security, time frame, communication, pricing, quality assurance and past experience with comparable projects. Pilot project is also suggested before making a big commitment.
The cost varies based on the nature and intricacies of work being outsourced. Some organizations use hourly rates while some offer per task/project basis. GigaBPO currently offers remote annotation teams for 4-8 per hour.
Yes, it will be if the organization outsources it to a partner who is taking care of the security and access control. Organizations dealing with sensitive information need to verify the security, contractual aspects, access and certification policies of the outsourcing firm.
Yes. It is possible for a firm to do all the confidential or very specialized data annotations themselves but outsource everything else that is either high volume or routine.
It is a form of annotation which includes human intervention into the process of annotation which starts with some kind of automation of annotation and then is verified or corrected by humans.
This page was last edited on 24 August 2026, at 5:30 pm
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