Thomas Robinson is COO of
Domino Data Lab
, where he is responsible for revenue and go-to-market, leading sales, marketing, professional services, customer support and partnerships.
The great cloud migration has revolutionized IT, but after a decade of cloud transformations, the most sophisticated enterprises are now taking the next generational leap: developing true hybrid strategies to support increasingly business-critical data science initiatives and repatriating workloads from the cloud back to on-premises systems. Enterprises that haven’t begun this process are already behind.
The great cloud migration
Ten years ago, the cloud was mostly used by small startups that didn’t have the resources to build and operate a physical infrastructure and for businesses that wanted to move their collaboration services to a managed infrastructure. Public cloud services (and cheap capital in a low interest-rate economy) meant such customers could serve a growing number of users relatively inexpensively. This environment enabled cloud-native startups such as Uber and Airbnb to scale and thrive.
Over the next decade, companies flocked en masse to the cloud because it lowered costs and expedited innovation. This was truly a paradigm shift and company after company announced “cloud-first” strategies and moved infrastructures wholesale to cloud service providers.
Cloud-first strategies may be hitting the limits of their efficacy, and in many cases, ROIs are diminishing, triggering a major cloud backlash.
The growing backlash
However, cloud-first strategies may be hitting the limits of their efficacy, and in many cases, ROIs are diminishing, triggering a major cloud backlash. Ubiquitous cloud adoption has given rise to new challenges, namely out-of-control costs, deepening complexity, and restrictive vendor lock-in. We call this cloud sprawl.
The sheer quantity of workloads in the cloud is causing cloud expenses to skyrocket. Enterprises are now running core compute workloads and massive storage volumes in the cloud — not to mention ML, AI, and deep learning programs that require dozens or even hundreds of GPUs and terabytes or even petabytes of data.
The costs keep climbing with no end in sight. In fact, some companies are now spending up to twice as much on cloud services as they were before they migrated their workloads from on-prem systems. Nvidia estimates that moving large, specialized AI and ML workloads back on premises can yield a 30% savings.
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