Cost savings are a core part of the value proposition. Enterprises that invest heavily in generative AI and machine learning often face ballooning infrastructure costs as they scale. Neoclouds alleviate this pain point with optimized GPU services and streamlined infrastructure, allowing companies to scale AI applications without running up exorbitant bills.
Neoclouds challenge the big three
Neoclouds represent a generational shift that threatens to erode the market share of AWS, Microsoft Azure, Google Cloud, and other hyperscalers. The big players are investing in GPU-centric services for AI workloads, but their general-purpose design inherently limits how far they can specialize. Hyperscale cloud providers support workloads ranging from legacy enterprise applications to emerging technologies like Internet of Things. However, this breadth creates complexity and inefficiencies when it comes to serving AI-first users.
Neoclouds, unburdened by the need to support everything, are outpacing hyperscalers in areas like agility, pricing, and speed of deployment for AI workloads. A shortage of GPUs and data center capacity also benefits neocloud providers, which are smaller and nimbler, allowing them to scale quickly and meet growing demand more effectively. This agility has made them increasingly attractive to AI researchers, startups, and enterprises transitioning to AI-powered technologies.
