Top 7 Text annotation tools for Developers in 2025

Manual text annotation is a common headache that causes delays in projects and introduces unnecessary errors.

You can waste hours on simple tasks, deal with confusing files, and end up with low-quality or incorrect labels that hurt your project.

This is where text annotation tools come in.

The right tool integrates smoothly into your existing workflow, provides the precise functionality your project requires, and transforms the time-consuming task of labeling into an efficient process.

This guide lists the best text annotation tools for developers, breaking down what each one does best, how they fit into various development environments, and what they cost.

Keep reading to find the right tool for your project and start creating better training data, faster.

Let’s get started.

Why You Should Use Text Annotation Tools?

The quality of data used to train machine learning models has a direct impact on their performance.

Without a good tool, preparing data to build and train AI models capable of processing human language is a slow and messy task.

Text annotation tools help add meaningful labels or tags to raw text, allowing natural language to be translated into a format that a machine can process and learn from.

It is the critical bottleneck that impacts the success or failure of most Natural Language Processing (NLP) projects.

Here’s why it matters:

• The most powerful NLP models today (like BERT and GPT) are based on supervised learning. This means they learn the patterns between input data (the raw text) and output data (the labels). Without high-quality labeled data, these models have nothing to learn from. The model’s performance is directly proportional to the quality, quantity, and consistency of your annotated data.
• Words can be tricky and have multiple meanings. For example, the word “python” could be a snake or a programming language. A well-defined annotation schema lets the model resolve these ambiguities according to a rule set and interpret text on its own in the future.
• Text annotation enables you to teach critical domain expertise to your AI.

Besides the core purpose of training supervised machine learning models, text annotation tools are also useful for:

• Building specific NLP applications like search algorithms, recommendation engines, chatbots, and virtual assistants.
• Assigning predefined categories or tags to documents, articles, or code snippets.
• Tagging customer support queries and building systems to analyze customer feedback.

7 Best Text Annotation Tools

Now that you understand why text annotation is useful, let’s explore the best tools available for it.

SuperAnnotate

SuperAnnotate is a versatile data annotation and management platform designed to create high-quality training datasets for machine learning and AI models.

It supports multimodal data types and combines human expertise with automation for smooth data curation, annotation, and evaluation, making it easier for teams to build and deploy production-ready AI models.

Key Features:

• Multimodal Annotation: It supports annotation for images, videos, text, and audio. Includes specialized tools for tasks like object detection, segmentation, named entity recognition (NER), and classification.
• AI-Assisted Automation: Uses AI algorithms to automate repetitive tasks like labeling and segmentation.
• Custom UI Builder: Allows teams to design custom annotation interfaces and multi-stage workflows to match specific project needs.
• Data Management Tools: Includes tools like Explore and similarity search to classify and navigate large datasets and maintain data quality.
• Integration Capabilities: Provides Python SDK, webhooks, and pipeline automation to integrate with ML frameworks like TensorFlow, PyTorch, and cloud storage.

Pros:

• High annotation accuracy and quality assurance tools.
• Strong enterprise security and compliance certifications.
• Real-time collaboration and version control.

Cons:

• Steeper learning curve for beginners.
• Higher cost for advanced features.

Pricing:

• Custom pricing available on request.

Snappify

Snappify is a user-friendly platform that helps developers, educators, and technical content creators explain code and technical concepts through animated infographics and presentations.

Unlike other text annotation tools that focus on labeling data for machine learning, Snappify is designed for communication and visual education, enabling users to create beautiful, shareable technical content quickly.

Key Features:

• Animated Code Presentations: Turn static code into visually appealing slides with smooth animations, syntax highlighting, and embedded explanations.
• Custom Annotations: Explain your code snippets using a rich-text editor. Add arrows, highlights, blurred boxes, and text callouts to emphasize specific parts of your code.
• Multiple Code Windows: Add as many code snippets as you want and compare them side-by-side within a single slide.
• Template Library: Access pre-designed templates for quick customization and brand consistency.
• Export and Sharing: Export presentations as images, GIFs, or MP4 videos, or share via public URLs for easy collaboration. Embed interactive infographics into platforms like Hashnode, Notion, and Medium, allowing viewers to copy code directly.
• Integration: Offers VS Code and IntelliJ plugins for easy snippet creation.

Pros:

• Technical storytelling with visual enhancements.
• AI-powered features reduce the time spent writing explanations.
• Strong collaboration features for teams and client reviews.

Cons:

• Lacks ML-focused features.
• Free plan includes watermarks and limited exports.

Pricing:

• Free plan available.
• Pricing starts from $5 per month.

Labelbox

Labelbox is a cloud-based, end-to-end platform for annotating text, images, and videos.

It is widely used for NLP and computer vision projects, helping teams create, manage, and maintain high-quality labeled datasets for training AI models.

Its flexibility, strong automation features, and enterprise-grade capabilities make it a popular choice for both startups and large organizations.

Key Features:

• Multimodal Annotation: Labelbox supports annotation for various data types, including text, images, video, and conversational data.
• AI and ML Assisted Labeling: The platform includes tools to auto-generate annotations and speed up the labeling process.
• Customizable Annotation Interfaces: You can create custom annotation interfaces using a drag-and-drop UI builder. This allows for the creation of project-specific labeling workflows and schemas.
• Collaboration: It offers features for team-based annotation, such as review workflows, role-based access control, and adjudication tools to resolve disagreements between annotators.

Pros:

• High degree of customization for complex projects.
• Strong integration with ML workflows.

Cons:

• It can be expensive for small teams or individual developers.
• Limited advanced 3D annotation features.

Pricing:

• Free plan available.
• Custom pricing based on usage.

Doccano

Doccano is an open-source text annotation tool that enables developers and data scientists to annotate text data for ML projects.

It is particularly focused on NLP tasks such as named entity recognition (NER), text classification, sequence labeling, and sentiment analysis.

Its web-based interface allows users to label text, collaborate in real-time, and export data in standard formats compatible with popular ML frameworks.

Key Features:

• Collaboration Tools: Multiple users can work on the same project simultaneously, with role-based access, real-time preview, and labeling statistics to maintain consistency and monitor progress.
• Export Formats: Supports various export formats, including JSONL and CSV, for smooth integration into ML workflows.
• Customization: Define custom labels with color coding and keyboard shortcuts to speed up annotation.
• REST API Integration: It offers optional auto-labeling integration via REST API to utilize pre-trained models for initial annotations.

Pros:

• Free and customizable.
• Easy to set up and use.

Cons:

• Performance issues in self-hosted environments.
• Limited features for large-scale teams.

Pricing:

• Free and open-source.

Prodigy

Prodigy is a scriptable annotation tool that is designed to help developers quickly label and iterate on datasets for tasks like named entity recognition (NER), text classification, and image annotation.

The creators of the spaCy library develop it with a focus on efficiency, customization, and privacy.

Key Features:

• Active Learning Integration: It utilizes model-in-the-loop workflows, where machine learning models pre-annotate data to make the annotation process more efficient.
• Customizable Workflows: Supports Python-based scripts for customizing annotation tasks to specific needs.
• Integrations: Works with popular libraries like spaCy, Hugging Face, PyTorch, and TensorFlow.

Pros:

• High efficiency with AI-assisted labeling and active learning.
• Excellent for iterative model development and rapid prototyping.

Cons:

• Paid tool with no free tier.
• Limited large-scale collaboration features.

Pricing:

• Life-time purchase license costs $390.

Label Studio

Label Studio is an open-source data annotation platform that helps create, manage, and refine labeled datasets for ML tasks and supports annotation over multiple data types.

It provides a web interface, APIs, SDKs, and customizable configurations so developers can build labeling pipelines that integrate with their workflows.

Key Features:

• Multimodal Data Support: Annotate text, images, audio, video, and time-series data.
• Customizable Interface: Use XML-like templates to create annotation workflows for specific tasks and define annotation types.
• Collaboration Tools: Support for multiple annotators with role-based access, real-time updates, and conflict resolution.
• Integrations: Integrate ML models, including TensorFlow and PyTorch, for pre-labeling and active learning.
• Export Flexibility: Export annotations in formats like JSON and CSV for integration into ML pipelines.

Pros:

• Highly customizable for various data types.
• Strong collaboration features and active community support.

Cons:

• Requires self-hosting for large projects.
• Setup and configuration can take time.

Pricing:

• Open-source and free for self-hosted use.

UBIAI

UBIAI is a cloud-based text annotation platform that focuses on automating data extraction and labeling for NLP tasks.

It supports a wide range of text annotation tasks, including named entity recognition, relation extraction, text classification, and fine-tuning custom models.

Key Features:

• Optical Character Recognition (OCR) Support: Extract text from scanned images, PDFs, and other unstructured formats.
• Automation: Includes auto-labeling features, model-assisted labeling, and integration with pre-trained models for tasks like sentiment analysis and entity recognition.
• Collaboration Tools: Enables multiple annotators to collaborate on projects through role-based access, validation workflows, and inter-annotator agreement metrics, ensuring annotation quality.
• Data Export: Supports JSON and CSV exports to integrate with ML pipelines.

Pros:

• User-friendly interface suitable for non-experts.
• Strong OCR capabilities and multilingual support.

Cons:

• Free plan includes basic annotation tools with limited storage and features.
• Occasional performance lag with large datasets.

Pricing:

• Pricing starts from $74 per month.

Final Words

Choosing the right text annotation tool depends on your specific needs, budget, and technical expertise.

We’ve included both open-source options for small teams or those who value customization and cost-efficiency, as well as enterprise-grade platforms for automation, security, and scalability.

Carefully evaluate your priorities and choose the perfect tool for your next project.

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