Your pricing page notably omits transfer pricing. Do you have free bandwidth between yourself and AWS/GCP/Azure or do you peer at any major exchanges?

Getting the data into and out of compute services is the most difficult part financially, at least in my experience.

Dan here (also Paperspace team). Totally agree that transfer costs are a significant pain point which is why we do not charge for it. We can peer with other providers (eg with AWS we can leverage Direct Connect directly from our datacenters) but most of our customers don't implement this unless they're moving major traffic.

That's a good start but do you have a partnership with anyone that can provide storage with free/low cost bandwidth to your service? Even Direct Connect is ridiculously expensive compared to transit.

>Getting the data into and out of compute services is the most difficult part financially, at least in my experience.

You can never forget that this is entirely because of compute services ripping you off, not because they're providing a valuable service in return for the transfer pricing.

Oh I never do forget. I currently colocate and buy transit and it's blindlingly obvious to me how much of a ripoff cloud egress is.

Even their "direct connect" services cost more than my transit does.

One of the biggest challenges with deep learning is training data. AWS makes loading large datasets easy with S3. What does Paperspace have to help with this? If I have to perform deep learning on multi-TB datasets in S3, any compute cost benefits get cancelled out by the increased data transfer cost from S3.

I've really enjoyed using your service, especially the cloud desktops. I use them for running Fusion 360 (windows only) from my ubuntu xps when I'm away from home.

Both the interface and GPU prices are fantastic.

Keep up the good work!

This is great!

I'm looking for a way to run serverless (Amazon Lambda style) GPU operations (preferably using OpenCL). Are there any plans for such a service in your platform?

We have definitely been thinking a lot about what that would look like (i.e. is it more of a job architecture, an API, clustering, etc). Would love to hear your thoughts on what GPU Lambda might look like. Feel free to hit me up directly dillon [@] paperspace [dot] com if you want to continue the conversation :)

We're adding sync support to Worker (which has GPU support) at Iron.io soon! This will allow you to run long running background jobs (current behavior) as well as sync serverless/faas Lambda-like functions within a single API.

DigitalOcean for GPUs, awesome! For someone wanting to play around learning more about machine learning, would one of your Standard GPU units be ideal? If so, which one would you recommend? (Or do you think I'd need a dedicated GPU unit?)

For ML/deep learning tasks you should definitely use a dedicated GPU. I would recommend our GPU+ (NVIDIA Quadro M4000/P4000) which has 8GB of VRAM and 1664 CUDA cores.

do you have spot instance pricing as well?