Potato vs Prodigy

Potato is an open-source platform for annotating text, audio, images, video, and qualitative data. Tasks are configured in YAML, and its maker says an annotation interface can be created without programming. The homepage lists 61 annotation types, including classification, spans, bounding boxes, polygons, and qualitative coding. Potato also supports annotating multi-agent teams on an interaction graph and evaluating computer-use, voice, and video agents. AI assistance can suggest labels, highlight keywords, check quality and consistency, pre-annotate material, and generate explanations. Listed integrations include OpenAI, Anthropic Claude, Google Gemini, Ollama, HuggingFace, OpenRouter, vLLM, YOLO, and LangChain. Imports cover text, image, audio, video, and PDF or HTML documents; exports include JSON, JSONL, CSV, CoNLL, Hugging Face, COCO, and YOLO. Potato runs locally or on users’ own servers and supports offline use. The free self-hosted plan includes all features and has no usage limits. It is licensed under GNU GPL version 3 or later; installation with pip requires Python 3.7 or later.

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.