In mid-August 2026, Google I/O Connect China was held at the Shanghai World Expo Center, drawing nearly 2,000 developers. It was one of the highest-profile and largest offline events Google has hosted in China in recent years. At the conference, Stanley Chen, President of Google Greater China and Korea, said AI is evolving from “a frontier concept that inspires imagination” into a working partner with proactive collaboration and action capabilities. Google announced the official launch of its “Google Startup Global Expansion Program,” integrating Google’s ecosystem of overseas expansion solutions and platform resources, and establishing new cross-border e-commerce acceleration centers in Guangzhou and Hangzhou.

The conference sent a clear signal: overseas cloud providers are accelerating their push into China’s outbound enterprise market. But Google Cloud has no data centers in mainland China; its positioning is “providing compute power for Chinese enterprises going global” — not competing with Chinese cloud providers on their home turf.

Google is not alone. In June 2026, DigitalOcean officially integrated Alipay; AWS and Azure have long operated China regions through local partners. Competition in the overseas cloud server market is extending from the global arena to the China outbound battlefield.

According to Synergy Research Group, global cloud service provider revenue reached $129 billion in Q1 2026, up 35% year-over-year. Gartner forecasts global public cloud end-user spending will hit $850 billion in 2026. The more critical structural shift: AI infrastructure now accounts for 19% of total cloud spending, up from just 8% in 2023 — more than doubling in three years.

But growth in this market is uneven. The traditional big three — AWS, Azure, and GCP — together hold roughly 63% of global market share, yet significant internal shifts are underway: AWS has slipped from 30% to 28%, while GCP has climbed from 13% to 15%, leading the pack with 82% growth. Oracle OCI is declaring the rise of a “fourth pole” with 92% IaaS revenue growth, while DigitalOcean is emerging as a standout in AI inference and the SMB developer market.

For Chinese enterprises going global, overseas cloud server selection has evolved from a “technology multiple-choice question” into a “strategic must-answer question” — it affects not only IT infrastructure stability and cost, but directly impacts the pace and competitiveness of global business expansion.

Six-Force Evaluation Model and Overall Rankings

Haibi Research Institute applied its “Six-Force Evaluation Model” with customized weight adjustments for the overseas cloud server sector, systematically assessing six providers: AWS, Azure, GCP, Oracle OCI, IBM Cloud, and DigitalOcean. Evaluation dimensions include brand capability (10% weight), product capability (22%), technology capability (26%), service capability (12%), security capability (15%), and value capability (15%).

The logic behind the weight adjustments: the overseas cloud server market is highly mature, and brand gaps are no longer a core variable; technology capability is the decisive factor in the AI era, hence the highest weight; product capability and cost-effectiveness are core decision factors for outbound enterprise selection. All scoring is based on publicly verifiable third-party reports, vendor financial disclosures, or official announcements.

Composite scores are as follows:

RankProviderComposite ScoreBrandProductTechnologyServiceSecurityValue1AWS4.754.84.94.94.54.84.52Azure4.564.54.74.54.54.84.33GCP4.174.24.34.33.84.24.04OCI3.863.53.84.03.54.04.25IBM Cloud3.473.23.53.24.04.03.26DigitalOcean3.213.03.33.03.23.03.8

Note: Scoring is on a 5-point scale; composite scores are weighted by dimension and rounded to two decimal places.

AWS ranked first with 4.75, Azure followed closely at 4.56, and GCP placed third at 4.17. OCI, IBM Cloud, and DigitalOcean ranked fourth through sixth with scores of 3.86, 3.47, and 3.21 respectively.

Core Capability Analysis of the Six Providers

AWS: Launched EC2 in 2006, AWS is the pioneer and long-time leader of the global public cloud market. In Q2 2026, it held 28% market share with annualized revenue of $169 billion. Its core competitive edge lies in the industry’s most complete product portfolio — 33 regions, 105 availability zones, over 300 instance types, the Graviton/Trainium in-house chip ecosystem, and 100,000-card-class cluster scheduling capability. The fifth-generation Graviton5 chip features 192 cores; M9g instances deliver 25% better performance than Graviton4 and 35% better machine learning performance. 98% of the top 1,000 EC2 customers use Graviton, and Trainium AI accelerator annualized revenue exceeds $25 billion. In China, AWS operates Beijing and Ningxia regions through Sinnet and NWCD.

Azure: Commercially launched in 2010, Azure is deeply integrated with Microsoft’s enterprise ecosystem including Windows Server, SQL Server, and Active Directory. Over 95% of Fortune 500 companies are its customers. In Q2 2026, it held 20% market share with annualized revenue surpassing $100 billion for the first time. Its core competitive edge lies in deep enterprise market penetration, the Cobalt in-house ARM chip, and coverage of over 60 global regions. In China, it is operated by 21Vianet.

GCP: Launched App Engine in 2008 and Compute Engine in 2012. Long viewed as the “perennial third,” GCP’s market share climbed to 15% in Q2 2026, with 82% revenue growth — the fastest among the big three. Its core competitive edge lies in AI/data analytics technology accumulation — TPU v7 has been released, Vertex AI offers over 200 pre-loaded models, and its global backbone network provides low-latency advantages. It has no data centers in mainland China but positions itself as an infrastructure provider for Chinese enterprises going global.

Oracle OCI: Formally entered the public cloud market in 2016, making it the latest entrant among the six. But leveraging Oracle’s massive database installed base and an explosion in AI compute orders, it ranked fourth globally for the first time in Q1 2026. Its core competitive edge lies in deep integration with Oracle databases, a multi-cloud interconnection strategy, and aggressive pricing. Its four-year revenue guidance has been raised from $18 billion to $144 billion. In Q3 2026, it will deploy 50,000 AMD Instinct MI450 GPUs to build an AI supercluster.

IBM Cloud: Its history traces back to the 2013 acquisition of SoftLayer. Its core competitive edge lies in enterprise hybrid cloud positioning, the unique value of IBM Power virtual servers (particularly in SAP migration scenarios), and IBM Consulting’s global professional services team. However, IBM’s China investment company ceased operations in 2025, with cloud services maintained through partners. Public cloud IaaS share is approximately 2% to 3%, with sluggish growth.

DigitalOcean: Founded in 2012, DigitalOcean has won over developers worldwide with its simple, easy-to-use Droplets. It listed on the NYSE in 2021. Its core competitive edge lies in extreme developer experience, community documentation ecosystem, and high cost-effectiveness. Q2 2026 revenue was $281 million, up 29% year-over-year. In June 2026, it officially integrated Alipay; AI customer ARR reached $234 million, up 212% year-over-year.

Scenario-Based Selection Recommendations

The report emphasizes that rankings are an illusion; fit is the truth. Which cloud provider to choose ultimately depends on “who you are, where your business operates, and what your goals are.”

For large-model AI training scenarios, AWS is the first choice — the maturity of the Graviton+Trainium ecosystem, 100,000-card-class single-cluster scheduling capability, and 98% adoption among top customers all point to AWS’s dominance in this scenario. GCP (unique TPU technology advantages) or OCI (explosive AI compute orders, aggressive pricing) are secondary options. DigitalOcean should be avoided outright in this scenario — its GPU Droplets target inference rather than training, and cluster scale cannot support large-model training needs.

For AI inference or budget-constrained startup teams, DigitalOcean is the first choice — AI customer ARR growing 212%, GPU Droplets using AMD MI350X, pricing far below the big three, and with Alipay integration, Chinese developers can purchase directly. AWS reserved instances or Spot instances are secondary options.

For enterprises deeply invested in the Microsoft technology stack, Azure is the undisputed first choice — Hybrid Benefit can significantly reduce migration costs for existing Microsoft licenses. For enterprises deep in the Google ecosystem, GCP is the first choice — seamless integration with BigQuery and Vertex AI is the core value.

For heavily regulated industries such as finance and healthcare, AWS or Azure are the first choices — AWS holds 143 compliance certifications, while Azure holds over 20 certifications and its Confidential Computing technology is distinctive. DigitalOcean should be avoided outright in this scenario.

For deep Oracle database users, OCI is the only correct choice — the performance and integration depth of Oracle databases on OCI are unmatched by other cloud providers.

Report Limitations and Trend Outlook

The report candidly identifies several limitations: data is current as of Q2 2026, and the cloud computing market is changing extremely rapidly; the evaluation is based primarily on public data and industry reports, without first-hand testing and verification of each provider’s actual deployments; and due to US-China technology tensions, some overseas cloud providers’ China-region businesses face policy and supply chain uncertainties.

On the trend front, the report identifies five directions: AI compute has become the primary engine of cloud growth — according to TrendForce, capital expenditure by the world’s nine largest cloud service providers is expected to reach $830 billion in 2026, with the annual growth rate revised upward from 61% to 79%; in-house chip development has become a core differentiation weapon; multi-cloud/hybrid cloud has shifted from “optional” to “mandatory”; Neocloud is rising, with specialized AI cloud providers like CoreWeave taking AI inference workloads from the giants; and China’s outbound enterprise market has become a must-win battleground for overseas cloud providers.

Meanwhile, the capital expansion of overseas cloud providers is drawing deeper financial scrutiny. According to Silicon Valley 101, the total debt of America’s five major data center operators — Amazon, Microsoft, Google, Meta, and Oracle — has reached $1.65 trillion. These companies are using special purpose entities, finance leases, credit guarantees, operating leases, and other instruments to move massive debt “off” their balance sheets. The Bank for International Settlements has called this practice “shadow lending.”

Take Meta’s Hyperion data center project as an example: the project borrowed a total of $27.3 billion, but Meta’s balance sheet records only a $2.37 billion investment. The remaining approximately $25 billion in debt is held through a special purpose entity structure in which a Blue Owl-affiliated fund owns 80% and Meta owns 20%, legally isolating the debt from Meta’s balance sheet. However, Meta has simultaneously provided approximately $28 billion in residual value guarantees, making it effectively the ultimate credit backstop for this debt.

Nvidia is also driving the financialization of GPUs. On August 10, 2026, Nvidia CEO Jensen Huang published a lengthy post on X announcing a partnership with six institutions — Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR — to establish a dedicated AI compute financing platform, aiming to leverage over $500 billion in third-party capital. Huang wrote: “In the age of AI, compute is revenue.”

These financial innovations are reshaping the capital structure of technology companies. Over the past two decades, the market has grown accustomed to viewing Silicon Valley tech companies as asset-light businesses — write code, sell software, generate cash flow, then use cash to buy back stock. But AI is inverting this logic: today’s most cutting-edge technology competition increasingly resembles the real estate industry — scramble for chips, secure power, buy land, build data centers, and use the next ten to twenty years of cash flow to pay for today’s construction.