The complete buyer's guide to data annotation

Data is everywhere, but raw numbers don't tell stories. Annotation adds the context needed, turning raw information into something valuable.

AI researchers and developers rely on this process to build smarter systems. Whether you're working on in-house models, using open-source solutions, or integrating third-party AI, good annotation matters.

But annotation isn't just about adding labels to data. It's a mix of skill and know-how. Do it well, and your AI performs better. Do it poorly, and your algorithm might struggle with basic tasks.

In this guide, we take you through data annotation step by step, covering the basics, exploring the methods used, and introducing software options to get you started.

Table of contents

1. Understanding data annotation and AI fine-tuning
2. What to consider before choosing a data annotation tool
3. Different types of data annotation tools
4. Criteria for evaluation data annotation tools and software
5. Vendor selection process
6. Post-purchase considerations and achieving long-term success
7. Key takeaways
8. The role of Prolific

1. Understanding data annotation and AI fine-tuning

Data annotation is the process of labeling or tagging data so AI models can understand it. Since machine learning algorithms rely on patterns in data to learn and make decisions, labeled data lays the foundation that allows these models to "see" or "understand" the patterns in text, images, audio, or video. 

Though you can still train some AI/ML models (like large language models) with raw data, it's an expensive process requiring access to massive amounts of text data. This is so the model develops a general understanding of grammar, syntax, and language. Without properly annotated or labeled data, however, an AI system is prone to inaccurate predictions, classifications, or recommendations.

Data annotation is needed for both the initial training of AI models and the ongoing process of fine-tuning them. As models evolve and need to adapt to new data or tasks, fine-tuning becomes even more important to maintaining high performance—especially in fast-changing fields where new information and contexts emerge at breakneck speed.

The process hinges on continuously improving the quality of the labeled data they are fed. Data annotation tools also help model creators evaluate their AI models so they work in the real world, including their:

• Helpfulness
• Accuracy
• Safety
• Robustness
• Fairness

Types of annotations

Different types of data annotations help train various AI models. Each annotation type fits a specific data format and AI use. These include: 

• Text annotations: Used for tasks like natural language processing (NLP), sentiment analysis, and language translation. This involves tagging parts of speech, named entities, and sentiments to help AI systems like chatbots interpret user input.
• Image annotations: Critical for AI models in image recognition, self-driving cars, and medical diagnostics. Objects or features are tagged within images to help models identify items like pedestrians or medical conditions.
• Audio annotations: Necessary for speech recognition models. Annotating voice data helps identify speakers, sounds, or speech patterns for virtual assistants or customer service systems.
• Video annotations: Used for tracking objects and labeling actions in video frames. This is key for industries like security and autonomous vehicles, where accurate tracking of movement is essential.

(We’ll go into more detail on each of these types of annotations and the tooling available for them further down in the guide.)

Examples of use cases in real-world AI-first products

From self-driving cars to chatbots, here’s what AI-first products look like in the real world and why data annotation is so fundamental to their success. 

• Self-driving cars: Autonomous vehicle technology depends heavily on high-quality annotations of image and video data. AI models in these vehicles need annotated data to recognize important aspects like traffic signals, pedestrians, road signs, and other vehicles in order to make safe, real-time decisions.
• Chatbots and virtual assistants: Text and audio annotations help develop chatbots and virtual assistants by relying on annotated data to understand the intent behind user inputs, perform tasks, and respond with relevant information.
• Sentiment analysis: Businesses use AI for sentiment analysis, typically applied in customer feedback or social media monitoring. Here, text annotations help the AI model identify whether a comment or review is positive, neutral, or negative, so companies can gauge customer sentiment at scale.

Sentiment analysis data visualization by Pulsar.

How accurate annotations make your model perform better

Better generalization

When you train your model on well-annotated data, it learns to handle new situations better. If your image recognition model has seen labeled examples of objects from many angles and lighting conditions, it won't stumble when it encounters these variations in real use.

Reduced bias

Good annotations help build fairer AI systems. When training data isn't carefully selected and labeled, AI models can learn to make decisions based on factors like gender, ethnicity, or age rather than relevant qualifications and capabilities. Taking time to create diverse, balanced datasets and annotate them thoughtfully helps teams spot and address potential bias before it gets baked into their models. This early attention to fairness pays off by producing AI systems that make more equitable decisions.

Increased efficiency

Clean, accurate annotations boost your model's speed and precision. Take content moderation—when you train on labeled examples of acceptable vs problematic content, your model makes faster, more reliable decisions about what to flag.

Consequences of poor annotation quality

Bias and inaccuracy

Poor annotation practices can introduce or worsen biases in AI systems. When training data isn't properly labeled, AI models may learn inappropriate associations or make offensive misclassifications, particularly around sensitive characteristics like race, gender, and age. These errors aren't just technical problems—they can cause real harm to users and damage trust in AI systems. Careful, systematic annotation work helps prevent these serious mistakes.

Reduced model performance

Messy annotations mean messy results. Say you're building a chatbot—if your training data mislabels the intent behind user messages, your bot will give wrong or nonsensical responses that frustrate users.

Wasted resources

When you cut corners on annotation quality, you pay for it later. You'll spend time and money fixing errors, retraining models, and dealing with unhappy customers. One poorly labeled dataset can set your project back weeks or months.

2. What to consider before choosing a data annotation tool

There are more than a few tools to choose from in the data annotation market, and picking the right one isn’t straightforward if you don’t know where to start. 

Here are some of the most important aspects to consider:

Model type and domain requirements

Understand the nature of your AI model and its domain-specific need before using any specific tool. Different models—whether proprietary, third-party, or open-source—may have unique requirements regarding annotation.

• Model type: If you’re working with a proprietary model, you may have specific customization needs that not all tools can support. Open-source models might allow more flexibility, while third-party models could have stricter guidelines around the kind of data they require for optimal performance. Knowing these distinctions helps narrow down your choice of tools.
• Domain-specific knowledge: For some industries, domain expertise is non-negotiable. Take, for example, medical image labeling. Annotators need to know how to label complex images such as MRI scans or X-rays, which requires a deep understanding of medical terminology and procedures. In contrast, annotating sentiment in text data for a social media sentiment analysis tool requires expertise in understanding language nuances. The right tool should support domain-specific annotations—either through built-in workflows or by allowing easy integration with expert annotators.

A tool used for medical image labeling would need features like region-of-interest (ROI) tools for precise annotations. In contrast, a sentiment analysis tool for text data might require language-specific tagging capabilities.

Data volume and scalability

The volume of data needed to annotate can vary depending on your project. Select a tool that can handle your current and future data needs, whether you’re working with small datasets for a proof of concept or large-scale datasets for production models. 

• Estimating data volume: Begin by estimating the amount of data you need annotated—be it text, images, audio, or video. Scalability is important for projects that deal with large datasets, such as those in the self-driving car industry, where thousands of hours of video footage need to be annotated.
• Choosing a scalable tool: Not all tools are created equal in terms of scalability. Some are better suited for handling small-to-medium-sized datasets, while others are designed to scale up and support high-volume annotation needs. Make sure the tool you choose can grow with your project so you can easily increase the data volume and complexity without major bottlenecks.

A company developing an autonomous vehicle platform may need to annotate hundreds of thousands of video frames. To accelerate the process, they’ll require a tool that supports large-scale annotation and has features like automated labeling.

Image source: Appen

Internal vs. outsourced annotations

One aspect to consider when selecting a data annotation tool is whether to keep the annotation process in-house or to outsource it to external partners. Both options have their pros and cons. The right choice will ultimately depend on your resources and expertise, as well as the sensitivity of your data.

Internal annotations

If you have an in-house team with the necessary expertise, doing annotations internally offers greater control and flexibility. You can manage the process closely, so that the annotations are aligned with your specific requirements. Internal teams may be especially valuable for projects that require domain expertise, such as legal document annotation or specialized medical data.

Outsourced annotations

Outsourcing the annotation process can be more cost-effective and efficient for businesses that don’t have the time or resources to manage a large-scale annotation team. External annotation services can also provide access to a broader range of expertise and may even accelerate the process for high-volume projects. Outsourcing does, however, come with potential challenges, such as loss of control over quality and security concerns around sensitive data.

A healthcare company working with patient data might prefer to keep annotations in-house due to privacy and compliance concerns. While a tech startup working on a general AI application might opt for outsourcing to save on costs and resources.

Computer vision used in healthcare image by Mindy Support

Pros and cons of outsourcing annotation to managed services

Outsourcing data annotation to managed services can streamline your project. But it comes with trade-offs.

Pros

• Scalability: Managed services can handle large volumes of data, quickly scaling with your needs.
• Cost-effectiveness: Outsourcing avoids the expense of hiring and managing an in-house team, making it ideal for short-term projects.
• Expertise: You can access specialized annotators with domain knowledge across industries.
• Faster turnaround: Faster for repetitive annotation tasks and great when prioritizing for throughput and efficiency.
• Reduced management burden: Frees up internal teams to focus on core tasks.

Cons

• Less control over quality: Outsourced services may not meet the quality standards needed for specialized tasks.
• Data privacy risks: Sharing sensitive data externally can expose it to security threats.
• Communication issues: Misalignment with external teams may lead to delays or inconsistent results.
• Longer feedback loops: Outsourced providers could slow down revisions and adjustments.
• Dependency: Relying on external services might make it harder to switch providers or bring the process in-house later.

3. Different types of data annotation tools

Your choice of data annotation tool should match with the capabilities of the type of data used by the AI model. Different tools specialize in text, image, or video annotation, and the choice between open-source and proprietary platforms can significantly impact your workflow. Here's an overview of the main types of tools and the pros and cons of each option.

What are text annotation tools?

Text annotation tools tag, classify, and label text data for tasks like natural language processing (NLP), sentiment analysis, and entity recognition. Annotators use them to mark parts of speech, named entities, and key phrases, and the tools often support multi-language and sentiment tagging. Popular text annotation tools include:

• Prodigy: A user-friendly platform for text, image, and audio annotations, offering flexibility for NLP tasks.
• LightTag: Focuses on collaborative annotation for text, allowing teams to work together to label large datasets.

Image annotation tools

AI models in fields like autonomous vehicles and medical diagnostics rely heavily on image annotation tools, making them especially important for computer vision tasks. These tools allow users to label objects, define boundaries, and categorize features in images. 

Key image annotation tools include:

• LabelImg: An open-source option for labeling images. It’s commonly used for object detection tasks.
• SuperAnnotate: A proprietary tool that offers advanced features for annotating images and videos with precise control and collaboration capabilities.
• CVAT (Computer Vision Annotation Tool) is one of the popular open-source data annotation tools. It’s widely used for annotating images and videos, particularly in computer vision tasks like object detection, image segmentation, and tracking.

Video annotation tools

Video annotation tools enable annotators to label objects and track their movements across frames, which provides the ability to capture dynamic actions as they unfold. These tools are important for industries like security and self-driving cars, where tracking objects over time plays a fundamental role in the AI’s ability to operate. Popular video annotation tools include:

• Vatic: An open-source video annotation tool that enables frame-by-frame labeling for tracking objects.
• Scale AI: A proprietary platform that offers advanced video annotation services with automated and manual tools for efficient labeling.
• Labellerr: A video annotation tool that offers AI-assisted labeling, multiple annotation types, and built-in quality assurance. It’s often used for scalable, secure projects​.

Open-source vs. proprietary annotation tools

Choosing between open-source and proprietary tools comes down to what works best for your project. Open-source options give you flexibility and save on costs, while proprietary platforms offer more advanced features and support. Your decision will depend on what you need to get the job done—eg, complexity of data— and the resources you have available.

Open-source tools

• Pros:
  • Cost-effective: Open-source tools like LabelImg and COCO Annotator are free to use—ideal for smaller teams or projects with limited budgets.
  • Customizability: Users can modify and extend the tools to fit specific project needs, which offers more flexibility.
• Cons:
  • Limited support: Open-source tools often lack dedicated customer support. This can be a challenge when technical issues arise, especially if you’re not an expert.
  • Fewer features: These tools may not have advanced capabilities like automation, collaboration features, or built-in integrations with other systems.

Proprietary platforms

• Pros:
  • Advanced features: Tools like Labelbox and Scale AI shine for features such as automation, quality control, team collaboration, and built-in analytics.
  • Dedicated support: Proprietary platforms typically offer customer support, tutorials, and detailed documentation. This can help speed up implementation and resolve issues.
• Cons:
  • Cost: Proprietary tools come with subscription fees, which can be expensive for smaller teams or startups.
  • Less flexibility: These platforms are less customizable, meaning users may be limited by the features and workflows provided by the vendor.

4. Criteria for evaluating data annotation tools

When choosing a data annotation tool, there are a lot of factors to think about, including accuracy, usability, and price. How do you find a tool that guarantees you the quality data you need and works for your project?

Here are a few important things to keep in mind.

Tool features and capabilities

The features and technical capabilities of the tool should match your project's complexity and security requirements:

• Data types and annotation formats: Tools should support the data types you need, whether it's text, images, videos, or multiple formats (multimodal). Make sure that the tool aligns with your task's input requirements and supports export formats like JSON, XML, or CSV.
• Collaboration features: Tools that support multi-user workflows, real-time tracking, and task assignments can improve team efficiency, especially on large-scale projects.
• Data security and compliance: The tool should adhere to necessary data security protocols, such as GDPR for European data and HIPAA for sensitive health data, to protect your information.
• Integration capabilities: The tool should integrate with your existing workflows through APIs or version control systems. This allows you to streamline the annotation process.

Accuracy and quality assurance

Look for a tool with built-in mechanisms so that you get high-quality, accurate annotations:

• Data quality processes: Some tools offer built-in data quality features to maintain annotation accuracy. These include automated checks or manual review workflows.
• Inter-annotator agreement: Look for tools that allow for multiple annotators to work on the same data set, with features to measure agreement and resolve inconsistencies between annotators.

Usability and learning curve

If your team can adopt a tool easily, you'll improve productivity and set your project up for success:

• User-friendly interface: The tool should have an intuitive design that reduces friction for annotators, especially if non-technical users will be involved in the annotation process.
• Ease of learning: Evaluate whether the tool is simple, especially for teams with diverse technical backgrounds. Tools with minimal onboarding requirements are typically more efficient to implement.

Support and training

Support options mean everything, especially when dealing with complex annotation projects:

• Available support: Check whether the tool provides comprehensive documentation, live customer support, or detailed training materials to help users troubleshoot and become proficient quickly.
• Community support: For open-source tools, active community support can be a valuable resource, offering forums, user guides, and collaborative problem-solving.

Pricing models and ROI

Pricing models can vary, so it’s important to choose a tool that fits within your budget while providing long-term value:

• Pricing models: Some tools charge based on the number of annotations, while others offer subscription-based or one-time license fees. Compare these models to find the most cost-effective option for your project.
• Return on investment (ROI): When evaluating the cost involved, consider the long-term ROI by factoring in the tool’s scalability, time savings, and potential improvement in AI model performance.

Evaluating these factors helps you choose a tool that delivers high-quality annotations and aligns with your project’s specific needs and long-term goals.

5. How to choose a vendor that works for you

The one you choose will impact how efficiently you can process data, how well the tool integrates with your workflows, and the final quality of your AI models. To guide you through the selection process, here’s a straightforward approach:

Creating a shortlist of vendors

To begin, you’ll need to narrow down the available options to a list of vendors that meet your business requirements.

• Business requirements: Clearly define the features your team requires, such as:
  • Supported data types: Does the vendor handle the specific formats (e.g., text, image, video) and annotation types you need (e.g., bounding boxes, segmentation, sentiment analysis)?
  • Security and compliance: Confirm that the vendor complies with regulations like GDPR, HIPAA, or CCPA if you’re dealing with sensitive data. Check if the platform offers data encryption, user-level permissions, and other security protocols necessary to protect your data.
  • Scalability: Determine if the tool can accommodate the growth of your data annotation needs over time. As your dataset increases, ensure the tool can handle higher volumes without performance degradation.
  • Custom workflows: If your team has specific needs for quality control, automation, or collaborative annotation, look for tools that allow workflow customization to fit your project requirements.
• Comparison: After identifying your needs, compare vendors based on these factors:
  • Features: Assess the breadth of features offered. Some tools specialize in certain annotation types (e.g., semantic segmentation, named entity recognition), while others may offer multi-modal capabilities for handling different data types simultaneously.
  • Pricing models: Vendors offer various pricing structures such as pay-per-annotation, subscription-based, or one-time licensing fees. To find the most cost-effective tool, think about how frequently you'll need to use it and the overall size of your project.Subscription models might be beneficial for continuous annotation needs, while pay-per-annotation can be more economical for smaller projects.
  • Long-term flexibility: Consider how flexible the platform is for future needs. Does it offer advanced features like automation (e.g., active learning) or integrations with machine learning platforms (e.g., TensorFlow, AWS, or GCP)? This is especially important for long-term projects where your data and requirements may evolve.

Requesting a demo or trial

Once you’ve shortlisted vendors, request demos or trials to see how well the tools perform with your actual data.

Importance of testing

Testing a tool in a real-world context is vital. A demo provides a hands-on opportunity to observe how easily the tool fits into your team’s existing workflow. Trials allow you to evaluate how it handles your specific data types and tasks under actual conditions, rather than theoretical ones.

Assessing functionality

In the demo or trial, keep an eye out for:

• Annotation speed: Can the tool process your data at the required speed, particularly for large datasets? For example, if you're handling video annotation, assess whether the tool can handle frame-by-frame processing efficiently.
• Accuracy: Evaluate how well the tool handles complex annotations. Does it offer automated tools, like AI-assisted labeling, to enhance speed while maintaining high accuracy?
• Ease of use: Look for a user-friendly interface that reduces the learning curve for annotators. Complex tools with steep learning curves can lead to delays and reduced productivity, especially for non-technical team members.
• Collaboration features: If your team includes multiple annotators, check if the tool offers real-time collaboration, role-based permissions, and progress tracking to streamline the workflow.
• Data quality mechanisms: Ensure the tool has built-in data quality assurance processes, such as inter-annotator agreement checks, automatic reviews, and feedback loops. These help maintain consistency and accuracy in annotations over time.

5 questions to ask vendors

Here are some essential questions to ask:

• How does the tool handle large-scale annotations? If you expect to process vast datasets, inquire about the platform’s ability to scale efficiently. Can it handle high data throughput without lag or downtime?
• What data quality processes are available? Does the tool include automated checks for consistency or allow for manual review workflows? Ask about features that enforce high data quality over time, such as cross-annotator comparison or automatic flagging of errors.
• What support and training are provided? Determine what customer support options are available, such as live chat, phone support, or dedicated account managers. Inquire about onboarding materials, tutorials, or training sessions to help your team get up to speed.
• What customization and integration options exist? If you use specific machine learning tools or data management platforms, check if the annotation tool offers API integrations to fit into your existing infrastructure.
• How secure is the platform? Ensure that the vendor adheres to strict data security protocols. Look for encryption options, role-based access, audit trails, and compliance with regulatory standards like GDPR or HIPAA.

Vendor reputation and case studies

If a vendor says they can deliver, make sure they can prove it. Look out for: 

• Reputation: Look for customer testimonials and user reviews on platforms like G2, Capterra, or Trustpilot. These reviews will often provide insights into real-world experiences with the tool, highlighting both strengths and potential drawbacks.
• Case studies: Ask vendors for case studies or success stories that showcase how their tool has had a positive effect on projects similar to yours. This can provide concrete examples of the tool’s capabilities and limitations in action.
• Peer recommendations: Reach out to peers or industry professionals who have used the tool. They can provide unbiased feedback on how well the tool performed and whether the vendor offered adequate support and customization.

6. Post-purchase considerations and achieving long-term success

Selecting the right data annotation tool is just the beginning. For long-term success, focus on proper onboarding, continuous optimization, and monitoring model performance. 

Onboarding and initial setup

A smooth onboarding process is necessary for integrating the tool into your existing workflows:

• Training the team: Offer training on best practices for using the tool. This could include tutorials or hands-on sessions so that every team member understands how to annotate data accurately and efficiently.
• Setting up workflows: Create clear processes for assigning tasks, reviewing work, and managing annotation queues. Document these steps so teams have a consistent approach to follow.

Ongoing tool optimization and feedback loops

Once your team is using the tool, find ways to improve it so you always get the highest data quality:

• Gather user feedback: Regularly collect feedback from your annotators and project managers to identify pain points or areas for improvement. Doing so helps catch any inefficiencies early on.
• Iterate on the process: Use the feedback to refine your annotation workflows, as well as optimize the tool settings and make sure you’re maintaining high-quality annotations. Reviewing regularly will help adapt the process to evolving project needs.

Monitoring model performance after annotation

Once your annotations are complete:

• Measure impact on model performance: After using the annotated data for fine-tuning, assess how well your AI model is performing. Look at aspects like accuracy and precision so that the annotations add value.
• Revisit and adjust annotations: If performance drops or your model begins to show bias or inaccuracies, it may be time to revisit and adjust the annotations. Regularly fine-tuning both the annotations and the model will improve the chances of continued success.

7. Summing up

Whether you're working on self-driving cars or medical imaging, quality annotations make all the difference. Poor data leads to poor results, from autonomous vehicles missing obstacles to medical systems making wrong diagnoses.

Finding the right annotation tool is key to getting those quality annotations. Study the market carefully, and match tools against your specific needs—whether that's specialized features for text analysis or the ability to handle thousands of video clips.

Tools and vendors vary widely in their capabilities. Use this guide's evaluation criteria to pick one that delivers the accuracy, security, and workflow efficiency your project demands. Your choice will determine how quickly and effectively you can build better AI.

With the right annotation foundation in place, you can focus on what matters: creating AI that works reliably in the real world.

8. Why choose Prolific for data annotation?

Prolific stands out in the data annotation field thanks to its unique approach to crowdsourcing human input. While not strictly a data annotation tool, Prolific is a human data platform that helps you recruit the right people to annotate or label your data in your data collection interface and offers distinct advantages for projects where human-annotated data is important. 

Built on a flexible and extensible API infrastructure with a UI, Prolific integrates with any data annotation tool of your choice. Users can design custom studies or tasks, enabling businesses to collect diverse types of annotations or feedback. Whether it’s collecting human-labeled data for sentiment analysis or gathering opinions on complex datasets, Prolific can adapt to a wide range of needs.

• Diverse data, nuanced inputs: As opposed to working with traditional managed services who work with trained annotators only, Prolific helps you hire the right annotator profile—whether you're looking for those proficient in AI tasks, or a more diverse pool for purposes like model evaluation and bias mitigation.
• Superior data quality: Prolific carefully screens and recruits participants for higher-quality responses compared to general crowdsourcing platforms. This is important for tasks where accuracy and attention to detail are paramount, like sentiment analysis, survey data collection, or cognitive assessments.
• Scalability and speed: With 200,000-plus active, vetted participants, Prolific offers fast turnaround for projects that require large-scale human input. Whether annotating thousands of text snippets or gathering diverse opinions, the platform can quickly scale to meet demand.
• Ethical data collection: Unlike some crowdsourcing platforms, Prolific is committed to ethical treatment of its participants, offering fair pay and transparency. This can help businesses build trust and ensure responsible data collection practices, a key consideration in today’s increasingly privacy-conscious landscape.

Prolific’s difference lies in its focus on quality, ethical standards, and flexibility, making it a strong choice for projects that require human judgment and high-quality data annotation at scale.

Find out how Prolific can support your AI project