In tech terms, a neocloud is a new breed of cloud infrastructure purpose-built for AI and high-performance computing (HPC).
Unlike traditional hyperscale cloud providers (like AWS or Azure), neoclouds focus on delivering raw GPU power, low-latency performance, and specialised environments for compute-intensive workloads.
🧠 Key Features of Neoclouds
- GPU-as-a-Service (GPUaaS): Optimised for training and running large AI models.
- AI-native architecture: Designed specifically for machine learning, deep learning, and real-time inference.
- Edge-ready: Supports distributed deployments closer to users for faster response times.
- Transparent pricing: Often more cost-efficient than hyperscalers for AI workloads.
- Bare-metal access: Minimal virtualisation for maximum performance.
🏗️ How They Differ from Traditional Clouds
| Feature | Neoclouds | Hyperscale Clouds |
|---|---|---|
| Focus | AI & HPC workloads | General-purpose services |
| Hardware | GPU-centric, high-density clusters | Mixed CPU/GPU, broad service range |
| Flexibility | Agile, workload-specific | Broad but less specialised |
| Latency | Ultra-low, edge-optimized | Higher, centralized infrastructure |
| Pricing | Usage-based, transparent | Often complex, with hidden costs |
🚀 Who Uses Neoclouds?
- AI startups building chatbots, LLMs, or recommendation engines
- Research labs running simulations or genomics
- Media studios doing real-time rendering or VFX
- Enterprises deploying private AI models or edge computing
Think of neoclouds as specialist GPU clouds—like a high-performance race car compared to a family SUV.
Both get you places, but one’s built for speed, precision, and specialised terrain.
