So I am going to answer it myself. On batched data, it's a lot faster than 200ms per embedding and I'd say on a par with Infersent. On the other hand, I couldn'

So I am going to answer it myself. On batched data, it's a lot faster than 200ms per embedding and I'd say on a par with Infersent. On the other hand, I couldn't get statistical performance in the same ballpark as Infersent and I had to backtrack. This was training a logistic regression on the embeddings to filter some text streams according to my preferences. If I had, I would have preferred Basilica as Infersent is py2 only, hard to install and distribute and a battery killer on my laptop. Its vectors are also 4x bigger. I experienced some server errors and the team at Basilica was very responsive and fixed it, very pleased with the interaction. It would be important IMHO to publish some benchmark results for these embeddings, as it's usually done in the universal embedding literature, or serve published embeddings with known performance when licensing terms are favorable.