Prodigy – Features, Pricing, Alternatives and Review

Prodigy, a cutting-edge annotation tool in AI and ML, stands out as a scriptable annotation tool so efficient that data scientists can seamlessly annotate datasets.

Developed by the creators of spaCy, a renowned NLP library, Prodigy uses state-of-the-art technology for tasks such as text classification and named entity recognition.

The web application, featuring an intuitive interface, facilitates quick experimentation and evaluation, making it an indispensable asset for data scientists engaged in diverse data science projects.

The tool’s documentation and API empower users to specify and update annotations effortlessly, showcasing its versatility and efficiency.

Read on to learn more about Prodigy AI’s key features, pricing plans, pros & cons, comparison with alternatives, and review to make an informed decision for your data science needs.

Prodigy AI Key Features

Feature	Description
Fully Scriptable	Prodigy provides a rich Python API, command-line integration, and a productive Jupyter extension, allowing users to customize and automate annotation workflows as needed.
Made for Machine Learning	The tool is tailored for machine learning tasks, including Named Entity Recognition, Text Classification, Dependencies and Relations, Computer Vision, Audio and Video labeling, and more.
Efficient Annotation	Prodigy’s web-based interface is designed to be highly efficient. The manual interface allows for easy entity annotation, while the binary interface facilitates rapid “yes” or “no” decisions.
Continuous Active Learning	Prodigy employs a continuous active learning system, focusing on annotating examples that the model is uncertain about, resulting in improved model training and efficiency.
Flexible Annotation Methods	Users can annotate using token boundaries or character-based annotation for various languages and writing systems, providing maximum flexibility in the annotation process.
Transfer Learning	Prodigy supports modern transfer learning and data augmentation techniques, allowing users to leverage pre-trained models like BERT or ULMFiT without extensive manual annotations.
Span Categorization	Extract longer phrases and nested expressions from documents for span categorization tasks, enhancing the capabilities of Natural Language Processing (NLP) applications.
Text Classification	Users can perform intent detection, information extraction, semantic role labeling, or sentiment analysis with easy and flexible annotation options for text classification tasks.
Dependencies & Relations	Annotate NLP relations such as coreference, syntax, or semantics to label data with Prodigy’s fast and intuitive relations user interface, backed by its scriptable nature.
Computer Vision	Label images for tasks like object detection, image segmentation, and image classification, utilizing Prodigy’s fully scriptable back-end for building powerful workflows.
Audio & Video Annotation	Annotate audio and video files for tasks like speaker diarization and classification, using Prodigy’s scriptable back-end to create efficient annotation workflows.
A/B Evaluation	Conduct rigorous evaluations using Prodigy’s efficient methodology, facilitating repeatable experiments for subjective quality decisions and improving model development and training.
Task Routing	Distribute annotation workload among multiple annotators or workers with Prodigy’s task routing capabilities, ensuring that particular annotators see specific examples.
Prompt Engineering	Prodigy supports prompt engineering through prompt tournaments, allowing users to create competition-like scenarios among prompts to optimize annotation quality.
Large Language Models	The tool integrates seamlessly with services offering large language models for zero/few-shot learning, enabling users to access powerful language capabilities for annotation tasks.
Model Training	Prodigy provides tools to facilitate training machine learning pipelines using the annotated data, offering utilities for spaCy and JSONL output for seamless integration with other tools.

Prodigy AI Pricing Plans

Prodigy offers a free online demo to try out all the annotation interfaces before committing to any of the following paid subscriptions;

1. Personal Plan

The Personal plan is tailored for individual users such as freelancers, indie developers, and hobbyists. Users receive a lifetime license with a one-time payment, allowing them to own and use Prodigy indefinitely.

The plan includes 12 months of free upgrades, ensuring access to the latest features and improvements.

Users can install Prodigy using the provided installer and access the web application for efficient data annotation.

The license covers an unlimited number of annotators and is suitable for both personal and professional projects. The license is issued to the individual, providing a comprehensive and flexible solution for solo users.

Price

It costs $390.

2. Company Plan

The Company plan is designed for organizational use, catering to startups, data science teams, and enterprises.

Priced at $490 per seat, users acquire a lifetime license, ensuring perpetual ownership of Prodigy.

Like the Personal plan, it includes 12 months of free upgrades, unlimited annotators, and Prodigy Installer and Web Application access.

Notably, seats are freely transferrable within the company, allowing for flexibility in team management.

The license is issued to the company, offering a scalable solution for teams with the option to buy packs of 5 seats to accommodate varying team sizes.

Price

It costs $490 per seat.

Prodigy AI Pros & Cons

Prodigy AI Comparison With Alternatives

Feature	Prodigy AI	Universal Data Tool	Roboflow Annotate	CVAT	Scale AI
Core Competency	Efficient machine teaching through active learning and rapid iteration in model training	Labeling datasets of images, audio, text, documents, and video in an open data format	Automated labeling with AI-assisted labeling for bounding boxes, polygons, and instance segmentation	Industry-leading data engine for machine learning, used across various industries with advanced features	Generative AI platform leveraging enterprise data for building advanced models
Unique Feature	Active learning system, scriptable and extensible design, seamless transition from prototype to production	Real-time collaboration, import from AWS S3 buckets, training labelers with courses, labeling directly in Jupyter Notebook	AI-assisted labeling, automatic annotation using pre-trained models, real-time teamwork, integration with various formats, secure role-based access	Integrated AI for auto-annotation, extensive features for image classification, object detection, semantic and instance segmentation, 3D annotations	Generative AI architecture, fine-tuning and RLHF, partnership with leading AI models (OpenAI, Anthropic, Google PaLM, Cohere), data engine for model improvement
Use Cases	Named entity recognition, text classification, image and video classification, annotation for large language models	Image, audio, text, document, and video labeling, collaborative dataset creation, training courses, Jupyter Notebook integration	Object detection, image classification, multi-label classification, semantic masks, and more	Pedestrian and vehicle tracking, driver’s action recognition, image classification, object detection, semantic and instance segmentation, 3D annotations	Various applications including defense, automotive, government, retail, logistics, autonomous vehicles, robotics, AR/VR, content & language
Other Features	Human-in-the-loop workflows, user-focused design, adaptability and extensibility, agile data collection	Community-driven approach, certification for labelers, tests and exercises creation, Jupyter Notebook labeling module	Fast labeling workflows, progress views, labeling instructions, notifications, security standards, 26+ available formats, API integration, managed labeling services	Auto-annotation, cloud deployment, algorithmic assistance, management & analytics, community collaboration	Data labeling, 3D, image, mapping, text, and audio data labeling, dataset management, model evaluation, safety, and alignment

Prodigy AI vs. Roboflow Annotate

Prodigy and Roboflow Annotate emerge as robust options, each with distinctive features and strengths when considering data annotation and model training tools.

1. Core Competency

Prodigy AI stands out for its focus on data annotation and model training, offering a scriptable design that facilitates efficient annotation handling.

Notably, it employs an active learning system and seamlessly integrates into Python-based workflows, allowing for rapid iteration in model training.

Roboflow Annotate swiftly labels training data, prioritizing automation and real-time teamwork.

Its strength lies in AI-assisted labeling, leveraging pre-trained models for efficient and accurate annotation.

2. Unique Features

Prodigy AI introduces a revolutionary approach to data annotation with a Tinder-like efficiency, simplifying complex tasks.

Additionally, it supports human-in-the-loop workflows, gaining trust from organizations like The Guardian.

Roboflow Annotate distinguishes itself with automated labeling capabilities powered by Segment Anything (SAM).

Its focus on teamwork is evident in the tools provided for project management, task assignment, and secure real-time collaboration.

3. Use Cases

Prodigy AI is ideal for data scientists, machine learning engineers, and organizations emphasizing agile data collection, model training, and idea exploration.

It excels in named entity recognition, object detection, and natural language processing tasks.

Roboflow Annotate caters to teams needing quick and accurate labeling of large datasets, particularly those focused on automation and collaboration.

It is well-suited for object detection, image classification, and multi-label classification tasks.

4. Other Features

Prodigy AI’s adaptability is underscored by its full scriptability and extensibility, seamlessly integrating into Python-based workflows.

It streamlines the transition from prototype to production, allowing users to export trained models as Python packages.

Roboflow Annotate emphasizes scalability, offering fully managed labeling services for efficiently scaling annotation tasks.

It ensures secure collaboration through role-based access and project management tools.

5. Pricing

Prodigy AI’s pricing model includes a Personal Plan at USD 390 per lifetime license and a Company Plan at USD 490 per seat (available in packs of 5 seats).

These plans offer lifetime licenses with free upgrades for the first 12 months.

Roboflow Annotate provides a Public plan for free, catering to personal, open source, and research needs.

The Starter plan, priced at $249 per month, suits individuals and businesses under 100 employees.

For enterprises with custom requirements, the Enterprise plan offers tailored solutions at custom pricing.

Prodigy AI Review

Prodigy is a scriptable annotation tool leveraging artificial intelligence and is so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration.

Prodigy AI, introduced in 2017 by Explosion, the creator of spaCy, revolutionizes machine teaching through its downloadable annotation tool.

Addressing the challenges of annotation and training, Prodigy enables data scientists to conduct rapid prototyping, accelerating project timelines and fostering experimentation.

Prodigy is a downloadable, scriptable annotation tool for AI, machine learning, NLP, and computer vision tasks such as named entity recognition, text classification, object detection, and more.

Let’s talk through its features and how they help data scientists.

1. Efficient Model Training

Prodigy AI, a downloadable annotation tool for AI developed by the makers of spaCy, introduces a paradigm shift in data annotation.

Prodigy’s radically efficient approach, incorporating a spacy model in the loop, enables data scientists to seamlessly handle annotations and iterate rapidly during model training.

The annotation server, built into Prodigy, streamlines the annotation process by actively involving the model, making it a fine-tuning powerhouse.

2. Agile Data Collection

Leveraging insights from the popular spaCy library, Prodigy has a built-in representation for computer vision tasks, including named entity detection.

Its active learning system, an optional component, ensures that users train production-quality models with minimal examples.

This feature accelerates agile data collection and model development, allowing users to move faster and be more independent in their projects.

3. User-Focused Design

Prodigy’s web app, following modern UX principles, simplifies complex annotation tasks such as named entity recognition and translation.

The tool is designed like a “Tinder for data,” focusing on one decision at a time, encouraging users to spend more time comprehending problems and interpreting results.

Prodigy’s adaptability, with its Python library and command-line interface, makes it easy for annotators to start.

4. Adaptability and Extensibility

Fully scriptable and extensible, Prodigy integrates seamlessly into Python-based data science workflows.

As an annotation tool for AI, it automates the annotation process, neatly creating training data for natural language processing and computer vision tasks.

Its extensibility allows users to create custom workflows, making Prodigy a valuable asset for various projects, from chatbot functionality to object detection.

5. Swift Idea Exploration and Export to Production

Prodigy facilitates swift idea exploration, allowing users to rapidly test and iterate on new concepts.

Once a model is trained, Prodigy supports seamless export as a versioned Python package, providing a smooth transition from prototype to production.

This feature streamlines the often challenging process of transitioning from experimental models to fully deployed solutions.

6. Human-in-the-loop Workflows

Trusted by organizations like The Guardian, Prodigy supports human-in-the-loop workflows, which are essential for effective teaching and model evaluation through human-AI interfaces.

Prodigy’s design ensures that the right decisions are easy, encouraging data scientists to spend more time understanding their data.

Conclusion

Prodigy AI, incorporating transformer algorithms and neural networks, offers a groundbreaking approach to data annotation.

The ability to handle batches of examples efficiently, coupled with a seamless command line interface, empowers data scientists in retraining models and exploring new ideas swiftly.

Its prowess in handling evaluation data ensures the production of high-quality models, making Prodigy AI a versatile and powerful tool for programming and advancing the field of machine teaching.

The integration of transformer technology allows for the creation of innovative and adaptive models, solidifying Prodigy AI as a frontrunner in efficient and effective AI model training.