Pricing score

6.3

Scale Nucleus Pricing Profile

Scale Nucleus accelerates the development of AI applications by helping computer vision teams generate high-quality ground truth data.

HQ Location

San Francisco, California, USA

Category:

AI

Key Takeaways

Active Learning

Over time, the AI improves by learning from the human feedback, reducing manual labeling efforts and increasing efficiency.

Accelerates Annotation

Scale Nucleus uses advanced machine learning models to automatically generate initial annotations (e.g., bounding boxes, segmentation masks, text entities). Human annotators then review and refine these annotations, ensuring a high level of accuracy.

Task Management

Track progress, set deadlines, and monitor the quality of annotations with real-time insights and metrics.

Product Overview

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Scale Nucleus is an AI-powered data labeling and annotation platform designed to help organizations build high-quality datasets for training machine learning models.

Scale Nucleus combines the power of machine learning with human-in-the-loop workflows to provide fast, scalable, and accurate annotations across multiple data types, including images, videos, text, and 3D point clouds.

INSIGHTS

Our insights about Scale Nucleus pricing

01

Free plan

02

Custom options

03

Pricing information is available

Available Pricing Models

How much does Scale Nucleus cost?

Scale Nucleus offers 3 paid plans and 1 free plan for academia and individuals who want to try out Nucleus with no commitments.

Team

  • Designed for small ML teams who want to start optimizing their labeling spend.
  • The price is $1500/month.

Pro

  • Designed for mid-sized ML teams who want to scale up their data curation.
  • The price is $7500/month.

Enterprise

  • Designed for large ML teams to work with unlimited data.
  • Custom pricing.

What users say about Scale Nucleus pricing

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Indranil M.

Simple UI and self-serve platform to get label data.

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Indranil M.

Simple UI and self-serve platform to get label data.

Scale Nucleus Pricing Rating

Real-Time Collaboration: 4.8/5

The platform supports collaborative workflows, allowing multiple users to annotate data simultaneously. Team members can be assigned different roles (e.g., annotators, reviewers, project managers) to ensure smooth project execution.

Scalability: 4.7/5

Scale Nucleus is designed for both small and large-scale annotation projects, capable of handling millions of data points. It supports collaborative workflows and large teams, allowing for fast and efficient processing of big datasets.

Customizable Annotation Workflows: 4.9/5

Tailor the annotation process to your specific use case. Define custom tools, labels, and instructions to meet the needs of your project, whether you are labeling objects in satellite imagery, transcribing audio, or labeling medical images.

FAQ on Scale Nucleus Pricing

How does Scale Nucleus integrate AI and human labeling?

Scale Nucleus uses a human-in-the-loop approach, where AI models suggest initial annotations, and human annotators review, verify, and refine these suggestions. This combination helps accelerate the labeling process while ensuring high accuracy, especially for complex or nuanced data that AI alone may not handle effectively.

What export formats are supported?

Scale Nucleus supports various export formats compatible with popular machine learning frameworks and tools, including:

- COCO (Common Objects in Context) format
- YOLO (You Only Look Once) format
- Pascal VOC
- TFRecord (for TensorFlow)
- CSV and JSON for text and structured data annotations

Can I use Scale Nucleus for large datasets?

Yes, Scale Nucleus is designed to handle large-scale annotation tasks. The platform is scalable and can process millions of data points across multiple projects. It leverages automation and machine learning-assisted labeling to efficiently manage and annotate extensive datasets, saving time and resources.

Is Scale Nucleus customizable?

Yes, Scale Nucleus is highly customizable. Users can define specific annotation guidelines, workflows, and tool configurations based on the type of data and the machine learning models being trained. This flexibility ensures that it can accommodate a wide range of industries and use cases.