[avin_regmi]

Sorry, I should've been more clear. Both predictions for TF serving and panini serving was done in a single thread in the same specification machine. We used a simple model for image classification of CIFAR dataset. Roughly, 500 predictions were made for panini and 200 predictions for TF serving. The graph on the website is for throughput. I'm planning to write a medium post soon regarding the benchmark test. There are many other projects getting higher throughput compare to TF serving. I've heard TF Serving could be optimized to make it more efficient but making it more optimized is not documented properly. We're planning to make it open source if there is enough interest from the community!

[ScoutOrgo]

What is your business model if your platform is free? Either that price has to change, or you plan on making money on the same thing all other free services run on: data.

The site isn't very upfront about it, which is the sketchy part. Other than that, it looks much more straight forward than other options (I did watch the youtube tutorial). I like the idea, just question the motives.

[avin_regmi]

Our platform is free for the beta users to try it with limit of 2GB per model. We are just starting and we haven't decided on our business model yet.

If a user downloads panini to their private server and use it that will always be free since there is not infrastructure cost for us. If you're deploying it in our website we will be charging you to pay for the infrastracture cost.

Our main goal currently is to find out if people find this product useful and if it's worth for us to spend more time working on it. Thanks for watching the YouTube tutorial and if you have further questions, please contact us. Thanks

[ScoutOrgo]

Fair enough, thanks for answering.

[tedivm]

I'd definitely be willing to try this if it was open source.

[avin_regmi]

What are you currently using to server ML models?