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中國雲市場，告別「白菜價」時代
#cloud-pricing#china-market#compute-profitchinese-cloud-market
💡中國雲價上漲—轉型影響全球AI算力成本！
⚡ 30 秒速覽
有什麼變化
告別「白菜價」時代
為什麼重要
提高AI訓練成本但穩定供應，有利長期算力密集工作負載基礎設施規劃。
下一步行動
比較阿里雲GPU實例價格與全球對手，優化AI模型訓練預算。
誰應關注：Enterprise & Security Teams
關鍵要點
- •告別「白菜價」時代
- •從燒錢換市場到算力換利潤
- •強調算力盈利
- •雲產業動態成熟
🧠 深度解析
背景與延伸：來自公開資料，非原文內容。引用 12 個來源。
🔑 增強重點摘要
- •AI Inference Marginal Cost Paradox: Unlike traditional cloud services where economies of scale drive deflation, the marginal cost of AI computing power increases with scale due to hardware scarcity and extreme energy density, forcing a reversal of the 20-year deflationary trend.
- •Hardware-Linked Pricing Tiers: Price increases are specifically targeted at high-performance infrastructure, such as Alibaba's in-house Zhenwu 810E AI chips and CPFS (Cloud Parallel File Storage), signaling a shift from general-purpose CPU subsidies to high-margin GPU/NPU monetization.
- •Token-Based Revenue Pivot: Major providers are transitioning from flat-rate VM pricing to usage-based billing for Large Language Models (LLMs); for instance, Tencent Cloud's Hunyuan series saw price increases of over 400% as they transitioned from free public beta to commercial production.
- •Supply Chain Cost-Push: The 'cabbage price' era ended not just by choice but by necessity, as the procurement costs for advanced AI accelerators (both international H20/H100 and domestic alternatives) and liquid-cooling infrastructure have surged significantly since 2025.
📊 競品分析▸ Show
| Provider | 2026 Pricing Action | Primary AI Focus | Market Share (Q3 2025) |
|---|---|---|---|
| Alibaba Cloud | Hiked AI compute/storage prices by 5%–34% | Qwen Model Family & Zhenwu 810E Chips | 36% |
| Baidu AI Cloud | Hiked AI compute/storage prices by 5%–30% | Model-as-a-Service (MaaS) & Ernie Bot | 22.5% (AI Cloud) |
| Tencent Cloud | Shifted from free beta to usage-based billing | Hunyuan 2.0 & WeChat Ecosystem Integration | 9% |
| Huawei Cloud | Maintained stable pricing (as of March 2026) | Pangu Models & Ascend Chip Bundling | 16% |
| China Telecom | Positioning as 'National Cloud' | Sovereign AI & State-owned Enterprise (SOE) Cloud | ~RMB 114B Revenue |
🛠️ 技術深入
The technical shift involves a transition from general-purpose IaaS to AI-native infrastructure stacks:
- Compute Hardware: Deployment of Alibaba's T-Head Zhenwu 810E AI chips and Huawei's Ascend 910C clusters to mitigate international supply constraints.
- Storage Architecture: Adoption of Parallel File Systems (e.g., CPFS) capable of handling the massive I/O requirements of trillion-parameter model training and real-time inference.
- Network Latency: Implementation of the 'Eastern Data, Western Computing' (Dongshu Xisuan) standards, targeting a maximum 20ms latency for real-time AI Agent applications across national hubs.
- Billing Logic: Migration from 'Instance-per-Hour' to 'Token-per-Request' and 'Capacity Blocks' for dedicated AI training clusters.
🔮 前景展望基於引用來源的 AI 分析
Consolidation of Tier-2 Cloud Providers
Smaller players like UCloud and Wangsu, unable to sustain the massive CAPEX required for AI infrastructure, will be forced to pivot to niche services or face acquisition.
Rise of Sovereign AI Clouds
State-owned telecom operators (China Telecom/Unicom) will capture the price-insensitive government sector, leaving private giants to compete on high-performance commercial AI.
AI Agent-Driven Demand Surge
The explosion of autonomous AI Agents will shift the market from training-heavy to inference-heavy, making inference efficiency the primary competitive benchmark by 2027.
⏳ 時間線
2023-04
Alibaba Cloud initiates 'historic' price war with 50% cuts
2024-05
Price war extends to LLM API tokens among DeepSeek, ByteDance, and Alibaba
2025-08
MIIT proposes 'National Cloud' platform to standardize computing power sales
2026-01
AWS and Google Cloud raise global infrastructure prices, setting a precedent
2026-03-11
Tencent Cloud ends free public beta for Hunyuan models, shifting to paid usage
2026-03-18
Alibaba and Baidu announce simultaneous price hikes for AI computing power
📎 來源 (12)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
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原始來源： 钛媒体 ↗
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