# Prodigy v free **URL:** \ **Category:** Uncategorized\ **Created:** [March 6, 2023, 2:14am UTC](https://support.prodi.gy/t/prodigy-v-free/6412 "2023-03-06T02:14:47Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![MalikRumi](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/malikrumi/32/3654_2.png) [@MalikRumi](https://support.prodi.gy/u/MalikRumi)\ **Post date:** [March 6, 2023, 2:14am UTC](https://support.prodi.gy/t/prodigy-v-free/6412/1 "2023-03-06T02:14:47Z")
Hello all. This is my first post to this forum. I am seriously considering buying Prodigy, but, as I'm sure you know, there are a number of free and open source tools out that do similar things. I'm wondering if someone here can help justify this purchase over these other options? Thanks. ---
**Author:** ![koaning](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/koaning/32/230_2.png) [@koaning](https://support.prodi.gy/u/koaning)\ **Post date:** [March 6, 2023, 11:39am UTC](https://support.prodi.gy/t/prodigy-v-free/6412/2 "2023-03-06T11:39:42Z")
I cannot comment on any other tools, because I've always been a Prodigy user and have never really given other tools a proper spin. That said, here are some personal reasons why I've always appreciated Prodigy. 1. Prodigy has _great UI_. The annotation interfaces come with text that's clear. It provides keyboard shortcuts out of the box and also supports many non-English language too. The UI is opinionated, but it's always felt right. 2. Prodigy is _programmatic_. I'm totally free to customise the annotation exprience with any machine learning trick that can be written in Python. That also means that when I wrote [doubtlab](https://github.com/koaning/doubtlab) (a tool to find bad labels) it's super easy to get it working in Prodigy. Same with bulk labelling, explained here: [![](https://img.youtube.com/vi/gDk7_f3ovIk/maxresdefault.jpg "Bulk Labelling and Prodigy") ](https://www.youtube.com/watch?v=gDk7_f3ovIk) 1. Prodigy plays nice with the spaCy stack. Once you've annotated your data you merely need to run `prodigy train` to train a performant pipeline that can do both text classification and entity detection in one go. 2. Prodigy is _flexible_. It's pretty easy to re-use components to get a labelling interface that's just right for your use-case. You can re-use all the existing text/audio/image interfaces or just create your own via html and you can still re-use all the built-in sanity checks that Prodigy provides. I've used it for plenty of non-text use-cases that leverage scikit-learn and it remains a simple workflow. I don't know how many other tools properly support this, but it feels rather unique. Here's an example that I made for data deduplication, just to give _a_ example. [![](https://img.youtube.com/vi/kJ5Jb56T5uc/maxresdefault.jpg "Finding DUPLICATES IN TABULAR DATA with Jupyter and Prodigy") ](https://www.youtube.com/watch?v=kJ5Jb56T5uc) 1. Prodigy comes with a good support forum ( you know, this one 😉 ) where people who work on Prodigy can answer questions for you. You're talking to a Prodigy user who became a Prodigy developer later on. So feel free to take my opinion with a grain of salt, but I've found Prodigy to be _such_ a productivity booster as a data science consultant back when I first used it that I can genuinely recommend it to folks. It really helps to have an annotation tool in your toolbelt just to quickly bootstrap a dataset for ML or to confirm the data quality of pre-existing datasets. ---
**Author:** ![MalikRumi](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/malikrumi/32/3654_2.png) [@MalikRumi](https://support.prodi.gy/u/MalikRumi)\ **Post date:** [August 11, 2023, 8:38pm UTC](https://support.prodi.gy/t/prodigy-v-free/6412/3 "2023-08-11T20:38:03Z")
Thanks for such a thorough answer. I'm sure you'd have gotten lots of points if this discussion was in Stack Overflow.. If there still is a Stack Overflow...