Active Learning Toolbox vs Prodigy

Active Learning Toolbox has no write-up on Specifiction yet; its figures are above.

Prodigy is a downloadable developer tool for creating training and evaluation data for machine learning models. Its browser-based app supports annotation of text, images, audio, video, relations, and multiple choice. Computer vision workflows include image classification, segmentation, and object detection; built-in recipes cover named entity recognition, text classification, dependency parsing, transcription, speaker diarization, training, and evaluation. Active learning models are included for named entity recognition, text classification, part-of-speech tagging, and dependency parsing. Users can create workflows with Python recipes and build interfaces using HTML, CSS, and JavaScript. Prodigy supports spaCy, has plugins for Hugging Face models, and integrates with major LLM API providers. Data is accessible through Python and the command line, and can be stored in SQLite, MySQL, or PostgreSQL, or exported to JSON. It runs on the user's own machines, including air-gapped machines, and its Python web server can be deployed in the cloud or on-premise. Personal costs $390, paid once, excluding tax; Company costs $490, paid once per seat in packs of five, excluding tax. Both include 12 months of free upgrades. Prodigy assumes basic Python and command-line familiarity; trials are not offered to individuals.