FastText

I want to encode the news article which has many named entities like person names, organization names, etc. means many vocabulary words are out of a dictionary. My dataset is having around 3 million articles and the average length of an article is 650. What are the benefits or drawbacks if I used FastText word embedding?

Can you please advise which one to choose FastText Or Gensim, in terms of:

1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
2. Performance
3. Customization of Intermediate steps
4. FastText and Gensim both have the same underlying libraries
5. Use cases each one tries to solve
6. Unsupervised Vs Supervised dimensions
7. Ease of Use.

Please mention any other points that I may have missed here.