Compare 10 Open Source Data Labeling Platforms in 2025

Data labeling, the process of annotating raw data (such as images, text, or audio), is essential for training ML models to perform tasks like classification and recognition. While pre-built solutions exist, they may not always meet specific needs, making open-source platforms a more flexible and customizable alternative. See the top 10 open-source data labeling tools.

Examples of open-source data labeling platforms

What are open-source data labeling platforms?

Open-source data labeling platforms enable firms to customize existing data labeling solutions without building software from scratch. Companies’ strategies vary, so using template solutions is not always effective. In such cases, where the budget or time of the companies is limited, using open code data labeling platforms is an effective solution for them.

Using open-source software allows IT to implement new code into the company’s data labeling facilities to customize functionality and achieve the desired result.

Why choose an open-source data labeling platform?

Open-source data labeling platforms allow businesses to customize existing software without developing annotation tools from scratch. Unlike proprietary solutions, which may have limited flexibility or high costs, open-source alternatives allow organizations to modify features, integrate with existing workflows, and enhance performance based on their needs.

Benefits of open-source data labeling platforms:

• Customization: Modify features, integrate APIs, and optimize workflows.
• Cost savings: Avoid licensing fees and long-term subscription costs.
• Community support: Benefit from continuous improvements and contributions from developers.
• Flexibility: Adapt platforms for various data types, including images, text, audio, and video.

Challenges in Open-Source Data Labeling

While open-source data labeling platforms provide flexibility and cost advantages, they also come with challenges. Below are some key considerations:

1. Limited Support & Documentation: Unlike commercial solutions, most open-source tools lack dedicated customer support. Documentation may be outdated or incomplete, making troubleshooting more difficult.
2. Scalability Issues: Some platforms struggle to handle large datasets efficiently. Lack of cloud-based infrastructure in some tools may require additional development for enterprise use.
3. Security & Compliance Risks: Since open-source tools require self-hosting, companies must ensure compliance with data protection regulations (e.g., GDPR, HIPAA). Security patches depend on community updates, increasing the risk of vulnerabilities.
4. Integration Challenges: Compatibility issues may arise when integrating open-source tools with proprietary ML pipelines.

Despite these challenges, open-source data labeling platforms remain a powerful option for businesses seeking customization and cost savings.

How to choose the right open-source platform?

Selecting the right data annotation platform depends on your organization’s needs and technical capabilities. Consider these factors:

1. Data compatibility: Ensure the platform supports your data type (e.g., images, text, audio, or time series).
2. Scalability: If handling large datasets, opt for tools that support automation and parallel processing.
3. Integration: Choose a platform that integrates seamlessly with your ML workflow and cloud services.
4. Ease of use: Some platforms require advanced programming skills, while others offer user-friendly interfaces.
5. Community & documentation: Active developer communities and detailed documentation enhance troubleshooting and customization.

To choose the data annotation tool or service that best suits your business needs, check out our sortable and filterable lists:

• Medical data annotation services
• Data annotation services
• Video annotation tools

Further reading

• Data labeling
• Data annotation
• Video annotation