#neoclouds

#neoclouds

By neoclouds, I'm referring to GPU-centric, purpose-built cloud services that focus primarily on AI training and inference rather than on the sprawling catalog of general-purpose services that hyperscalers offer. In many cases, these platforms deliver better price-performance for AI workloads because they're engineered for specific goals: keeping expensive accelerators highly utilized, minimizing platform overhead, and providing a clean path from model development to deployment.

ChatGPT, launched in 2022, began making a significant impact on the market by late 2023, according to Synergy Research Group. The company's chief analyst, John Dinsdale, points out that cloud market leaders have experienced accelerated revenue growth over time. Additionally, the emergence of numerous neocloud companies ( see box: What is a neocloud?) has further strengthened the already positive momentum in the market.

AI inference is the process by which a trained large language model (LLM) applies what it has learned to new data to make predictions, decisions, or classifications. In practical terms, the process goes like this. After a model is trained, say the new GPT 5.1, we use it during the inference phase, where it analyzes data (like a new image) and produces an output (identifying what's in the image) without being explicitly programmed for each fresh image. These inference workloads bridge the gap between LLMs and AI chatbots and agents.

Internal Amazon documents reveal that AI startups have been delaying AWS adoption, diverting early budgets toward AI models and tools, inference platforms, and new "neoclouds" that sell GPU access, according to a blockbuster scoop from Business Insider's Eugene Kim. Instead of relying on AWS for traditional cloud compute and storage, founders are starting with OpenAI, Anthropic, and AI tool providers such as Vercel, along with specialized GPU providers like CoreWeave.