The global AI Data Labelling market size was valued at USD 1.45 billion in 2024 and is projected to reach USD 13.11 billion by 2033, growing at a CAGR of 27.2% from 2025 to 2033.
Every successful AI model depends on data labelling, which works as a backbone. But most of the time, it creates confusion for organisations to choose between an in-house team for data labelling or outsourcing a specialised partner.
Both an indoor and outsourced data labelling offer several benefits to your AI projects. But the main concern is which one keeps the right balance between quality, scalability, and cost. This blog will let you make an informed decision for choosing the right source for label data.
What is Data Labelling?
Data labelling, also known as data annotation, is a process of identifying raw data(images, text files or videos)and assigning them one or more valuable labels for machine learning models.
Benefits of Data Labelling to AI Projects
Here’s how right data labelling is beneficial for your projects:
Improves accuracy:
Labelled data helps systems understand patterns and objects correctly. It ensures more accurate and consistent results.
Reduces errors:
Proper labelling ensures consistency across datasets, which minimises the risk of mistakes during processing and analysis.
Improve decision-making:
Clearly organised data allows better insights, which support more informed and effective business or project decisions.
Saves time:
Structured labelling reduces confusion and rework, speeding up project development and implementation.
Supports scalability:
High-quality labelled data makes it easier to expand projects and manage larger datasets efficiently without compromising results.
In-house vs Outsource Data Labelling Team
Whether it’s in-house data labelling or outsourcing data labelling, both have several benefits. But your final decision should depend on several aspects. Let’s have a look at the details of each choice:
In-house Data Labelling Team
In-house labelling refers to hiring a team of in-house annotators who work alongside data engineers and data scientists to perform data labelling tasks.
Based on current market data, in-house data annotators in the USA earn around $60,000 to $67,000 a year on average.
Advantages
Here are the benefits of hiring an in-house team for data labelling:
Direct Oversight: In-house team control of every step of data labelling by managing the entire process under the company’s policies and culture. It helps to monitor guidelines, quality standards, and feedback directly, which is crucial for complex datasets.
Data Security & Compliance: Ensure that all the sensitive data related to health, finance and exclusive product details remains within the internal ecosystem of the organisation. It is important for regulatory compliance (HIPAA, GDPR) and mitigating risk.
Domain Knowledge: Internal teams are familiar with the business objectives, data context, and domain, especially about complex industries such as healthcare, finance, and defence. It allows them to apply accurate and contextually relevant labelling without time-consuming practices.
Disadvantages
Let’s have a look at the drawbacks of the internal team:
- As compared to outsourcing, it is costly due to salaries and infrastructure.
- Limited internal capacity due to large datasets.
- Require continuous workforce management for repetitive tasks.
When to Choose?
Here are some conditions in which it is suitable to work with an internal data labelling team, instead of outsourcing:
- Want to secure sensitive data like HIPAA, PII, or other confidential information.
- Need to control the projects with complex guidelines and maintain policy compliance.
- Company-specific tools or processes can be handled perfectly by the internal team.
Outsource Data Labelling Team
An outsourced data labelling refers to a third-party service provider that allows expert AI teams that specialise in providing annotation or tagging of raw data.
Outsourced providers now deliver 69% of all labelling work and are expanding at 29.9% CAGR through 2030 as companies replace in-house teams with specialists that guarantee scale, quality and compliance.
Advantages
Below are some key benefits of outsourced data labelling:
Speed & Scalability: External providers can quickly adjust the AI team according to project needs. It ensures the smooth progress of the application for both small projects and large-scale applications.
Access to Specialised Expertise: Outsourcing data labelling can provide access to trained annotators with domain-specific knowledge such as multilingual labelling. It ensures the complex data is labelled accurately for improving the reliability of AI models.
Improves Data Quality: Outsourcing data labelling from reputable providers ensures that the labelled data is accurate and reliable. They typically use robust quality control processes, such as multi‑level reviews and standard quality checks, to reduce errors and inconsistencies.
Disadvantages
Here are some major drawbacks of outsourced data labelling:
- Security and compliance risk due to sharing sensitive data externally.
- Inconsistent labelling due to the interpretation guidelines by external annotators.
- Incorrect data labelling due to a lack of deep domain expertise.
When to Choose?
Here are some conditions in which outsourcing the data labelling team can be beneficial for your AI projects:
- If your project requires a massive amount of data which can’t be handled by the internal team.
- When your AI projects have tight deadlines and teams must work quickly.
- When you want to identify and reduce potential biases present in internal teams.
Wrapping Up
Whether you choose to handle data labelling with an internal team or outsource it to external providers, both approaches have a role in the AI development process. The right choice depends on project complexity, data sensitivity, and budget constraints.
Looking to outsource data labelling expertise? Connect with Gravity-Based, a leading AI consulting firm in Dubai. With our professional experience, we help companies to build reliable AI teams for their projects and provide expert consultancy to optimise AI strategy.
FAQs
What is data labelling?
Data labelling (or data annotation) is the process of adding meaningful tags or labels to raw data like images, text, audio, or videos.
Is outsourced data labelling costly?
Outsourcing can save money, especially for large-scale projects, because it avoids hiring, training, and infrastructure costs.
When to outsource a team for data labelling?
It’s good to outsource when you have lots of data, tight deadlines, or need expert labellers that your internal team doesn’t have.
What’s the difference between in team and outsourced data labelling?
In short, In-house teams give you control and understanding of your data. On the other hand, outsourcing offers speed, tools, and expert help.
What to consider before choosing in-house vs outsourcing data labelling?
Before choosing must consider your data type, project size, cost, budget, time, expertise, and quality needs.
Can I combine In-house and outsource data labelling?
Yes, hybrid models apply for internal teams for sensitive or strategic data, and outsourcing for massive or routine tasks for efficiency.
Why outsource a team for data labelling?
Outsourcing allows faster project completion, cost efficiency, access to domain expertise, scalability, and higher-quality labelled datasets.
