For a mature company that kickstarted supercomputing as we know it, Cray has done a rather impressive job of reinventing itself over the years.

From its original vector machines, to HPC clusters with proprietary interconnects and custom software stacks, to graph analytics appliances engineered in-house, and now to machine learning, the company tends not to let trends in computing slip by without a new machine.

However, all of this engineering and tuning comes at a cost—something that, arguably, has kept Cray at bay when it comes to reaching the new markets that sprung up in the "big data" days of Hadoop, when storage and compute performance at scale could be captured on the cheap. Cray systems are a lot of a things, but inexpensive is not one of them—something that no one denies, but those that are dedicated Cray shops overlook when it comes to mission-critical performance.

With both the reinvention and cost elements in mind, it is surprising that until today, renting a Cray super has been out of reach, especially when such live, remote testing of a workload on a commodity versus Cray cluster could bring new converts. In short, the key to continued reinvention is providing the ability for previously out-of-league users to commit and add to the base that informs next-generation machines.

The first company to provide "Cray as a Service" is Boston-based cloud and data center co-lo provider, Markley, which intends to find rich business with the local (Cambridge, MA) genomics and biotech set. While Markley's CTO, Patrick Gilmore, tells The Next Platform, that they are tracking how Cray's XC line of more traditional supercomputers might fit the needs of a wider set of users, they are the first to offer the Urika-GX graph analytics appliance for rent on a reservation basis.

Markley was an early player in the datacenter space and has firm footing in biotech-rich Boston. It is housed in a nearly million-square foot facility that serves as the telco routing and switching hub for all of New England. For those local users with fiber in-house, Markley charges a port fee to get around the major problem of bioinformatics clouds–expensive data movement, although the company does web-based business with other life sciences companies elsewhere in the U.S.. Gilmore says that while the larger cloud providers, including Amazon, Google, and others, have indeed built tooling to draw in these same target users in bioinformatics and pharma, the platform is not tailored for performance on key applications and data movement creates an expensive proposition.