云服务对比与展望研究
Strategic Analysis of Hyperscale Cloud Infrastructure: AWS, Microsoft Azure, and Google Cloud Platform (2025-2030)
1. Executive Summary: Market Geopolitics and Strategic Divergence
1. 执行摘要：市场地缘政治与战略分歧
The global cloud computing landscape in 2025 has crystalized into an oligopoly dominated by three hyperscale providers: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Collectively, these entities control approximately two-thirds of the global cloud infrastructure market, creating a competitive environment that has shifted from commoditized resource provisioning to a high-stakes arms race involving custom silicon, generative AI supercomputing, and quantum supremacy. As of early 2025, AWS maintains its market leadership with a share of approximately 31%, driven by a Year-over-Year (YoY) growth of 17.5%.1 Microsoft Azure follows firmly with a 23% market share but exhibits a more aggressive growth trajectory of 33%, fueled significantly by its enterprise integration and exclusive OpenAI partnership.1 Google Cloud, while holding a smaller 13% share, matches Azure's rapid expansion with 32% growth, leveraging its vertically integrated AI stack and data analytics dominance.1
2025 年的全球云计算格局已固化为由亚马逊网络服务（AWS）、微软 Azure 和谷歌云平台（GCP）三大超大规模服务提供商主导的寡头垄断局面。这些实体合计控制了全球约三分之二的云基础设施市场，通过定制芯片、生成式 AI 超级计算和量子霸权等高风险军备竞赛，将竞争环境从商品化资源供应转变为技术高地争夺。截至 2025 年初，AWS 以约 31% 的市场份额保持领先地位，年同比增长（YoY）为 17.5% 1。微软 Azure 以 23% 的市场份额紧随其后，但表现出更激进的增长轨迹，增长率达到 33%，这主要得益于其企业集成和独家 OpenAI 合作伙伴关系 1。谷歌云虽然仅占 13% 的份额，但凭借其垂直整合的 AI 栈和数据分析优势，以 32% 的增长率匹配了 Azure 的快速扩张 1。
This triangulation of the market reveals distinct strategic identities. AWS continues to operate as the "Builder's Cloud," prioritizing the broadest service catalog (200+ services) and the deepest maturity in infrastructure primitives, attracting startups and diversified enterprises.1 Azure has successfully positioned itself as the "Enterprise Computer," capitalizing on the inertia of the Microsoft software ecosystem (Office 365, Teams, Active Directory) to facilitate hybrid deployments and massive-scale AI adoption through pre-integrated "copilot" functionalities.3 Google Cloud distinguishes itself as the "Data & AI Factory," emphasizing deep technical innovation in containerization (Kubernetes), open-source standardization, and performance-per-watt efficiency through its TPU architecture.4 The narrative has moved beyond simple "lift-and-shift" migrations to strategic architectural alignment, where the choice of cloud provider dictates an organization's access to future hardware innovations and AI capabilities.
市场的这种三足鼎立揭示了截然不同的战略身份。AWS 继续作为“构建者的云”运营，优先考虑最广泛的服务目录（200 多项服务）和基础设施原语的最深成熟度，吸引了初创公司和多元化企业 1。Azure 成功地将自身定位为“企业计算机”，利用微软软件生态系统（Office 365、Teams、Active Directory）的惯性，通过预集成的“副驾驶”（copilot）功能促进混合部署和大规模 AI 采用 3。谷歌云则以“数据与 AI 工厂”自居，强调在容器化（Kubernetes）、开源标准化以及通过其 TPU 架构实现的每瓦性能效率方面的深厚技术创新 4。现在的叙事已超越简单的“直接迁移”，转向战略架构对齐，即云提供商的选择决定了组织获取未来硬件创新和 AI 能力的途径。
2. Global Infrastructure Resilience and Availability Architecture
2. 全球基础设施弹性与可用性架构
The fundamental value proposition of the public cloud is reliability. However, the architectural approaches to achieving high availability (HA) vary significantly among the providers, influencing disaster recovery strategies and application design.
公有云的核心价值主张是可靠性。然而，各提供商实现高可用性（HA）的架构方法存在显著差异，这直接影响了灾难恢复策略和应用程序设计。
2.1 Regions, Zones, and Logical Isolation
2.1 区域、可用区与逻辑隔离
All three providers utilize the construct of "Regions" and "Availability Zones" (AZs), but with critical implementation nuances. An AWS Region is a separate geographic area consisting of multiple, physically isolated AZs connected with low-latency, high-throughput, and highly redundant networking.6 Crucially, AWS implements independent mapping of AZs; the identifier us-east-1a for one account may correspond to a different physical data center than us-east-1a for another account.6 This shuffling is designed to distribute load evenly across physical infrastructure, preventing resource contention in a "popular" zone identifier. In contrast, Azure and GCP generally map zones consistently within a subscription or project but may have different topologies for resource availability.7
三大提供商均使用“区域”和“可用区”（AZ）的概念，但在实施细节上存在关键差异。AWS 区域是一个独立的地理区域，由多个物理隔离的 AZ 组成，这些 AZ 通过低延迟、高吞吐量和高度冗余的网络连接 6。至关重要的是，AWS 实施了 AZ 的 独立映射；一个账户的标识符 us-east-1a 可能与另一个账户的 us-east-1a 对应的物理数据中心不同 6。这种通过混洗的设计旨在将负载均匀分布在物理基础设施上，防止“热门”区域标识符中的资源争用。相比之下，Azure 和 GCP 通常在订阅或项目中一致地映射区域，但在资源可用性方面可能具有不同的拓扑结构 7。
2.2 Service Level Agreements (SLA) and Uptime Strategy
2.2 服务水平协议 (SLA) 与正常运行时间策略
A critical differentiator often overlooked in high-level comparisons is the Single Instance SLA. For legacy applications—often Windows-based—that cannot easily be refactored into a clustered, cloud-native architecture, Azure offers a distinct advantage. Microsoft provides a 99.9% uptime SLA for single virtual machines provided they use Premium SSD or Ultra Disk storage.8 This allows enterprises to "lift and shift" monolithic applications while maintaining compliance requirements.
在高层级比较中经常被忽视的一个关键差异化因素是单实例 SLA。对于无法轻易重构为集群式云原生架构的遗留应用程序（通常基于 Windows），Azure 提供了明显的优势。只要使用高级 SSD 或 Ultra Disk 存储，微软就为单个虚拟机提供 99.9% 的正常运行时间 SLA 8。这允许企业在保持合规性要求的同时，“直接迁移”单体应用程序。
In comparison, Google Cloud's standard single-instance SLA is 99.5%, which tolerates significantly more downtime per month. To achieve 99.9% on GCP single instances, customers must utilize specific Memory Optimized instance families, limiting flexibility for general-purpose workloads.10 AWS focuses its SLA commitments heavily on the Region-Level (Multi-AZ) architecture, promising 99.99% availability only when instances are deployed across two or more zones, reinforcing its philosophy that software must be architected for failure.12
相比之下，谷歌云的标准单实例 SLA 为 99.5%，这意味着每月容忍的停机时间要多得多。要在 GCP 单实例上达到 99.9%，客户必须使用特定的内存优化实例系列，这限制了通用工作负载的灵活性 10。AWS 将其 SLA 承诺重点放在区域级（多可用区）架构上，仅当实例部署在两个或更多区域时才承诺 99.99% 的可用性，这加强了其软件必须针对故障进行架构设计的理念 12。
2.3 Hybrid and Edge Computing Extensions
2.3 混合与边缘计算扩展
The extension of cloud infrastructure to the edge reveals further strategic divergence. AWS employs a hardware-centric approach with AWS Outposts, bringing proprietary racks of AWS hardware to on-premise data centers to ensure the same API experience and hardware capabilities (including Nitro system benefits) exist locally.1 Additionally, AWS Wavelength embeds compute resources directly into 5G carrier networks to minimize latency for mobile applications.1
云基础设施向边缘的延伸揭示了进一步的战略分歧。AWS 采用以硬件为中心的方法，通过 AWS Outposts 将专有的 AWS 硬件机架引入本地数据中心，以确保本地存在相同的 API 体验和硬件能力（包括 Nitro 系统优势）1。此外，AWS Wavelength 将计算资源直接嵌入 5G 运营商网络，以最大限度地减少移动应用程序的延迟 1。
Azure leverages Azure Arc, a software-defined control plane that extends Azure management services to any infrastructure, including competitors' clouds (e.g., managing Kubernetes clusters on AWS) and on-premise hardware.1 This reflects Microsoft's acknowledgement of a multi-cloud reality and leverages its software dominance. Google Cloud's Distributed Cloud (formerly Anthos) focuses heavily on containerization, using Kubernetes as the common abstraction layer to run workloads across on-premise, edge, and multi-cloud environments, prioritizing software consistency over hardware uniformity.5
Azure 利用 Azure Arc，这是一个软件定义的控制平面，将 Azure 管理服务扩展到任何基础设施，包括竞争对手的云（例如管理 AWS 上的 Kubernetes 集群）和本地硬件 1。这反映了微软对多云现实的承认，并利用了其软件优势。谷歌云的 Distributed Cloud（前身为 Anthos）非常注重容器化，使用 Kubernetes 作为通用抽象层，在本地、边缘和多云环境中运行工作负载，优先考虑软件一致性而非硬件统一性 5。
3. The Silicon Wars: Computational Architectures and Benchmarks
3. 芯片战争：计算架构与基准测试
The era of x86 (Intel/AMD) hegemony in the cloud is ending. The primary battlefield has shifted to custom ARM-based silicon, where providers design their own processors to optimize performance-per-watt and decouple from the supply chain constraints of traditional chipmakers.
云端 x86（Intel/AMD）霸权的时代正在终结。主战场已转移到定制的 ARM 架构芯片，提供商设计自己的处理器以优化每瓦性能，并摆脱传统芯片制造商的供应链限制。
3.1 The Rise of Custom ARM Silicon
3.1 定制 ARM 芯片的崛起
AWS Graviton4 (The Mature Incumbent):
AWS leads the market in maturity with the Graviton4, based on the ARM Neoverse V2 core. The key architectural advantage of AWS is not just the CPU, but the Nitro System. Nitro offloads networking, storage, and security functions to dedicated hardware cards, allowing the Graviton processor to dedicate nearly 100% of its cycles to the customer's workload.14 Independent benchmarks indicate that Graviton4 offers up to 30-40% better price-performance than comparable x86 instances for scale-out workloads like web servers and containerized microservices.14
AWS Graviton4（成熟的在位者）：
AWS 凭借基于 ARM Neoverse V2 核心的 Graviton4 在市场成熟度方面处于领先地位。AWS 的关键架构优势不仅在于 CPU，还在于 Nitro 系统。Nitro 将网络、存储和安全功能卸载到专用硬件卡上，允许 Graviton 处理器将其近乎 100% 的周期用于客户的工作负载 14。独立基准测试表明，对于 Web 服务器和容器化微服务等横向扩展工作负载，Graviton4 的性价比由比同类 x86 实例高出 30-40% 14。
Google Axion (The Challenger with Titanium):
Google entered the custom CPU arena later with Axion, also built on the ARM Neoverse V2 core. Google claims Axion delivers up to 50% better performance than comparable x86 instances and 30% better performance than general-purpose ARM instances.16 Similar to AWS Nitro, Google employs the Titanium system, a custom infrastructure processing unit (IPU) that offloads storage I/O and networking processing.16 Benchmarks comparing Axion (C4A) against Graviton4 have shown competitive results; while Graviton4 maintains a lead in specific database query latencies, Axion has demonstrated 10-45% improvements in specific integer and floating-point benchmarks due to Google's aggressive system-level optimizations.17
Google Axion（配备 Titanium 的挑战者）：
谷歌凭借同样基于 ARM Neoverse V2 核心构建的 Axion 较晚进入定制 CPU 领域。谷歌声称，Axion 的性能比同类 x86 实例高出 50%，比通用 ARM 实例高出 30% 16。与 AWS Nitro 类似，谷歌采用了 Titanium 系统，这是一种定制的基础设施处理单元（IPU），负责卸载存储 I/O 和网络处理 16。将 Axion (C4A) 与 Graviton4 进行比较的基准测试显示了具有竞争力的结果；虽然 Graviton4 在特定的数据库查询延迟方面保持领先，但由于谷歌激进的系统级优化，Axion 在特定的整数和浮点基准测试中表现出 10-45% 的提升 17。
Azure Cobalt 100 (The Integrated Specialist):
Microsoft's Cobalt 100 utilizes the ARM Neoverse N2 core (unlike the V2 in AWS/GCP), prioritizing thread density and power efficiency over raw per-core peak performance.18 This 128-core chip is optimized for Microsoft's internal workloads, specifically Azure SQL and Microsoft Teams. Microsoft claims a 40% performance improvement over previous ARM generations on Azure. The strategy here is vertical integration: optimizing the silicon specifically for the.NET and SQL Server software stacks that dominate the Azure customer base.19
Azure Cobalt 100（集成专家）：
微软的 Cobalt 100 利用 ARM Neoverse N2 核心（不同于 AWS/GCP 中的 V2），优先考虑线程密度和能效，而不是原始的单核峰值性能 18。这款 128 核芯片针对微软的内部工作负载进行了优化，特别是 Azure SQL 和 Microsoft Teams。微软声称，与 Azure 上的上一代 ARM 相比，性能提高了 40%。这里的策略是垂直整合：专门针对主导 Azure 客户群的.NET 和 SQL Server 软件栈优化芯片 19。
3.2 Comparative Compute Benchmarks (2024/2025 Findings)
3.2 比较计算基准测试（2024/2025 发现）
Recent rigorous benchmarking published in IEEE and arXiv provides a granular view of performance differences. In High-Performance Computing (HPC) scenarios involving OpenMP workloads, distinct hierarchies emerge:
IEEE 和 arXiv 最近发布的严格基准测试提供了性能差异的精细视图。在涉及 OpenMP 工作负载的高性能计算 (HPC) 场景中，出现了明显的层级：
Runtime Efficiency: AWS consistently delivers the shortest runtimes across all processor architectures (Intel, AMD, ARM). In direct comparisons, AWS's ARM-based instances (Graviton) were found to be approximately 33% faster than AWS's own AMD instances and nearly 50% faster than Intel instances for specific computational kernels.20
Architectural Maturity Gap: The benchmarks highlighted significant lag in GCP's older ARM generation (Tau T2A), which exhibited runtimes nearly 3x slower than AWS Graviton in specific legacy tests. This validates Google's urgent push to deploy Axion to close the performance gap.20
Cost Implications: While AWS generally offers superior raw performance, its on-demand pricing often carries a premium. However, when adjusted for runtime efficiency (price/performance ratio), the speed advantage of Graviton often results in a lower total cost for compute-bound tasks compared to cheaper but slower instances on GCP or Azure.20
运行时效率： AWS 在所有处理器架构（Intel、AMD、ARM）中始终提供最短的运行时间。在直接比较中，发现 AWS 的基于 ARM 的实例（Graviton）在特定计算内核上比 AWS 自己的 AMD 实例快约 33%，比 Intel 实例快近 50% 20。
架构成熟度差距： 基准测试突显了 GCP 较旧 ARM 代次 (Tau T2A) 的显著滞后，在特定的遗留测试中，其运行时间比 AWS Graviton 慢近 3 倍。这验证了谷歌紧急部署 Axion 以缩小性能差距的必要性 20。
成本影响： 虽然 AWS 通常提供卓越的原始性能，但其按需定价通常带有溢价。然而，当针对运行时效率（性价比）进行调整时，与 GCP 或 Azure 上更便宜但更慢的实例相比，Graviton 的速度优势通常会导致计算密集型任务的总成本更低 20。
4. The AI Infrastructure Stack: Supercomputers and Custom ASICs
4. AI 基础设施栈：超级计算机与定制 ASIC
The generative AI explosion has bifurcated cloud infrastructure into two parallel tracks: massive deployments of NVIDIA GPUs (H100/Blackwell) for immediate compatibility, and proprietary AI accelerators (ASICs) for long-term cost control and supply chain security.
生成式 AI 的爆发将云基础设施分化为两条平行轨道：大规模部署 NVIDIA GPU (H100/Blackwell) 以实现即时兼容性，以及开发专有 AI 加速器 (ASIC) 以实现长期成本控制和供应链安全。
4.1 Proprietary Accelerators: The War on Cost
4.1 专有加速器：成本之战
Providers are developing custom chips to break the NVIDIA monopoly, focusing on specific stages of the AI lifecycle.
提供商正在开发定制芯片以打破 NVIDIA 的垄断，专注于 AI 生命周期的特定阶段。
Google Cloud TPU (The Mature AI Factory): Google possesses the most mature custom silicon stack with the Tensor Processing Unit (TPU). The TPU v5p serves as the high-performance training workhorse, featuring huge High Bandwidth Memory (HBM) capacity (3x that of v4) and an inter-chip interconnect (ICI) with a 3D torus topology delivering 4,800 Gbps per chip.21 This allows thousands of TPUs to act as a single "Pod," ideal for training massive LLMs. The TPU v5e is the cost-optimized variant for inference and smaller training jobs, delivering 2x better performance-per-dollar than predecessors.21
AWS Trainium & Inferentia (The Decoupled Approach): AWS splits the workload. Trainium2 targets training, offering 650 TFLOPS and 96 GB of HBM2e memory, explicitly designed to undercut Nvidia's training costs by up to 50%.22 Inferentia2 focuses solely on inference, optimizing for low latency and high throughput for deploying models like Llama 3 or Claude. The challenge for AWS is software; developers must use the Neuron SDK to translate PyTorch/TensorFlow code to run on these chips, creating a migration friction not present with NVIDIA GPUs.23
Microsoft Azure Maia 100 (The Generative Specialist): Azure's Maia 100 is the newest entrant, specifically purpose-built for large-scale generative AI (like OpenAI's GPT models). It introduces hardware support for MXFP4 (sub-8-bit data types), significantly increasing throughput for transformer models by reducing precision requirements where acceptable.24 Unlike general-purpose accelerators, Maia requires custom liquid-cooled server infrastructure ("Sidekicks") due to its extreme power density, reflecting Microsoft's willingness to redesign the data center entirely for AI.3
谷歌云 TPU（成熟的 AI 工厂）： 谷歌凭借 张量处理单元 (TPU) 拥有最成熟的定制芯片栈。TPU v5p 作为高性能训练主力，具有巨大的高带宽内存 (HBM) 容量（v4 的 3 倍）和采用 3D 环面拓扑的芯片间互连 (ICI)，每芯片提供 4,800 Gbps 21。这允许数千个 TPU 作为一个“Pod”运行，非常适合训练大规模 LLM。TPU v5e 是用于推理和较小训练任务的成本优化变体，性价比是前代产品的 2 倍 21。
AWS Trainium & Inferentia（解耦方法）： AWS 分割了工作负载。Trainium2 针对训练，提供 650 TFLOPS 和 96 GB 的 HBM2e 内存，明确旨在将 Nvidia 的训练成本降低高达 50% 22。Inferentia2 仅专注于推理，针对部署 Llama 3 或 Claude 等模型优化低延迟和高吞吐量。AWS 面临的挑战是软件；开发人员必须使用 Neuron SDK 转换 PyTorch/TensorFlow 代码才能在这些芯片上运行，这产生了 NVIDIA GPU 所不存在的迁移摩擦 23。
微软 Azure Maia 100（生成式专家）： Azure 的 Maia 100 是最新的进入者，专门为大规模生成式 AI（如 OpenAI 的 GPT 模型）构建。它引入了对 MXFP4（8 位以下数据类型）的硬件支持，通过在可接受的情况下降低精度要求，显著提高了 Transformer 模型的吞吐量 24。与通用加速器不同，Maia 由于其极高的功率密度，需要定制的液冷服务器基础设施（"Sidekicks"），这反映了微软完全为 AI 重新设计数据中心的意愿 3。
4.2 NVIDIA Integration and Networking Topologies
4.2 NVIDIA 集成与网络拓扑
When deploying standard NVIDIA H100/H200 clusters, the differentiation lies entirely in the network architecture, which determines the efficiency of distributed training.
在部署标准 NVIDIA H100/H200 集群时，差异完全在于 网络架构，它决定了分布式训练的效率。
Azure (InfiniBand - The Supercomputer): Azure stands out by implementing NVIDIA Quantum-2 InfiniBand for its ND H100 v5 instances.26 This offers 3.2 Tbps of bandwidth with extremely low latency, effectively replicating a dedicated research supercomputer environment inside the cloud. This architecture is preferred for tightly coupled, massive-scale training jobs that require absolute minimal latency between GPU nodes.26
AWS (EFA - The Ethernet Evolution): AWS rejects InfiniBand in favor of its proprietary Elastic Fabric Adapter (EFA) Gen 2. Using standard Ethernet cabling but bypassing the OS kernel, EFA achieves the same 3,200 Gbps bandwidth on P5 instances.28 Crucially, EFA uses the Scalable Reliable Datagram (SRD) protocol, which sprays packets across multiple network paths to avoid congestion, offering higher resilience in multi-tenant environments compared to standard TCP.29
Google Cloud (Titanium & Jupiter): For its A3 Ultra VMs, Google utilizes its Titanium offload processors and a 4-way rail-aligned network to also hit 3.2 Tbps.30 Google's strategy integrates these GPU clusters into its massive Jupiter data center fabric, emphasizing flexibility and rapid reconfiguration via Google Kubernetes Engine (GKE), treating GPUs as composable resources rather than static clusters.30
Azure（InfiniBand - 超级计算机）： Azure 通过为其 ND H100 v5 实例实施 NVIDIA Quantum-2 InfiniBand 脱颖而出 26。这提供了 3.2 Tbps 的带宽和极低的延迟，有效地在云端复制了专用的科研超级计算机环境。这种架构是紧密耦合的大规模训练任务的首选，这些任务需要 GPU 节点之间的绝对最小延迟 26。
AWS（EFA - 以太网进化）： AWS 拒绝使用 InfiniBand，转而支持其专有的 第二代弹性结构适配器 (EFA)。EFA 使用标准以太网布线但绕过操作系统内核，在 P5 实例 上实现了相同的 3,200 Gbps 带宽 28。至关重要的是，EFA 使用 可扩展可靠数据报 (SRD) 协议，该协议通过多条网络路径分发数据包以避免拥塞，与标准 TCP 相比，在多租户环境中提供了更高的弹性 29。
谷歌云（Titanium & Jupiter）： 对于其 A3 Ultra 虚拟机，谷歌利用其 Titanium 卸载处理器和 4 路轨道对齐网络，同样达到了 3.2 Tbps 30。谷歌的策略将这些 GPU 集群集成到其庞大的 Jupiter 数据中心结构中，强调通过 Google Kubernetes Engine (GKE) 实现灵活性和快速重新配置，将 GPU 视为可组合资源而非静态集群 30。
5. Storage Innovations and Data Gravity
5. 存储创新与数据重力
As compute speed increases, storage I/O often becomes the bottleneck. The providers have introduced disaggregated storage architectures to solve this.
随着计算速度的提高，存储 I/O 往往成为瓶颈。提供商已推出解耦存储架构来解决这一问题。
Azure Premium SSD v2 & Ultra Disk: Azure has moved to a highly granular model where IOPS, throughput, and capacity can be provisioned independently. Premium SSD v2 allows administrators to dynamically adjust performance without downtime, a critical feature for mission-critical databases (SQL Server, Oracle) that experience "bursty" traffic patterns.31
Google Hyperdisk: Leveraging the Titanium offload system, Google's Hyperdisk architecture decouples storage processing from the host VM. This allows for massive throughput (up to 500k IOPS) that does not degrade the compute instance's performance, as the storage logic is handled by the Titanium card. This is particularly advantageous for data analytics workloads (BigQuery, Hadoop) that require massive I/O bandwidth.30
AWS io2 Block Express: AWS positions io2 Block Express as the first "SAN in the Cloud," offering sub-millisecond latency and durability of 99.999%. It is designed to replace on-premise Storage Area Networks, supporting the highest performance tier for IOPS-intensive applications.33
Azure Premium SSD v2 & Ultra Disk: Azure 已转向高度精细的模型，其中 IOPS、吞吐量和容量可以独立配置。Premium SSD v2 允许管理员在不停机的情况下动态调整性能，这对于经历“突发”流量模式的关键任务数据库（SQL Server, Oracle）来说是一项至关重要的功能 31。
Google Hyperdisk: 利用 Titanium 卸载系统，谷歌的 Hyperdisk 架构将存储处理与主机虚拟机解耦。这允许实现巨大的吞吐量（高达 500k IOPS），且不会降低计算实例的性能，因为存储逻辑由 Titanium 卡处理。这对于需要巨大 I/O 带宽的数据分析工作负载（BigQuery, Hadoop）特别有利 30。
AWS io2 Block Express: AWS 将 io2 Block Express 定位为首个“云端 SAN”，提供亚毫秒级延迟和 99.999% 的持久性。它旨在取代本地存储区域网络，支持 IOPS 密集型应用程序的最高性能层级 33。
6. Quantum Computing: The Physics of the Future
6. 量子计算：未来的物理学
The "Big Three" are diverging radically in their path to post-classical computing.
“三巨头”在通往后经典计算的道路上正出现根本性的分歧。
6.1 Microsoft: The High-Stakes Topological Gamble
6.1 微软：高风险的拓扑赌注
Microsoft has placed a singular, massive bet on Topological Qubits (Majorana zero modes). This theoretical approach promises a "hardware-protected" qubit that is inherently immune to environmental noise, theoretically reducing the massive overhead required for error correction in other approaches. In 2025, Microsoft unveiled Majorana 1, a processor powered by a topological core, with a roadmap to scale to one million qubits on a single chip.34 However, this approach remains scientifically controversial; while Microsoft claims to have observed the necessary physics, independent verification of robust topological protection remains a subject of intense scientific scrutiny.36 If successful, it leaps past competitors; if it fails, Azure lacks a backup hardware plan.
微软在 拓扑量子比特（马约拉纳零模）上押下了唯一的、巨大的赌注。这种理论方法承诺一种“硬件保护”的量子比特，它在本质上对环境噪声具有免疫力，理论上减少了其他方法中纠错所需的巨大开销。2025 年，微软推出了 Majorana 1，这是一款由拓扑核心驱动的处理器，拥有在单个芯片上扩展到一百万个量子比特的路线图 34。然而，这种方法在科学上仍存在争议；尽管微软声称已观察到必要的物理现象，但对稳健拓扑保护的独立验证仍然是科学界严格审查的主题 36。如果成功，它将超越竞争对手；如果失败，Azure 将缺乏备用硬件计划。
6.2 Google: The Superconducting Engineer
6.2 谷歌：超导工程师
Google Quantum AI is pursuing the more established Superconducting Qubit route. Their roadmap is clear and milestone-driven: achieve a useful, error-corrected quantum computer by 2029.37 Google's strategy focuses on brute-force engineering: scaling physical qubits (targeting 1,000,000) to create reliable logical qubits. Recent breakthroughs with the Willow chip demonstrated that increasing the physical qubit count actually reduced error rates—a critical proof-of-concept for fault tolerance.38 This is a "lower risk" physics path compared to Microsoft but requires solving immense engineering challenges in wiring and refrigeration.
谷歌量子 AI 正在追求更成熟的 超导量子比特 路线。他们的路线图清晰且以里程碑为驱动：在 2029 年之前实现有用的、经过纠错的量子计算机 37。谷歌的策略侧重于强力工程：扩展物理量子比特（目标是 1,000,000 个）以创建可靠的逻辑量子比特。Willow 芯片的最新突破表明，增加物理量子比特数量实际上降低了错误率——这是容错的关键概念验证 38。与微软相比，这是一条“低风险”的物理路径，但需要解决布线和制冷方面的巨大工程挑战。
6.3 AWS: The Platform Agnostic
6.3 AWS：平台不可知论者
AWS adopts a service-broker model via Amazon Braket. Rather than betting on one horse, AWS allows customers to test hardware from multiple vendors (IonQ, Rigetti, QuEra, D-Wave) through a single interface.39 While AWS is developing its own "Ocelot" chip focused on efficient error correction, its primary value proposition is enabling customers to be "quantum ready" today without locking into a specific hardware modality that might become obsolete.37
AWS 通过 Amazon Braket 采用服务代理模式。AWS 不押注于单一选项，而是允许客户通过单一界面测试来自多个供应商（IonQ, Rigetti, QuEra, D-Wave）的硬件 39。虽然 AWS 正在开发自己专注于高效纠错的“Ocelot”芯片，但其主要价值主张是使客户今天就能“量子就绪”，而无需锁定于可能过时的特定硬件模式 37。
7. Economic Models: Pricing, Discounts, and Sustainability
7. 经济模型：定价、折扣与可持续发展
7.1 Discount Mechanics
7.1 折扣机制
AWS Savings Plans: The gold standard for flexibility. Customers commit to a specific dollar-per-hour spend (e.g., $50/hr) for 1 or 3 years. This applies to any usage (Lambda, Fargate, EC2) regardless of region or instance family. Discounts reach up to 72%.40
Azure Savings Plans: Similar to AWS, offering up to 65% savings. Azure's unique strength is the Azure Hybrid Benefit, allowing customers to bring existing on-premise Windows Server and SQL Server licenses to the cloud, potentially saving an additional 40% on top of compute discounts—a massive incentive for legacy enterprises.40
Google Committed Use Discounts (CUDs): Historically less flexible (often tied to specific regions), offering up to 70% off. However, Google offers a unique benefit: Sustained Use Discounts (SUDs). These apply automatically to older instance families if they run for a significant portion of the month, requiring no upfront commitment. This is ideal for unpredictable workloads that end up running longer than expected.42
AWS Savings Plans: 灵活性的黄金标准。客户承诺在 1 年或 3 年内特定的每小时支出（例如 $50/小时）。这适用于 任何 使用（Lambda, Fargate, EC2），无论区域或实例系列如何。折扣高达 72% 40。
Azure Savings Plans: 与 AWS 类似，提供高达 65% 的节省。Azure 的独特优势在于 Azure 混合权益 (Azure Hybrid Benefit)，允许客户将现有的本地 Windows Server 和 SQL Server 许可证带到云端，除了计算折扣外，可能还能额外节省 40%——这对传统企业来说是一个巨大的激励 40。
Google 承诺使用折扣 (CUDs): 历史上灵活性较低（通常绑定特定区域），提供高达 70% 的折扣。然而，谷歌提供了一个独特的好处：持续使用折扣 (SUDs)。如果较旧的实例系列在一个月内运行了相当长的时间，这些折扣会自动应用，无需预先承诺。这对于最终运行时间超出预期的不可预测工作负载非常理想 42。
7.2 Sustainability Targets
7.2 可持续发展目标
Google (24/7 CFE): The most aggressive target. By 2030, Google aims to run on Carbon-Free Energy 24/7. This means matching energy consumption with renewable generation every hour on the local grid where the data center resides, effectively eliminating reliance on fossil fuels even when the sun isn't shining.44
Microsoft (Carbon Negative): Committed to being carbon negative by 2030, removing historical carbon since the company's founding by 2050. They heavily invest in carbon capture technologies alongside renewable procurement.1
AWS (100% Renewable): Aiming for 100% renewable energy by 2025. AWS relies heavily on Power Purchase Agreements (PPAs) to offset annual usage, a strategy that is effective but less operationally complex than Google's hourly matching.44
谷歌 (24/7 CFE): 最激进的目标。到 2030 年，谷歌的目标是 24/7 全天候使用无碳能源运行。这意味着在数据中心所在的 本地电网 上，将能源消耗与可再生能源发电 每小时 进行匹配，有效地消除了对化石燃料的依赖，即使在没有阳光的时候也是如此 44。
微软 (负碳排放): 承诺到 2030 年实现负碳排放，并到 2050 年消除公司成立以来的历史碳排放。除了可再生能源采购外，他们还大力投资碳捕获技术 1。
AWS (100% 可再生能源): 目标是到 2025 年实现 100% 可再生能源。AWS 严重依赖购电协议 (PPA) 来抵消年度使用量，这种策略虽然有效，但在运营复杂性上低于谷歌的每小时匹配 44。
8. Conclusion: Strategic Alignment
8. 结论：战略对齐
The selection of a cloud provider in 2025 is a strategic bet on architectural philosophy.
2025 年选择云提供商是对架构理念的战略押注。
AWS is the optimal choice for "Builders" and technically sophisticated organizations. Its Nitro System and Graviton4 silicon provide the highest margins for those willing to optimize code for ARM. Its EFA networking offers a flexible, scalable path for AI without the rigidity of specialized HPC topologies. It remains the safe, high-performance default for the majority of the market.
AWS 是“构建者”和技术成熟型组织的最佳选择。其 Nitro 系统 和 Graviton4 芯片为那些愿意针对 ARM 优化代码的人提供了最高的利润空间。其 EFA 网络为 AI 提供了一条灵活、可扩展的路径，没有专用 HPC 拓扑的僵化。它仍然是市场大多数份额的安全、高性能默认选择。
Azure is the "Business Integrator." It wins where hybrid compatibility and OpenAI access are paramount. Its Single Instance SLA and Hybrid Benefit make it the most cost-effective home for Windows/SQL workloads, while its InfiniBand-connected AI supercomputers offer the fastest path for organizations needing to train massive models using standard Nvidia hardware with zero compromises on latency.
Azure 是“商业集成商”。它在 混合兼容性 和 OpenAI 访问 至关重要的地方获胜。其单实例 SLA 和混合权益使其成为 Windows/SQL 工作负载最具成本效益的归宿，而其 InfiniBand 连接的 AI 超级计算机为需要使用标准 Nvidia 硬件训练大规模模型且对延迟零妥协的组织提供了最快的路径。
Google Cloud is the "Future Tech Lab." It is the destination for data-intensive organizations. The TPU Pod architecture offers the best theoretical performance-per-watt for AI training, and its Kubernetes (GKE) implementation remains the gold standard for container orchestration. Its ambitious 2029 Quantum Roadmap and 24/7 CFE goals appeal to organizations prioritizing long-term scientific innovation and strict sustainability mandates.
谷歌云 是“未来技术实验室”。它是数据密集型组织的目的地。TPU Pod 架构为 AI 训练提供了最佳的理论每瓦性能，其 Kubernetes (GKE) 实现仍然是容器编排的黄金标准。其雄心勃勃的 2029 量子路线图 和 24/7 CFE 目标吸引了优先考虑长期科学创新和严格可持续发展要求的组织。
Works cited
AWS vs Azure vs Google Cloud: The Ultimate 2025 Comparison Guide - Pilotcore, accessed November 20, 2025,
Cloud Roadmap for SDEs: AWS, Azure, GCP 2025 - Get SDE Ready, accessed November 20, 2025,
The Cloud AI Wars: How Google, AWS, and Azure Stack Up in 2025 | CloudSyntrix, accessed November 20, 2025,
What is Better: AWS, Azure or Google Cloud? 2024 Comparison - UUUSoftware, accessed November 20, 2025,
AWS vs Azure vs Google Cloud (2025) - SotaTek, accessed November 20, 2025,
AWS Regions and Availability Zones, accessed November 20, 2025,
Regions and zones | Compute Engine - Google Cloud Documentation, accessed November 20, 2025,
Azure Disk Storage, accessed November 20, 2025,
Announcing the general availability of Azure shared disks and new Azure Disk Storage enhancements | Microsoft Azure Blog, accessed November 20, 2025,
Compute Engine Service Level Agreement (SLA) - Google Cloud, accessed November 20, 2025,
Compute Engine Service Level Agreement (SLA) - Google Cloud, accessed November 20, 2025,
Amazon Compute Service Level Agreement - AWS, accessed November 20, 2025,
Regions and Zones - Amazon Elastic Compute Cloud, accessed November 20, 2025,
Why hyperscalers and the industry are making the switch - The Register, accessed November 20, 2025,
Google Axion CPU With GCE C4A vs. AWS Graviton4 Performance Review - Phoronix, accessed November 20, 2025,
Google Axion processors, accessed November 20, 2025,
ARM Wrestling: Benchmarking the Latest Cloud ARM CPUs | by Muhammad - DoiT, accessed November 20, 2025,
Exploring AI CPU-Inferencing with Azure Cobalt 100 - Thomas Van Laere, accessed November 20, 2025,
Arm's Influence Rising at Microsoft - Liftr Insights, accessed November 20, 2025,
Evaluating HPC-Style CPU Performance and Cost in ... - arXiv, accessed November 20, 2025,
Introducing Cloud TPU v5p and AI Hypercomputer | Google Cloud Blog, accessed November 20, 2025,
Cloud AI Platforms Comparison: AWS Trainium vs Google TPU v5e vs Azure ND H100, accessed November 20, 2025,
AWS Trainium vs Google TPU: Performance per Dollar Analysis - Sparkco, accessed November 20, 2025,
Tech titans lock horns in the AI chip revolution - IO, accessed November 20, 2025,
AI Accelerator Comparison Tables - Spill / Fill, accessed November 20, 2025,
ND-H100-v5 size series - Azure Virtual Machines | Microsoft Learn, accessed November 20, 2025,
ND family virtual machine size series - Azure - Microsoft Learn, accessed November 20, 2025,
Amazon EC2 P5 Instances - AWS, accessed November 20, 2025,
AWS and NVIDIA Collaborate on Next-Generation Infrastructure for Training Large Machine Learning Models and Building Generative AI Applications, accessed November 20, 2025,
A3 Ultra with NVIDIA H200 GPUs are GA on AI Hypercomputer | Google Cloud Blog, accessed November 20, 2025,
Azure managed disk types - Virtual Machines - Microsoft Learn, accessed November 20, 2025,
General-purpose machine family for Compute Engine - Google Cloud Documentation, accessed November 20, 2025,
AWS vs. Azure vs. Google Cloud: A Complete Comparison - DataCamp, accessed November 20, 2025,
Quantum Roadmap, accessed November 20, 2025,
Microsoft unveils Majorana 1, the world's first quantum processor powered by topological qubits, accessed November 20, 2025,
Experts weigh in on Microsoft's topological qubit claim - Physics World, accessed November 20, 2025,
Quantum Computing Roadmaps & Predictions of Leading Players, accessed November 20, 2025,
Our Quantum Echoes algorithm is a big step toward real-world applications for quantum computing - Google Blog, accessed November 20, 2025,
Quantum Computing Companies in 2025 (76 Major Players), accessed November 20, 2025,
GCP vs AWS vs Azure: A Comparison of Savings Plans and Reserved Instances - Sedai.io, accessed November 20, 2025,
AWS vs. Azure vs. Google Cloud: Comparing Discounts, Commitments, and Reservations, accessed November 20, 2025,
AWS vs Azure vs Google Cloud: comprehensive comparison for 2025 | Blog - Northflank, accessed November 20, 2025,
AWS vs. Azure vs. Google: Compute Pricing Comparison | News - Essential Designs, accessed November 20, 2025,
The Tech Industry's Transition to 24/7 Carbon-Free Energy | Policy Interns, accessed November 20, 2025,
Amazon, Google, and Microsoft's Race Toward a Green Cloud - CTO Magazine, accessed November 20, 2025,
Cloud carbon footprint: Do Amazon, Microsoft and Google have their head in the clouds?, accessed November 20, 2025,
Clouding the issue: are Amazon, Google, and Microsoft really helping companies go green? | Insights & Sustainability | Climatiq, accessed November 20, 2025,