""Greenops" didn't emerge because enterprises suddenly had a moral awakening. Rather, cloud bills became painful, regulators got serious, and sustainability teams started asking questions IT couldn't answer with confidence. Once companies realized that cloud consumption wasn't only a technical concern but also a financial and reputational risk, they did what they always do: They operationalized it. Let me explain. At first, greenops was mostly finops with a greener badge."
"Reduce waste, right-size instances, shut down idle resources, clean up zombie storage, and optimize data transfer. Those actions absolutely help, and many teams delivered real improvements by making energy and emissions a visible part of engineering decisions. The key innovation wasn't adopting new technology-it was adopting a new operating model that made "doing the efficient thing" repeatable. Greenops matters because it forces enterprises to confront the reality that the cloud is not a magical, consequence-free abstraction."
"Then AI arrived as more than a curiosity. It turned into an arms race. The difference between moving apps to the cloud and building an AI capability is the difference between renovating a house and pouring concrete for a new city. Modern AI, especially at the scale enterprises want in order to compete, demands dense compute, fast networks, accelerated hardware, and round-the-clock capacity. AI workloads are not only expensive, they are structurally predisposed to consume more power per unit of time than many traditional enterprise work"
Greenops originated from cost pressure, regulation, and sustainability demands, transforming finops into an operational model that made efficiency repeatable. Typical greenops actions include reducing waste, right-sizing instances, shutting down idle resources, cleaning up storage, and optimizing data transfer. Greenops creates governance linking architecture, procurement, and product decisions to measurable energy and emissions outcomes. AI changes the equation by requiring dense compute, fast networks, accelerated hardware, and constant capacity at enterprise scale. AI workloads consume more power per unit time than many traditional workloads, threatening prior efficiencies and complicating accurate carbon accounting.
Read at InfoWorld
Unable to calculate read time
Collection
[
|
...
]