Doccano
7.0#4 in data labeling softwareFree planFree planWebAPILinuxmacOS
VisitDoccano vs Prodigy
What both publish
| Fact | Doccano | Prodigy |
|---|---|---|
| Deployment | The repository lists one-click deployment options for AWS and Heroku, and the guide supports installation locally or in the cloud. | The standard Python web server can be deployed on any cloud provider or entirely on-premise. |
| Integrations | The auto-labeling guide demonstrates Amazon Comprehend Sentiment Analysis and allows users to configure a custom REST API. | Prodigy has first-class support for spaCy, plugins for Hugging Face models, and integrations with major LLM API providers. |
| Purpose | Doccano is an open-source text annotation tool for machine-learning practitioners. | Prodigy is a downloadable developer tool for creating training and evaluation data for machine learning models. |
| Support | The project directs users to its FAQ and invites them to contact the author for help and feedback. | The Personal license includes community forum support; Company licenses include priority forum and email support. |
Doccano plans
- doccanoFree
Prodigy plans
- Personal$390, paid once
- Company$490, paid once
Pick Doccano if…
Doccano suits machine-learning practitioners creating labeled text datasets for tasks such as sentiment analysis, entity recognition, and summarization. Teams can collaborate on annotations or connect scripts through its REST API.
Pick Prodigy if…
Prodigy suits developers building machine-learning data workflows, especially those who need local or air-gapped operation. Annotation tasks can be completed in the web app without programming, but the tool assumes basic Python and command-line familiarity.