Looking to outsource data annotation but not sure which provider to choose? You may be wondering whether price alone is enough to compare providers, or how to evaluate the quality of the work you will receive.
Cost is important, but it is not the only factor that can affect the quality and consistency of your training data. The experience of the annotators, quality control processes, scope of services, and communication throughout the project can all make a difference.
This article covers four key factors to consider when comparing data annotation providers, along with practical questions to ask before choosing a provider.
Data annotation can be handled in-house by engineers or other internal team members. However, as the volume of data increases, so does the burden of recruiting annotators, training them, and maintaining quality.
When engineers are responsible for annotation, the work can also take time away from their core responsibilities, such as AI and machine learning development.
Outsourcing may be worth considering when you face challenges such as:
Outsourcing itself does not automatically solve these issues. The key is to choose a provider that matches your data, project requirements, and quality expectations.
One of the first questions to ask a potential provider is: Who will actually perform the annotation work? Annotation quality depends not only on how much data can be processed, but also on how consistently annotators interpret and apply your labeling criteria.
Providers may use different types of workers, including AI vendor engineers, part-time or temporary workers, crowdsourced workers, BPO teams, or specialized employees. The right approach depends on the volume and complexity of your data and the level of quality you require.
Ask about the experience of the people who will work on your project and the training they receive before they begin.
For example, blurry images, images containing many objects, or datasets that require nuanced judgment can create room for interpretation. If different annotators apply different interpretations, the consistency of the resulting dataset can suffer.
When requesting a quote, look beyond the number of workers. Ask who will perform the work, how they are trained, and how difficult or ambiguous cases will be handled.
The way annotated data is reviewed is another important factor when evaluating a provider. Quality control processes can vary, including single review, double review, triple review, 100% inspection, sample-based review, and spot checks.
However, simply hearing that a provider uses double review is not enough to evaluate the quality of the process.
Ask who performs the review, what criteria they use, and how much of the data is checked.
For example, is every annotation reviewed, or is only a sample checked? Is the reviewer different from the original annotator? If reviewers identify different interpretations, how are those decisions resolved and reflected in the annotation rules?
When comparing providers, look beyond whether a quality check exists. Understand how the quality control process is actually operated.
Per-unit pricing is one of the easiest ways to compare data annotation providers, but it does not necessarily tell you the total cost of a project.
Depending on the provider, a quote may include more than the annotation work itself, such as:
The scope included in a quote can differ from one provider to another. As a result, the provider with the lowest unit price is not necessarily the provider with the lowest overall project cost.
When requesting quotes from multiple providers, compare what is included in the price—not just the price per annotation. Making the scope of work consistent across quotes will give you a more meaningful comparison.
Annotation projects often uncover questions that are difficult to anticipate before work begins. For example: Is this object within the scope of annotation? Where should the boundary of an object be drawn? How should an unexpected case be handled?
If these questions remain unresolved until the end of the project, inconsistencies may be discovered after delivery, potentially leading to revisions or rework.
Before selecting a provider, ask how communication will be handled once the project is underway. For example:
Annotation rules cannot always be fully defined in advance. A strong project process should allow questions and edge cases to be identified as the data is reviewed and, when necessary, incorporated into the annotation guidelines.
When contacting potential data annotation providers, the following questions can help you compare them on a consistent basis.
| Evaluation Area | What to Ask the Provider |
| Annotators | Who will actually perform the work—employees, part-time workers, crowdsourced workers, or others? |
| Experience & Training | What experience and training do the annotators assigned to the project have? |
| Quality Control | What review methods and quality standards are used? |
| Inspection Scope | Is the work fully inspected or sample-checked? Who performs the inspection? |
| Pricing | Are setup, requirements definition, data processing, or other services billed separately? |
| Scope of Services | Can the provider support the required annotation formats and data types? |
| Communication | How are questions, progress updates, and changes to annotation rules handled? |
| Security | In what environment will the project data be accessed and handled? |
| Timeline | Can the provider support the required data volume and delivery schedule? |
| Trial / Pilot | Can you evaluate quality using sample data before the full project begins? |
For teams outsourcing annotation for the first time, evaluating the project process—not just the quote—can help reduce misunderstandings after the work begins.
Some annotation projects are straightforward, while others require more judgment and complex annotation rules.
Examples include images where the target and background have similar colors, making objects difficult to distinguish, or datasets involving details such as wire sagging or twisting, where it may be difficult to define exactly what should be annotated.
In these situations, simply increasing the number of workers does not necessarily improve consistency. The experience and training of the annotators, together with a process for sharing judgment criteria and reviewing the results, can be critical to maintaining quality.
For complex projects, evaluate the people performing the work and the quality control system supporting them as a complete process.
Yazaki provides data annotation services focused on image and video data.
Our annotation capabilities include image classification, object detection, region extraction, pose estimation, and 3D point cloud annotation.
Trained employees perform the annotation work, with support covering requirements discussions, trials, production annotation, quality inspection, and delivery. Our quality control process includes 100% inspection, spot checks, and final inspection.
Yazaki also supports annotation projects across a range of industries and applications, including construction, automotive, real estate, disaster prevention, manufacturing, agriculture, medical applications, and food-related applications.
If you would like to evaluate annotation quality using your own data before moving forward with a full project, Yazaki offers a free trial.
When choosing a data annotation provider, look beyond price. The people performing the work and the quality control process supporting them can have a direct impact on the consistency of your dataset.
When comparing multiple providers, request quotes based on the same scope of work and evaluate not only unit pricing, but also service coverage, quality control, and project communication. Whenever possible, consider running a trial or pilot using your own data before starting the full project.
Yazaki provides image and video annotation services with trained employees and a structured quality control process. If you are considering outsourcing data annotation, contact us to discuss your project or request a free trial.