With the development of cloud computing, cloud services have evolved from traditional computing and storage resource provision to more valuable AI cloud services. AI cloud services not only provide underlying computing power support but also integrate model training, inference platforms, and Model as a Service (MaaS) and other capabilities to promote the implementation of AI technology in various industries. The full-stack AI cloud service focuses on out-of-the-box use, full-stack integration, and flexible expansion, aiming to lower the threshold for AI adoption.
PART.01
Infrastructure Layer (IaaS) in Full-Stack AI Cloud Services
The role of the IaaS layer is to serve the upper-layer PaaS andSaaSProvides computing power services and large-scale data storage centers. It offers underlying resources such as computing infrastructure (e.g., GPU/TPU clusters, distributed computing resources), storage, and network to support elastic scaling needs for AI model training and inference. As a basic server, IaaS's business model mostly relies on delivery through combination with other services.
IaaS products typically refer to AI cloud infrastructure provided by various vendors, such as AIHeterogeneous computing platformAs a cloud service model, it allows users to remotely access virtualized hardware resources through a management platform, paying for actual consumption. It provides flexibility, scalability, and economic benefits, enabling enterprises to quickly adapt to technological changes and demand fluctuations, thereby focusing on core business rather than infrastructure management.
Source: Analysis by Frost & Sullivan
PART.02
Platform as a Service (PaaS) in Full-Stack AI Cloud Services
The full-stack AI cloud service provides AI development toolchains (such as data annotation, model training frameworks, deployment platforms), algorithm libraries, and APIs to lower the threshold for enterprise AI development. It will be divided into AI development platforms and data governance platforms. The training, fine-tuning, deployment, and inference processes of large models, as well as the Agent development process, are extremely complex. The AI development platform encapsulates these complexities and provides an end-to-end model production line, allowing developers to focus on business logic and model applications.
Source: Analysis by Frost & Sullivan
PART.03
Model Application Layer (MaaS) in Full-Stack AI Cloud Services
In cloud services of the AI era, the application layer has evolved from transformation into MaaS, which is a reusable service packaged with multiple different AI models and their related capabilities.AI AgentThe emergence has brought about a qualitative change in the essence of application layer enterprise services, from 'letting customers work on their own' to 'accomplishing work for customers'. MaaS, namely Model as a Service, allows users to directly use AI model capabilities through cloud platforms by packaging them. By integrating a full-process toolchain, resource reuse, and sharing, it reduces the technical threshold and usage cost of model development, helping customers implement in real scenarios more quickly.
PART.04
Core Value of Full-Stack AI Cloud Services
Full-stack AI cloud services are becoming a key engine driving the intelligent upgrade of enterprises. They not only meet diverse business needs but also significantly improve the efficiency and value of AI applications.
Source: Analysis by Frost & Sullivan
PART.05
Build a trustworthy full-stack AI cloud service ecosystem
The development of full-stack AI cloud services is entering a deeper stage - building an end-to-end ecosystem that is secure and trustworthy. Future competition will no longer be merely about individual technical indicators but a comprehensive contest centered around security, reliability, compliance, sustainability, and ecological collaboration. Building an 'trusted full-stack AI cloud service ecosystem' has become industry consensus and an inevitable trend.
Source: Analysis by Frost & Sullivan
