Core Insights - The global cloud computing market is projected to reach nearly $2 trillion by 2030, driven by the increasing demand for AI integration and services [2][3] - China's cloud computing market is expected to grow to 828.8 billion yuan in 2024, reflecting a year-on-year increase of 34.4% [3] - The rapid growth of AI technologies is transforming cloud computing services, shifting from traditional models to AI-driven solutions [6][7] Global Market Overview - The global cloud computing market is forecasted to reach $692.9 billion in 2024, with a year-on-year growth rate of 20.3% [2] - The demand for IaaS, PaaS, and SaaS is increasing, particularly due to AI model training and application services [2][3] China's Cloud Computing Landscape - China's cloud computing market is experiencing significant growth, with a projected market size of 828.8 billion yuan in 2024 [3] - The public cloud market is expected to reach 621.6 billion yuan, growing by 36.6%, while the private cloud market is anticipated to reach 207.2 billion yuan, growing by 29.3% [3][5] - The integration of technologies like quantum computing and blockchain with cloud computing is expected to expand market boundaries [3] Market Dynamics and Competition - The IaaS market in China is projected to reach 420.1 billion yuan in 2024, with intelligent computing services being the primary growth driver [5] - Major players in the public cloud IaaS market include Alibaba Cloud, Tianyi Cloud, Mobile Cloud, Huawei Cloud, and Tencent Cloud, while the PaaS market is led by Alibaba Cloud, Baidu Cloud, Huawei Cloud, Tencent Cloud, Tianyi Cloud, and Mobile Cloud [5] Challenges and Data Security - The surge in data volume due to AI development poses significant challenges for cloud computing, necessitating a shift towards model-as-a-service delivery [6][9] - Data security is critical for the stable operation of AI cloud services, with increasing data interactions leading to complex security challenges [9][10] - The emergence of low-quality data can adversely affect AI model performance, highlighting the need for robust data management and security measures [9][10]

Core Insights - The cloud computing market in China is expected to maintain a growth rate of over 20% during the "14th Five-Year Plan" period, driven by advancements in artificial intelligence large models [1][7] - The global cloud computing market is projected to reach nearly $2 trillion by 2030, with a market size of $692.9 billion in 2024, reflecting a year-on-year growth of 20.3% [2][3] - In 2024, China's cloud computing market size is anticipated to reach 828.8 billion yuan, representing a year-on-year increase of 34.4% [1][3] Market Trends - The global cloud computing market is experiencing steady growth, with significant contributions from IaaS, PaaS, and SaaS segments [2] - The public cloud market in China is expected to reach 621.6 billion yuan in 2024, growing by 36.6%, while the private cloud market is projected to reach 207.2 billion yuan, with a growth rate of 29.3% [3][5] - The integration of technologies such as quantum computing, blockchain, and artificial intelligence is expected to further expand the market boundaries of cloud computing [3] Demand Drivers - Intelligent computing services and intelligent agents are identified as the main growth drivers for the IaaS and SaaS markets [5] - The demand for intelligent computing services is a significant factor contributing to the growth of the public cloud IaaS market, which is expected to reach 420.1 billion yuan in 2024 [5] Industry Dynamics - The competitive landscape is characterized by a clear head-to-head market structure, with leading players like Alibaba Cloud, Huawei Cloud, and Tencent Cloud dominating the public cloud IaaS market [5] - Mid-tier companies are attempting to break the market structure by focusing on niche areas and enhancing AI capabilities [5] Future Outlook - The cloud computing market in China is projected to exceed 3 trillion yuan by 2030, indicating robust growth potential [3] - The shift from traditional computing power leasing to model-as-a-service is anticipated due to the increasing demand for AI model training and inference [6] Data Security Challenges - The rapid growth of data generated in cloud environments poses significant challenges, particularly in data security, as the volume of data is expected to reach approximately 58.53 zettabytes by 2029 [9] - The emergence of new business models, such as intelligent agent services, will drive collaborative development across the cloud computing industry [6][9] Regulatory Environment - National policies are increasingly focusing on data security, with regulations aimed at establishing a robust framework for AI cloud data security [10] - Recommendations include creating a comprehensive AI cloud data security standard system to address issues like data leakage and bias [10]

Core Insights - The AI model, AI computing power, and AI applications are driving each other in a spiraling upward trend, leading to the evolution of traditional cloud architecture towards AI-native cloud solutions [1][2] - The public cloud market in China is expected to grow at a rate of 17.7% in the second half of 2024, according to IDC [1] - Fire Mountain Engine has reduced the cost of large model inference by over 90%, which not only lowers the cost for customers but also pressures other cloud providers to follow suit [1] - The daily token call volume for public cloud large models in China is projected to reach 952.2 billion by December 2024, a tenfold increase from 96.3 billion in June 2024 [1] Company Insights - Fire Mountain Engine holds a market share of 46.4% in the total large model call volume for 2024 [2] - The daily token call volume for Doubao's large model reached 16.4 trillion by May 2025, a 137-fold increase from 120 billion in May 2024 [2] - The transition from PC to mobile and now to AI era signifies a shift in technology focus from web pages and apps to AI agents [2] Industry Insights - The innovation in cloud computing infrastructure is being driven by changes in application paradigms, moving away from traditional IaaS, PaaS, and SaaS models [2] - AI-native cloud architecture is being redefined based on business architecture rather than technical division, focusing on optimizing computing, storage, and network architecture around agents [2] - The goal is to enhance the speed and volume of token generation in a given time frame to improve the responsiveness of AI applications [2][3]