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Comment by exhilaration | Hacker News Reader

This is nice, but I don't see the pricing after the free trials. The Pitney Bowes data [1] they used as an example in linked article only shows $0 for the free trial, not what it's going to cost you afterwards. It'd be nice to know the long term cost before tying this data into your business.

[1] https://aws.amazon.com/marketplace/pp/prodview-bwf7mapyyjzom...

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Their screenshot shows an "autorenew" enabled on a $0 free trial with zero information on how much it will cost after the free trial is up.

You shouldn't allow people to sign up to "autorenew" at the end of their free trial period without clearly displaying the price that will apply when the trial ends.

It looks like the trial dataset in this case is simply a truncated subset of the full dataset (only San Francisco zip code boundaries). It would likely remain free forever under this model of "trial".

AWS Data Exchange product manager here. The auto-renew here only applies to the free trial product that you're subscribing to, and would therefore auto-renew at $0. You can view and subscribe to the full product separately (it's technically a separate product), which will show its price. Hope this helps.

The other datasets show prices for me when I login. A few financial datasets I checked seemed over-priced by as much as 10x.

I had the same thought! I submitted a request for one of the 'free' one month buckets of non-profit consumer geo-data, and clicked no auto-renew. could potentially have some value (though I don't think much). I don't know how much due diligence they will do or if they will just accept our request automatically.

> This is nice, but I don't see the pricing after the free trials.

AWS charges are really obscure. I think they make an effort at making it hard to estimate/determine how much you'll be charged. (ie: You charged for EC2 time, bandwidth, storage, etc...). That makes it very hard to see where the money is going.

At Scale (scale.com), we strongly believe that the “open-source” alternative to this is pretty critical.

We’ve built this index for autonomous driving datasets (https://scale.com/open-datasets) and are building that out for other domains right now.

Open source data has been a pillar to progress in ML (starting with ImageNet). It should continue to be the case that data that enables researches is sufficiently democratized.

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