In 2025, artificial intelligence is more powerful than ever, and behind every successful AI model is a mountain of well-labeled data. That’s where data annotation tools come in. These platforms are essential for turning raw data into machine-readable formats so that models can learn patterns, make predictions, and drive automation.

At HAIVO, we understand that the quality of your labeled data directly affects your model's accuracy. That’s why we’ve curated this expanded list of the best data annotation tools in 2025, so your AI projects can hit new heights of performance.

1. SuperAnnotate – Built for Team Speed and Scale

SuperAnnotate is a favorite among large teams that require real-time collaboration and efficient labeling pipelines. Whether you're working with images, videos, or LiDAR data, this platform offers end-to-end workflow management with robust automation support.

Key Features:

• Annotation support for 2D, 3D, video, LiDAR, and text data
• Integrated version control and labeling analytics
• Built-in QA tools for quality assurance at scale
• Scalable to enterprise-level workloads
• AI-assisted annotation for faster turnarounds

SuperAnnotate shines in industries like autonomous vehicles, healthcare imaging, and drone technology, where managing large teams and complex data types is key.

2. V7 Darwin – Smarter Labeling with AI Assist

V7 Darwin is revolutionizing how machine learning teams handle data with its AI-powered automation tools. Known for its deep roots in medical imaging and scientific data labeling, it provides semi-automated tools that help reduce human effort while boosting accuracy.

Key Features:

• Automate repetitive labeling with model-assisted tools
• Annotation support for images, documents, video, and multi-modal data
• Custom workflows and collaborative dashboards
• Rapid training model integration for active learning loops

If your team values precision and speed, especially in regulated industries, V7 Darwin is worth every penny.

3. Labelbox – Versatile and Customizable for Enterprise Needs

Labelbox offers one of the most powerful platforms for managing end-to-end data labeling operations. It delivers serious power to enterprise AI teams, from annotating images, videos, text, and audio to deploying model-assisted pipelines.

Key Features:

• Data types: image, video, text, geospatial, and audio
• Fully customizable labeling interfaces
• Built-in data governance tools
• Model-assisted labeling and active learning capabilities
• API-friendly for full pipeline integration

Labelbox is perfect for teams that need fine control over their data labeling processes and the flexibility to tailor workflows.

4. Roboflow – Fast, Developer-Friendly Vision Annotation

Roboflow is built for developers creating computer vision applications. It’s a browser-based platform that handles everything from annotation to training and deploying your models, making it a one-stop shop for vision AI development.

Key Features:

• Upload, annotate, preprocess, and train—all in one place
• Multiple export formats, including YOLO, COCO, and TensorFlow
• Built-in image augmentation and preprocessing tools
• Hosted model training and deployment
• Strong community and open datasets

For startups, students, and agile AI teams, Roboflow provides the speed and flexibility needed to move fast.

5. Amazon SageMaker Ground Truth 

Amazon's Ground Truth is deeply integrated with the AWS ecosystem, making it a natural choice for teams already using Amazon’s cloud stack. It combines human annotation with machine learning to optimize labeling speed and cost.

Key Features:

• Human-in-the-loop and automatic labeling capabilities
• Deep integration with AWS services like S3, SageMaker, and Lambda
• Built-in annotation UIs for image, text, and video
• Custom workflows and label verification
• Scalable for large datasets across cloud projects

Ground Truth is ideal for enterprise teams building data-heavy ML models inside the AWS infrastructure.

6. Dataloop – Full Stack for Data Lifecycle Management

Dataloop offers more than just annotation—it’s a data lifecycle platform. From labeling and versioning to pipeline management, it provides everything you need to build, maintain, and improve your datasets over time.

Key Features:

• Annotation support for images, video, text, and sensor data
• Automation tools for pre-labeling and task routing
• Real-time dashboards and workforce analytics
• Dataset management and version control
• Plug-and-play model integrations

Dataloop is a solid choice for teams that want granular control over their data infrastructure.

7. Segments.ai – Smart Labeling for Autonomous Systems

Segments.ai is designed for 3D data, making it ideal for robotics, drone tech, and autonomous vehicle projects. It supports annotations for LiDAR, point cloud, and multi-camera sensor data—all accessible via a browser interface.

Key Features:

• Native 3D point cloud and multi-camera data support
• Efficient UI for spatial labeling tasks
• Plugin architecture for custom features
• Review and QA tools built-in
• Secure collaboration for data teams

Segments.ai excels when spatial awareness and sensor fusion are critical to project success.

8. CVAT – Open Source Power for Custom Solutions

The Computer Vision Annotation Tool (CVAT) is an open-source platform developed by Intel. It’s widely used by developers and research teams who need custom labeling workflows and full control over infrastructure.

Key Features:

• Support for object detection, segmentation, and tracking
• Manual and semi-automated annotation modes
• Support for image and video data
• REST APIs and Docker-based deployment
• Active community with frequent updates

CVAT is ideal for internal annotation projects that require flexibility, control, and cost-efficiency.

9. DataTurks 

DataTurks focuses on simplicity and speed for small-to-medium projects. It supports text and image annotation, with a user-friendly interface that’s perfect for teams looking to move fast without heavy infrastructure.

Key Features:

• Fast onboarding and intuitive UI
• Pre-built templates for common annotation tasks
• Supports JSON exports and integration with training pipelines
• Collaborative labeling with role-based access

If you’re a startup, researcher, or product team that needs results fast, DataTurks gets the job done without the bloat.

10. Unitlab – Rising Star in Scalable Annotation

Unitlab is quickly becoming a standout platform in the data annotation space. Designed for speed, scalability, and intuitive use, it supports a wide range of data types and integrates easily with modern ML pipelines.

Key Features:

• Real-time team collaboration with project dashboards
• Smart auto-labeling powered by active learning
• Supports image, text, video, and document annotation
• Secure data management with encryption and access control
• Easy integration with model training workflows

Unitlab is ideal for teams that want to stay lean but scale fast.

Choosing the Right Tool

Not every annotation tool fits every use case. When choosing the right platform for your team, consider:

• What data types are you working with?
• Do you need automation or human-only labeling?
• How important is integration with your ML workflow?
• What’s your team size and level of technical expertise?

Let HAIVO Help You Annotate with Confidence

At HAIVO, we don’t just understand annotation, we live and breathe it. Whether you need text classification in multiple languages, pixel-perfect image segmentation, or high-accuracy audio labeling, our platform and expert team are here to help.

We work with startups, enterprises, and research labs to deliver annotated datasets that fuel smarter models and better business decisions.

Contact HAIVO today for custom data annotation services built to scale with your vision.