Cloud

Infrastructure
About 1 min read

An IT model that provides computing resources on-demand over the internet. In the AI field, it serves as a core foundation for flexibly scaling the high-performance computing resources needed for large model training and inference—without building expensive GPU hardware—and instantly leveraging the latest AI models via APIs.

Also known as
Cloud ComputingCloud Computing

Detailed explanation

Cloud computing is a model where IT resources such as servers, storage, and networking are accessed on-demand over the internet without owning physical servers. Modern cloud has evolved beyond simple infrastructure provision into 'AI-dedicated platforms.' Major providers like AWS (Bedrock), Azure (OpenAI Service), and Google Cloud (Vertex AI) integrate the latest GPU resources like H100 with pre-trained foundation models (FMs) and specialized MLOps environments. This allows enterprises to quickly train, fine-tune, and deploy generative AI models while minimizing initial capital expenditures. In addition, pay-as-you-go pricing models enable both cost-efficiency and scalability for AI services.

Why it matters for AI tool selection

As the scale of AI models grows, the cost burden for individual enterprises to purchase and maintain high-performance computing resources (such as GPUs) has become severe. The cloud is the only realistic alternative to solve this, serving as a key factor in business competitiveness by providing access to the latest hardware, massive data processing capabilities, and managed AI services that accelerate development.

What to check when adopting cloud for AI

  • Verify the available regions for the latest GPUs (H100, A100, etc.) and specialized accelerators (TPU, Trainium)
  • The range and exclusivity of foundation models (GPT, Gemini, Claude, etc.) offered within the platform
  • The level of native integration support with existing data repositories and MLOps tools
  • Compliance with data security and governance (Sovereign Cloud) regulations and SLA conditions

Examples of AI utilization by service model

You can train proprietary models by directly provisioning GPU instances via IaaS, or use PaaS offerings like Vertex AI to automate everything from data preprocessing to model deployment. Alternatively, you can instantly implement translation or speech recognition features into your services by calling SaaS-style AI APIs.

Commonly confused operational models

On-premise

A method of directly building physical servers in-house. While offering high security and control, it requires massive initial costs and has low scalability.

Edge Computing

A method of processing data near where it is generated (devices) rather than in a centralized cloud. Used in applications like autonomous driving where real-time processing is critical.

Related terms

GPUMLOpsSaaS