Doccano vs Prodigy

Doccano is a free, open-source data-labeling tool for machine-learning practitioners. Its supported annotation tasks include text classification, sequence labeling, and sequence-to-sequence annotation, with uses such as sentiment analysis, named entity recognition, and text summarization. Users can set up projects, import datasets, add collaborators, annotate examples, and export labeled data. REST APIs let scripts connect to Doccano for model-assisted labeling, and multiple people can work collaboratively. Installation options include pip, Docker, Docker Compose, source, or cloud deployment. The installation guide lists Linux, Windows, and macOS machines running Python 3.8 or later. SQLite 3 is the default database; PostgreSQL configuration is described, and MySQL is mentioned as an option. Imported datasets can be stored using Amazon S3 or Google Cloud Storage. The project also lists mobile and multi-language support, emoji support, and a dark theme. Integration guidance demonstrates Amazon Comprehend Sentiment Analysis and custom REST APIs for auto-labeling. One upgrade caveat is important: the installation guide warns that an upgrade using SQLite 3 can cause database loss.

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.