This is supported by MinIO, but not "as a service." Essentially you run MinIO everywhere (AWS, GCP, Azure, IBM, on-prem, OpenShift, Tanzu etc). In the public clouds you can either roll your own or use the marketplace offerings.
In effect, you are choosing MinIO object storage over the "stock" object storage (which is incompatible with the other clouds).
You can use MinIO's ILM policies to replicate, tier, etc.
You still pay for compute, network + drive but then pay MinIO vs. S3/Blob. There will be no egress fees.
Many companies that do this look at MinIO for object storage. Given they run in AWS, GCP and Azure, they will minimize or eliminate your application rewrites. They are cloud-native by design and very fast.
The key here is that TDA is packaged into an application that is designed explicitly for use by practitioners. All of the underlying math (and you know there is lots of it in TDA) is abstracted. What is shown is the groups and the atomic level explains (this group is here for these reasons e.g. they received albuterol upon admittance). Your instinct is correct, but that is what is interesting about this case - the hospital, without a single data scientist, was able to to achieve this with only slick SQL skills and engaged doctors.
Hi infinite8s, to get additional information on how that chart was made, you can to go https://www.mapd.com/product/ scroll down to the bar chart, and click “See Details” under the chart. Shows the machines used, queries, and the source data set and size. Note that the machine configurations used to generate the chart were normalized for equivalent cost on AWS, i.e. the chart is hardware-dollar normalized.
Look at the coloring around the rides near bridges. People take the subway down to the closest point and then take a cab home. The hybrid trip is both pocketbook friendly and probably faster.