AWS doesn’t make sense for scientific computing
Scientific computing has a completely different usage profile than modern apps. Scientists need powerful computers and massive data transfer that runs on relatively simple infrastructure. Most cloud computing has infrastructural complexity that isn’t necessary for scientific computing, and that complexity comes at a cost.
From the blog post, a break up of the anticipated costs:
- ~$2670.36/mo for a c5a.24xlarge AWS on-demand instance
- ~$1014.7/mo for a c5a.24xlarge AWS reserved instance on a three-year term, paid upfront
- ~$558.65/mo on OVH Cloud[1]
- ~$512.92/mo on Hetzner[2]
- ~$200/mo on your own infrastructure as a large institution[3]
This is 10x more expensive to use AWS. Don’t forget the data egress costs, too.
AWS realized that large successful web apps have high data egress[5]. Those customers that can afford extra bandwidth charges, will struggle to migrate off AWS, and are an easy way to increase margins. In contrast, a small scientific computing project can transfer more data than large successful apps: a month-long DNA sequencing project can generate 90 TB of data[6]. At $0.09 per gigabyte, that costs more than $8k to transfer out of AWS.
Maybe multi-million dollar “grants” can cover those costs, but money isn’t unlimited. There are clear upsides to having on-premises computing. These blog posts are difficult to come by (especially on costs), as there are no clear returns on investments for cloud computing.
RadOncNotes 