Choosing between in-house and outsourced data annotation is key to building high-quality training data for machine learning. This post compares annotation models, highlights risks around quality, governance, and security, and explains how to select the right partner to scale your AI development.
For companies striving to unlock the full potential of artificial intelligence, access to accurate and scalable datasets often becomes a major bottleneck. Many data labeling approaches carry tradeoffs in accuracy, cost, and turnaround time, making it difficult to generate the high-quality inputs modern ML models require.
In-house labeling draws on deep institutional knowledge and can produce strong context alignment, but it is also expensive, time-intensive, and difficult to scale. Crowd-based methods offer speed and cost efficiency, yet the distributed nature of these workforces often introduces risks around label quality, iteration cycles, and AI governance.
As the industry matures, best practices for training data are becoming clearer, helping teams navigate these choices with more confidence. This post breaks down the pros and cons of common annotation models and provides guidance to help you determine which approach best fits your ML goals.
Understanding the strengths and limitations of in-house data annotation can help teams decide when it’s the right approach and when external resources might be more efficient.
There are some drawbacks to in-house annotation that merit consideration.
The costs to hire, train, and retain training specialists can be significant, especially in cases where your own in-house data scientists are taking on annotation work, or where a partner has to hire data scientists to add to their own teams. Their time is better spent on data analytics and building and fine-tuning the models that your labeled data will fuel.
There will also be costs associated with sourcing your own annotation tool — whether you’re investing in a team to develop a tool in-house, using an open-source solution with limited features, or paying licensing costs to a labeling platform.
Managing an in-house annotation team can be time-consuming as well, especially if there is high turnover and the need to scale up the team at periods of peak demand for annotations. You will also need to set aside time for quality assurance regardless of whether it’s your own team or your vendor’s; in some cases, ML engineers can spend several hours reviewing annotations and providing feedback to annotators.
Beyond cost, there is the more subtle problem of bias. Annotators that are primarily exposed to your organization’s way of looking at data and the problem you are attempting to solve will adopt a mindset around labeling shaped by that perspective. This can lead to missed opportunities to create useful training examples that fall outside your norm.
This limited view point can constrain model robustness if not addressed. In situations where internal perspective dominates, bringing in a managed external workforce with different experiences can help surface blind spots and edge cases.
Now, having highlighted all the drawbacks mentioned above, in-house annotation confers some truly meaningful advantages, particularly if you hire a vendor with their own in-house annotators to do the labeling tasks.
In-house annotators — whether data scientists or a small dedicated team of labelers you’ve added to your own team or hired through a partner — have the advantage of being well versed in your business. They have a good understanding of your data and processes as well as the objectives of your machine learning initiatives. This close alignment often strengthens annotation accuracy and context.
It’s often the best option for earlier stages of the ML production lifecycle, when the volume of data is comparatively small and models are still being developed and fine-tuned.
Labeling data in-house using skilled annotators can give precious insights into potential model errors and edge cases, which can save time and money in the long run if they are tackled early enough. You can experiment and iterate quickly because the feedback loop can be lightning-fast. Annotators have direct access to the ML team, and they can work together to update instructions as unforeseen situations arise, saving hours of rework later on.
Finally, labeling your data with a properly vetted partner who employs in-house annotators gives you full control over your data and physical security. Here’s how and why:
Outsourcing data annotation plays a major role in scaling ML workflows, but the benefits and risks vary depending on project complexity and data quality requirements.
The need for large volumes of data and low-cost annotation has driven the growth of a variety of outsourced data labeling solutions, from crowdsourcing to business process outsourcing (BPO) solutions.
When teams are working with simple, low-context data and well-defined labeling instructions, outsourcing can help reduce internal time and headcount required to produce training data. Instead of hiring, training, and managing a large in-house annotation workforce, teams can redirect more of their effort toward model design, evaluation, and deployment.
Outsourcing can also provide flexibility when data volumes spike or fluctuate over time. Rather than maintaining a permanently large internal team to handle occasional peaks, organizations can rely on an external provider to scale annotation capacity up or down as needed.
Traditional crowdsourcing platforms optimize for quantity over quality; though clients can affordably access a large distributed third-party workforce for their machine learning projects, annotators do not often have domain expertise and resulting datasets lack quality control.
Business Process Outsourcing (BPO): BPO companies may offer more bespoke solutions, but implementation can be expensive and slow, and this approach is not optimized for scaling or the integration of new tools.
Massively distributed annotation workforces often come hand-in-hand with opaque practices in regards to AI governance and ethics. The complexity of the data procurement process combined with a lack of standards around equitable data supply chains has several downstream implications for essential but largely unseen annotators.
For some, the decision to crowdsource the annotation process can result in unwittingly doing business with an unethical partner who does not follow fair labor practices.
Additionally, cutting corners early on can slow the path to production in later stages of ML model development. Crowdsourcing — especially when annotators are anonymous — does not lend itself to an agile labeling process. Many ML engineers prefer to stay close to their data in the early stages of their AI projects, with tight feedback loops to uncover and mitigate edge cases, iterate on labeling instructions, and ultimately get better results more quickly.
Your data is valuable intellectual property, especially if it is important enough to be a key component in your machine learning initiatives. Yet crowdsourcing typically relies on a large distributed workforce, making it difficult to control physical security measures. If you outsource, can you confidently answer the following questions:
The short answer is that there is no way to guarantee that there won’t be any data leak or even to know if such a leak occurred when you’re working with crowd-sourced annotators. In fact, it’s been proven that sensitive data entrusted to data annotation companies employing outsourced annotators can be (maliciously or unintentionally) leaked online.
Obtaining the reassurance that your data — and your clients’ data — is secure becomes a challenge when your annotators remain anonymous.
Outsourcing data labeling can provide a quick path to receiving a high volume of simple, low-context labeled data, which may suffice depending on your use case. A false negative in an autonomous vehicle or biomedical algorithm could mean life or death; , however, in the case of an e-commerce chatbot, it may just result in poor customer service. In short, better quality training data generally leads to higher and more reliable model performance, especially for the same amount of data.
Since the weight and severity of a false negative differs across verticals, it’s important to define the level of data quality and domain expertise needed to train your algorithm as a part of your training data strategy.
The limitations of low-cost, crowdsourced annotation are clear and substantial, and the industry has responded by developing product-led, AI-driven alternatives. Businesses now have the option of using a service that combines the best qualities of existing services for their data annotation projects.
Labeling platforms with directly managed workforces prioritize innovation in their tech and place more emphasis on tight feedback loops and QA processes — consequently improving label quality, eliminating questionable, unethical labor practices, and mitigating data security risks.
This new category of labeling providers are product-led, often with a dedicated team of machine learning engineers building better annotation tools. This is an important point: the platform improves over time because the ML engineers building the service can do experiment-driven development, for example by using A/B tests, to continuously improve the platform.
These improvements are coupled with a highly skilled workforce that is trained in the specific domain they are annotating and directly managed by your team. By pairing domain-trained annotators with an experiment-ready platform that continually improves data quality, product-led, domain-expertise services can deliver higher quality annotated data while still controlling costs.
In practice, there is no one-size-fits-all approach for deciding between in-house and outsourced data annotations, but Sama has seen a common pattern amongst our customers.
Some clients find in-house labeling works best early in the project as they are refining their requirements and discovering edge cases. Once models are well-performing on a subset of their data and they have a good understanding of requirements, these clients then look for a partner to help them scale annotations.
Here are some of the most critical questions you need to ask yourself when you consider which approach or combination of approaches to take:
If you’re convinced that you need to select a data training partner, you’ll have to consider a number of things. To start, when choosing an annotation partner, we recommend looking for the following in order to maximize the chances of success for your training data strategy:
Look for indicators that the company is product-led: actively developing new techniques for improving annotation quality and validating their advances, for example, through A/B testing, technical conferences, or peer-reviewed publications.
Ensure that annotators are specifically trained on your use case, and that you can remain in constant communication with labelers to help monitor quality, respond to edge cases, and iterate on instructions as you go. Look for evidence that your annotation partner understands the impact of the data on the AI models that are being developed and trained.
Labeling needs change frequently during the course of model development, and your partner should have the ability to customize workflows and QA processes accordingly. You want to find a partner that has gone through the iterative process many times, and can scale with your projects on demand if needed.
These processes should include automation and AI-powered QA processes bolstered by humans-in-the-loop.
Look for organizations that have documented ethical supply chains, verified by independent third-party review. Look for other indicators of sound business practices, for example, B Corp Certification.
Assess the data retention practices of the annotator. Ideally, the annotating service should not retain your data. Verify they can comply with relevant industry and government regulations, such as GDPR for European Union customers. Finally, validate that they adhere to best practices for physical security, such as ISO certified delivery centers, biometric authentication, and user authentication with 2FA.
These are but a few of the dimensions you should consider when selecting an annotation partner who can deliver the high-quality annotations you need to get your ML models into production more quickly.
Choosing between in-house and outsourced data annotation is not a one-time, binary decision.
It is an ongoing strategy that should evolve as your models, risk profile, and data needs change. High-risk, complex, or highly regulated use cases usually demand in-house or tightly managed workforces with strong governance, while lower risk, high-volume tasks may be a better fit for carefully vetted external partners.
Sama provides ML Professionals and AI team Leads with an indispensable solution for Computer Vision data labeling.
