Compare the Top Data Labeling Software that integrates with Kubernetes as of July 2025

This a list of Data Labeling software that integrates with Kubernetes. Use the filters on the left to add additional filters for products that have integrations with Kubernetes. View the products that work with Kubernetes in the table below.

What is Data Labeling Software for Kubernetes?

Data labeling software is a tool that assists in the organization and categorization of large datasets. Data labeling tools enable data to be labeled with relevant tags depending on the purpose such as for machine learning, image annotation, or text classification. Data labeling software can also assist in categorizing input from customers so businesses can better understand their needs and preferences. The software typically comes with different features such as automated labeling, collaboration tools, and scaleable solutions to handle larger datasets. Compare and read user reviews of the best Data Labeling software for Kubernetes currently available using the table below. This list is updated regularly.

  • 1
    Datasaur

    Datasaur

    Datasaur

    Welcome to the best tool for managing your labeling team, improving data quality, and working 70% faster—all in one place.
    Starting Price: $349/month
  • 2
    Diffgram Data Labeling
    Your AI Data Platform Quality Training Data for Enterprise Data Labeling Software for Machine Learning Free on your Kubernetes Cluster Up to 3 Users. TRUSTED BY 5,000 HAPPY USERS WORLDWIDE Images, Video, Text Spatial Tools Quadratic Curves, Cuboids, Segmentation, Box, Polygons, Lines, Keypoints, Classification Tags, and More Use the exact spatial tool you need. All tools are easy to use, fully editable, and powerful ways to represent your data. All tools are available in Video. Attribute Tools More Meaning. More degrees of freedom through: Radio buttons. Multiple select. Date pickers. Sliders. Conditional logic. Directional Vectors. And more! You can capture complex knowledge and encode it into your AI. Streaming Data Automation Up to 10x Faster then manual labeling
    Starting Price: Free
  • 3
    Label Studio

    Label Studio

    Label Studio

    The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Configurable layouts and templates adapt to your dataset and workflow. Detect objects on images, boxes, polygons, circular, and key points supported. Partition the image into multiple segments. Use ML models to pre-label and optimize the process. Webhooks, Python SDK, and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases, and data types in one platform. Start typing in the config, and you can quickly preview the labeling interface. At the bottom of the page, you have live serialization updates of what Label Studio expects as an input.
  • 4
    Snorkel AI

    Snorkel AI

    Snorkel AI

    AI today is blocked by lack of labeled data, not models. Unblock AI with the first data-centric AI development platform powered by a programmatic approach. Snorkel AI is leading the shift from model-centric to data-centric AI development with its unique programmatic approach. Save time and costs by replacing manual labeling with rapid, programmatic labeling. Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets. Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data. Version and audit data like code, leading to more responsive and ethical deployments. Incorporate subject matter experts' knowledge by collaborating around a common interface, the data needed to train models. Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators.
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