We do electronic health records, electronic billing, and telemedicine, using artificial intelligence.
We're quite pleased with NetApp Cloud Volumes Service for Google Cloud and we're using it for HIPAA compliance, as well as in Luxembourg, where we have another one of our business units, for GDPR. It's meeting both of those requirements. Google is opening up a new data center in Luxembourg this month, so we've been coordinating with NetApp on that.
The tools are very nice and we've been working with them on upscale and downscale of storage. We've been working with them on the architecture for doing backups and how we do that for the U.S. and in Europe, and they then work with Google.
On the administrative side, the tools are very good for our backend guys. Their console makes it easy to look at our backup and storage availability, and the tools make it easy to manipulate the volumes on the storage and move things around.
NetApp Cloud Volumes Service for Google Cloud has multiple performance tiers that allow you to closely align it with your workload performance requirements. From the console you can move different volumes around, based on the CPU setup that you want to use. With backups, you need less compute to move the data around, so we use their tools very effectively to tune the environment for storage.
We are able to fine-tune storage and capacity on the fly, as our needs grow and shrink. We're using their tools and their dashboard, which allow us to view our peak loads and to tune the system as we go. That probably reduces our storage costs by 25 percent in the way we are currently using it. It has also lowered our costs by cutting about 10 percent of our time. We need fewer manpower resources to run it. We've got two people, whereas in some cases, in other environments, it takes four to five people.
We also see Windows infrastructure savings. A Windows Server is a little over $1,000 a month. Instead of needing two, we only need one, so the service has helped in regards to our compute costs.
The service enables us to share data across VMs. Those VMs are based on the environments that we have, including dev, test, and production. We move volumes around pretty rapidly when we need to do load testing and scale testing. When you're doing scale loads, you're running routines that will scale up and you look for the failure points, where the VM will crash. Those test loads help us to understand scale.