汽車雲端服務平台（2026）

2026年，車聯網（IoV）產業每天將產生Petabyte的資料，車輛後端系統每天將自動與雲端伺服器通訊10到100次。隨著VLA模型和駕駛座代理的迭代周期進一步縮短，對雲端運算的穩定性、低延遲和儲存效率的需求將會增加，推動雲端基礎設施的轉型從「規模主導」轉向「價值主導」。

對於雲端服務供應商而言，競爭的重點已從「硬體互補性」轉向「提升服務品質」。演算法最佳化、雲端原生人工智慧、協同調度和安全合規性正成為關鍵的競爭優勢。

對於OEM廠商而言，多重雲端策略使其能夠合理利用各種雲端供應商的生態系統和技術優勢，實現「降低成本和提高效率」，確保即時雲端服務的穩定性，加速自動駕駛、智慧駕駛座和出行服務等核心業務的發展，並建立差異化的競爭優勢。

雲端服務供應商的基礎設施重點轉向「提高品質和效率」。

2024年，汽車雲端服務供應商面臨「晶片短缺和算力短缺」的雙重困境。為了滿足大規模人工智慧模型和自動駕駛輔助系統（NOA）整合到車輛中所帶來的運算能力激增需求，雲端服務供應商增加了硬體投資，增加了伺服器和GPU的數量。一些服務提供者甚至開始自主研發晶片。

到2026年，隨著通用晶片產能緊張狀況的逐步緩解，以及雲端運算算力利用效率的演算法最佳化不斷進步（虛擬化、分段和池化技術日趨成熟），汽車雲端基礎設施將不再盲目追求硬體擴張，而是將重點放在提高下一代汽車雲端服務解決方案的運算能力利用效率、穩定性和適應性上。

以Google雲端和阿里雲等雲端服務供應商為例，它們在2026年的雲端基礎設施解決方案將著重利用新演算法來提高現有雲端基礎設施的效率，並透過應用新的伺服器架構來最佳化雲端叢集的穩定性。

1.Google的新演算法提高了雲端運算叢集的效率。

Google於2026年初發布了名為TurboQuant的演算法。該演算法透過量子壓縮和智慧快取技術，有效降低了儲存需求，並加快了推理處理速度。它針對汽車場景的輕量級運算需求進行了最佳化，解決了「儲存硬體不足限制運算能力利用」的問題，並帶來了以下優勢：

在 KV 快取量化中，實現了近乎無損的精度，同時保持了每個通道 3.5 位元的等效精度，將所需的儲存容量減少到原生 16 位元格式的五分之一以下。

減少記憶體存取可以加快推理速度，並且不會在推理管道中產生額外的開銷。

量化速度比 PQ/RabitQ 快 100,000 到 1,000,000 倍。

根據Google公佈的結果，TurboQuant 曲線在長上下文壓縮方面實現了近乎無損的性能（得分達到 0.997）。

2.阿里雲等中國雲端服務供應商採用超級節點架構來提高其運算叢集的運作效率。

在中國雲端服務供應商中，阿里雲、百度雲和華為雲已於2025年採用超級節點伺服器架構，以最佳化叢集穩定性。這提高了推理效率和叢集穩定性，提升了解決方案的整體性價比。

本報告對中國汽車產業進行了分析，概述了汽車雲端服務的現狀和發展趨勢，並介紹了各公司的解決方案、基礎設施和平台。

目錄

第1章 汽車產業雲端服務的概述與發展趨勢

• 汽車雲端服務產業概覽
• 汽車業對雲端服務的需求
• OEM廠商與雲端服務供應商合作的趨勢
• 雲端原生
• 汽車領域雲端運算技術的發展趨勢

第2章 面向汽車產業的雲端服務解決方案

• 自動駕駛雲
• 物聯網雲
• V2X雲
• 數位化
• 雲端資料閉合迴路
• AI雲
• 雲端資訊安全
• SOA 雲

第3章 雲端平台基礎設施

• 汽車雲產業鏈
• 資料中心：分發
• 資料中心：公共雲端資料中心佈局
• 雲端伺服器
• 伺服器晶片：技術藍圖
• 伺服器晶片：晶片供應商
• 雲端服務供應商的專有晶片（1）至（5）

第4章 汽車產業公共雲端平台

• Amazon Web Services（AWS）
• Microsoft Cloud Azure
• Google Cloud Platform（GCP）
• Huawei Automotive Cloud
• Baidu Automotive Cloud
• Alibaba Automotive Cloud
• Tencent Automotive Cloud
• ByteDance Automotive Cloud

第5章 OEM雲端平台佈局

• Geely
• Xpeng
• Li Auto
• NIO
• FAW
• Changan
• Great Wall Motor
• SAIC
• GAC
• Dongfeng Motor
• BAIC
• BMW
• Mercedes-Benz
• Stellantis
• GM

Research on automotive cloud service platform: with architecture upgrade and computing power improvement, cloud services enter a new stage

In 2026, the Internet of Vehicles industry generates petabytes of data in a single day, and the vehicle backend system communicates automatically with the cloud server ten to hundreds of times a day. As the iteration cycle of VLA models and cockpit agents is further shortened, higher requirements are placed on the stability, low latency, and storage efficiency of cloud computing power, promoting the transformation of cloud infrastructure from "scale-driven" to "value-driven".

For cloud providers, the focus of competition has shifted from "complementing hardware" to "improving service quality". Algorithm optimization, cloud-native AI, collaborative scheduling, and security compliance have become competitive edges;

For OEMs, through a multi-cloud strategy and rational use of the ecosystem and technical advantages of different cloud providers, they can achieve "cost reduction and efficiency improvement", ensure the stability of real-time cloud services, and accelerate the implementation of core businesses such as autonomous driving, intelligent cockpits, and mobility services, building differentiated competitive edges.

The focus of cloud providers' infrastructure shifts to "improving quality and efficiency".

In 2024, automotive cloud providers found themselves trapped in a dilemma of "chip shortages and insufficient computing power." Cloud providers ramped up their hardware investments to stack servers and GPUs to meet the surging demand for computing power driven by the integration of AI large models and NOA (Navigate on Autopilot) into vehicles. Some providers also began to develop chips in-house.

In 2026, as the tight production capacity of general-purpose chips gradually eases and algorithms continue to optimize utilization efficiency of cloud computing power (virtualization, segmentation, and pooling technologies become more mature), automotive cloud infrastructure will no longer blindly pursue the expansion of hardware, but will center on improving utilization efficiency, stability, and adaptability of computing power as the focus of developing next-generation automotive cloud service solutions.

Taking cloud providers such as Google Cloud and Alibaba Cloud as examples, their cloud infrastructure solutions in 2026 focus on improving the efficiency of existing cloud infrastructure with new algorithms and applying new server architectures to optimize the stability of cloud clusters.

1.Google's new algorithm improves cloud computing cluster efficiency

Google introduced the algorithm TurboQuant in early 2026. With quantitative compression and intelligent caching technology, it effectively lowers storage requirements and speeds up inference. It can adapt to the lightweight computing power requirements of automotive scenarios and solve the problem of "insufficient storage hardware restricting the utilization of computing power". It offers the following benefits:

For KV Cache quantization, 3.5 bits per channel achieves near-lossless precision with equivalent accuracy, reducing the storage required by more than 5x compared to the native 16-bit format.

Reduced memory access enables faster inference, with zero additional overhead in the inference pipeline.

The quantization speed is 100,000 to 1 million times faster than PQ/RabitQ.

According to the results released by Google, the TurboQuant curve achieves nearly lossless performance in long context compression (score reaches 0.997).

2.Chinese cloud providers such as Alibaba Cloud apply super-node architectures to improve the operating efficiency of computing clusters.

Among Chinese cloud providers, Alibaba Cloud, Baidu Cloud, and Huawei Cloud launched super-node server architectures that optimize cluster stability in 2025, optimizing inference efficiency and cluster stability, and improving the cost-effectiveness of the entire solutions:

Alibaba Cloud

Alibaba Cloud released Panjiu AI Infra 2.0 AL128 super node servers at the 2025 APSARA Conference. Through ScaleUp interconnection within the super node, they shorten the completion time of E2E inference tasks and improve foundation model inference experience for users. One of the features of such servers lies in ScaleUp interconnection, a technology that caters to modern GPU design, including:

Native memory semantics: Direct access to the computing core of the GPU is allowed, and it is easy to mount to the SoC bus via the interface. There is no conversion overhead and intrusive design for the computing core.

Ultimate performance: Extremely high bandwidth (the entire chip can reach TB/s) and extremely low latency can be achieved. In addition to the high message efficiency of the protocol, excellent performance under high load is also required.

Minimalist implementation: Chip area and cost are minimized, allowing valuable resources and power consumption to be reserved for the computing power and on-chip memory of GPU.

Highly reliable link: In a very high-density SerDes environment, high availability is ensured through a high-performance physical layer and link-level retransmission and fault isolation mechanisms.

Huawei

Huawei has released the next-generation AI data center architecture - CloudMatrix and the mass production product - CloudMatrix384, which breaks through the traditional CPU-centric hierarchical design and supports direct high-performance communication between all heterogeneous system components (including NPU, CPU, DRAM, SSD, NIC and domain-specific accelerators), realizing the transformation of the resource supply model from the server level to the matrix level.

In August 2025, Changan Tops AD adopted Huawei Cloud's CloudMatrix384 super node solution". Based on the CloudMatrix384 super node and Huawei Cloud's high-bandwidth and large-capacity storage cluster, Changan Automobile has achieved efficient training of its autonomous driving model, and adaptation to various autonomous driving models such as VLA and end-to-end models.

Baidu

Relaying on Kunlunxin, a super node server architecture was released. This solution achieves super single-node performance. Its 32-GPU/64-GPU configuration uses faster in-machine communication to increase inter-GPU interconnection bandwidth by 8 times, single-machine training performance by 10 times, and single-GPU inference performance by 13 times, which can support large-scale VLA training and promotion.

Device-cloud collaboration technology optimizes cockpit and vehicle-road-cloud scenario experience.

From 2025 to 2026, device-cloud collaboration technology serves as one of the technical bases to accelerate the penetration into cockpit and vehicle-road-cloud scenarios. With the complementary model of "cloud computing power empowerment + automotive real-time response", it will solve problems such as unsmooth cockpit interaction and vehicle-road-cloud system effects that are not as good as expected, and optimize user experience.

1.Cockpit scenario

In 2026, the cockpit device-cloud collaborative architecture upgrades capabilities through the combined approach of "cloud foundation model optimization + vehicle lightweight model execution". The cloud undertakes high-load computing and inference tasks, including complex semantic understanding, multi-turn dialogue tracking, massive knowledge base data invocation, and other tasks requiring high computing power. The vehicle is in charge of real-time response, low-latency interaction, and privacy protection. With technologies such as edge node sinking, the end-to-end latency is controlled within 500 milliseconds to meet user needs. Cloud IVI is a typical application of device-cloud collaboration in cockpit scenarios.

For example, the Aion Cloud IVI released by GAC and Huawei in September 2025 uses vehicle-cloud intelligent collaboration to reconstruct the cockpit computing power allocation logic: all computing and rendering tasks are handed over to the cloud, and the local IVI is only responsible for interaction and display. The IVI local computing only consumes 0.02-0.03TFLOPS, which greatly reduces the consumption of automotive computing power. This not only ensures a smooth experience of the new IVI system, but also solves the problem of the old vehicle upgrade: there is no need to replace hardware, and smooth intelligent interaction can be achieved even with mid- to low-end chips.

In addition to saving computing resources, this cloud IVI also takes advantage of cloud resources to:

Complete cloud ecosystem aggregation, open up 20,000+ cloud applications, and support the flow of mobile applications to IVI.

Speed up the OTA frequency; all application and system updates are completed in the cloud, and the latest version can be updated in half a day, allowing cockpit functions to always remain "cutting-edge".

2.Vehicle-road-cloud scenario

In the vehicle-road-cloud scenario, the core value of device-cloud collaboration lies in opening up the data links between vehicles, roadside equipment and cloud platforms, and building a complete collaborative closed loop of "vehicle perception, roadside blind spot coverage, and cloud scheduling".

The cloud is responsible for core tasks such as data fusion, macro traffic flow prediction, and global scheduling optimization. Through multi-dimensional data fusion, intelligent allocation of mobility resources is realized. The cloud control platform adopts a two-level architecture of "edge cloud + zonal cloud" to achieve hierarchical processing and global optimization.

Edge computing nodes serve as vehicle-road connection hubs, ensuring end-to-end latency of <=10 milliseconds and focusing on real-time data processing and local scheduling.

In August 2025, Dongfeng eπ007 realized the technology of optimizing the smart parking function with vehicle-road-cloud collaboration technology. The technical path is "cloud scheduling + parking lot allocation + vehicle execution". This technology can increase the parking space utilization rate by 45% and increase the number of vehicles parked per unit area by 1.8 times. Thanks to parking lot sensors and cloud technology, Dongfeng eπ007 does not require manual operation after running into the parking lot. The parking lot equipment can instantly recognize license plates, compressing the entry time to within 15 seconds.

Table of Contents

1 Overview and Trends of Automotive Cloud Services

• 1.1 Overview of Automotive Cloud Service Industry
• Definition of Automotive Cloud
• China's Automotive Cloud Market Size (1)-(3)
• Automotive Cloud Platform Classification: Comparison of Features
• Automotive Cloud Platform Classification: Three Different Service Types
• Automotive Cloud Platform Classification: Comparison of Typical Cloud Providers in China
• Competitive Landscape of Automotive Cloud Services (1)-(2)
• China's Automotive Cloud Business Models
• 1.2 Automotive Cloud Service Demand
• Automotive Cloud Application Scenarios
• China's Automotive Cloud Service Demand: Three Stages
• China's Automotive Cloud Service Demand: Transformation Characteristics
• China's Automotive Cloud Service Demand: Status Quo
• China's Automotive Cloud Service Demand Sub-scenarios: AI Foundation Models
• China's Automotive Cloud Service Demand Sub-scenarios: Autonomous Driving Cloud Platforms
• China's Automotive Cloud Service Demand Sub-scenarios: Deep Integration of Cloud Platform Tool Chains
• 1.3 Trends in Cooperation between OEMs and Cloud Providers
• Automotive Cloud Business Models
• OEM Cloud Platform Selection Trends (1):
• OEM Cloud Platform Selection Trends (2):
• Summary of Cloud Capabilities Required by OEMs
• 1.4 Cloud Native
• Cloud Native: Concept
• Cloud Native: Main Technologies and Advantages
• Cloud Native: Application Scenarios
• Cloud Native System Technology Base: Vehicle-Cloud Collaboration
• Cloud Native System Technology Base: Data Lake
• Cloud Native: Security Evolution
• Cloud Native Application Cases of Suppliers: (1)-(3)
• Cloud Native Application Cases of OEMs: (1)-(6)
• Cloud Native Application Cases of OEMs: Summary
• 1.5 Automotive Cloud Technology Trends
• AI Cloud Trends (1):
• AI Cloud Trends (2): Cloud Native AI Becomes A New Paradigm
• Edge Cloud Trends:
• Internet of Vehicles Cloud Trends:
• Cloud Computing Trends:

2 Automotive Cloud Service Solutions

• 2.1 Autonomous Driving Cloud
• Autonomous Driving's Demand for Cloud
• Autonomous Driving Cloud Application Scenarios
• Autonomous Driving Cloud Platforms: Enable Three Types of Functions
• Examples of Autonomous Driving Cloud Service Providers: (1)-(2)
• World Models Empower the Actual Value of Autonomous Driving Cloud
• 2.2 Internet of Vehicles Cloud
• Application Scenarios
• Demand for Cloud (1)-(3)
• Examples of Cloud Service Providers: (1)-(2)
• 2.3 V2X Cloud
• Overview
• Service Architecture: Common Architecture
• Service Architecture: Architecture Segments
• Automotive Cloud Computing: Six Services
• Automotive Cloud Computing: Problems And Solutions
• Examples of Cloud Service Providers: (1)-(2)
• 2.4 Digitization
• Overview
• Demand for Cloud
• 2.5 Cloud Data Closed Loop
• Overview
• Role of Cloud Platform in Data Closed Loop: (1)-(3)
• Cloud Platform Data Closed Loop Cases: (1)-(5)
• 2.6 AI Cloud
• Application Scenarios
• Reference Architecture
• Application of AI in IaaS, PaaS and MaaS
• Integration of AI Cloud Computing and Intelligent Computing
• Cloud AI Accelerator
• Collaborative Deployment of AI Cloud and Edge
• 2.7 Cloud Information Security
• Internet of Vehicles Security Challenges
• Cloud Security Scenarios
• Cloud Information Threats
• Cloud Information Security Architecture
• Cloud Security Strategies: (1)-(5)
• Typical Cases of Cloud Security: (1)-(4)
• 2.8 SOA Cloud
• Cloud Native in SOA
• Cases (1)-(2)

3 Cloud Platform Infrastructure

• 3.1 Automotive Cloud Industry Chain
• 3.2 Data Centers: Distribution
• 3.2 Data Centers: Public Cloud Data Center Layout
• 3.3 Cloud Servers
• 3.4 Server Chips: Technical Roadmap
• 3.4 Server Chips: Chip Suppliers
• 3.5 Self-Developed Chips of Cloud Providers: (1)-(5)

4 Automotive Public Cloud Platforms

• 4.1 Amazon Web Services (AWS)
• Overview of Automotive Cloud Business
• Regional Distribution
• Automotive Industry Layout
• AWS for Automotive
• Solutions for Software-Defined Vehicles
• Internet of Vehicles Data Lake
• Autonomous Driving Data Lake
• SDV Cloud Development Architecture
• Cloud Digital Operation
• Cloud Data Management
• Automotive Industry Customers
• Supply Relationship
• Cooperation Cases: (1)-(7)
• 4.2 Microsoft Cloud Azure
• Automotive Solutions
• Internet of Vehicles Cloud Platform
• Microsoft Connected Vehicle Platform (MCVP): Business Models and Major Customers
• Microsoft Connected Vehicle Platform (MCVP): Ecosystem Partners
• Cooperative Auto Parts Companies
• Cooperative OEMs
• 4.3 Google Cloud Platform (GCP)
• GCP
• Cooperation with Qualcomm
• 4.4 Huawei Automotive Cloud
• Business
• Automotive Solutions
• Internet of Vehicles solutions
• Autonomous Driving Development Solution: Ascend Platform
• Mobile Mobility Solutions
• Automotive Simulation Solutions
• Digital Intelligent Platform Solutions
• Digital Marketing Solutions
• Overseas Business Solutions (1)-(5)
• Cooperative Customers (1)-(2)
• Internet of Vehicles Cloud Technology: Serverless Technology
• Infrastructure
• Dynamics: FAW-Volkswagen Manufacturing Cloud
• 4.5 Baidu Automotive Cloud
• Baidu Cloud Autonomous Driving Solutions (1)-(4)
• Baidu Internet of Vehicles Cloud
• Baidu Internet of Vehicles Cloud: OEM Customers
• Baidu V2X Cloud
• Baidu Cloud Data Closed Loop Solutions
• Baidu Cloud Data Annotation Solutions
• Baidu Cloud Security System
• Baidu Autonomous Driving Cloud's Support Base for VLA
• Baidu Autonomous Driving Cloud's Support Base for VLA (1): Computing Power
• Baidu Autonomous Driving Cloud's Support Base for VLA (2): Data
• Baidu Autonomous Driving Cloud's Support Base for VLA
• 4.6 Alibaba Automotive Cloud
• Business
• Industrial Capabilities
• Technical Base: (1)-(4)
• Main Customers
• Internet of Vehicles Security Solution: Cloud-Network-Edge Integrated Joint Defense
• Overseas Services
• 4.7 Tencent Automotive Cloud
• Business
• Architecture: AI Cloud
• Cockpit Function Layout
• "Cloud-Map Integration" Layout
• Architecture: Next-Generation Data Closed Loop
• Autonomous Driving Cloud
• Intelligent connected cloud
• Capability
• Security mechanism
• Automakers served
• 4.8 ByteDance Automotive Cloud
• Business
• System architecture
• System Architecture: Autonomous Driving Tool Chain
• Important Nodes in Business Development
• Ecology
• Computing Capabilities of ByteDance Cloud
• Multi-Cloud Disaster Tolerance Architecture of Volcano Engine: (1)-(5)
• Latest Cooperation with OEMs: (1)-(2)

5 OEM Cloud Platform Layout

• OEM Solution Comparison
• 5.1 Geely
• Geely's Cloud Platform Strategy
• Geely's Digital Transformation Strategic Plan
• Geely Group's Cloud Platform Solution and Planning
• Geely's Cooperation Cases (1)-(3)
• Geely Cloud IVI
• Geely's Internet of Vehicles Cloud Platform Technology
• ZEEKR's Internet of Vehicles Cloud Cooperation Case
• The Cloud Smart Center of ZEEKR AI BMS Has Four Major Protection Functions
• 5.2 Xpeng
• Cloud Platform
• Cooperation with Alibaba Cloud
• 5.3 Li Auto
• Cloud Platform Layout
• IoV Cloud
• Data Storage Solution
• Application of Device-Cloud Integration Architecture in Functional Safety (1): Problems
• Application of Device -Cloud Integration Architecture in Functional Safety (2): Solutions
• Application of Device -Cloud Integration Architecture in Functional Safety (3): Effects
• Internet of Vehicles Cloud Data Analysis Platform (1): Problems
• Internet of Vehicles Cloud Data Analysis Platform (2): Architecture and Characteristics
• Internet of Vehicles Cloud Data Analysis Platform (3): Performance and Guarantee
• Internet of Vehicles Cloud Data Analysis Platform (4): History
• Internet of Vehicles Cloud Data Analysis Platform (5): Usage Effects
• Internet of Vehicles Cloud Data Analysis Platform (6): Scenarios
• Internet of Vehicles Cloud Data Analysis Platform (7): Future Planning
• Internet of Vehicles Architecture Based on NDN Helps Vehicle-Cloud Collaboration
• Vehicle-Cloud Collaborative Architecture of Data Observability Platform
• Cloud AI Is Used in Manufacturing
• 5.4 NIO
• Hybrid Cloud
• Energy Cloud: AI Order Prediction and Edge-Cloud Collaboration Strategy
• Cloud Infrastructure Team
• 5.5 FAW
• Cloud Platform Layout
• Hongqi Smart Cloud
• Local Data Centers
• Cooperation Cases (1)-(4)
• 5.6 Changan
• Digital Path: Cloud Migration Phase
• Digital Path: Data Governance Phase
• Digital Path: Intelligence Enablement Phase
• Cloud Platform Big Data
• Autonomous Vehicle Cloud Big Data Processing Architecture
• Internet of Vehicles Cloud and R&D Cloud
• Device-Cloud Integration SDA
• Device-Cloud Integration Service Ecosystem
• Automotive Cloud Platform Partners (1)-(2)
• Vehicle-Road-Cloud Integrated Project
• Avatr's Private Cloud Deployment
• 5.7 Great Wall Motor
• Intelligent Cloud
• Great Wall Motor and Huawei Cloud
• Internet of Vehicles Cloud Bus Technology Uses Alibaba Cloud Services
• AWS and Alibaba Cloud Are Used Overseas
• Automotive AI Cloud
• 5.8 SAIC
• Cloud Business Layout
• Cloud Products and Services
• Cloud Platform: (1)-(2)
• Autonomous Driving Cloud
• Fin-Shine Combines the Data Process of Cloud Foundation Models
• Fin-Shine's Intelligent Connectivity Cloud
• Fin-Shine's Cloud Cooperation Cases
• Cloud Product Technical Route and Security Route
• Overseas Cooperation with AWS
• Z-One Completes CVIS Energy Saving through Cloud Mechanism
• Cloud Platform Security Protection
• 5.9 GAC
• Cooperate with Tencent in Internet of Vehicles Cloud/GAC Autonomous Driving Cloud
• Cooperate with ByteDance Cloud in Digital Cloudification
• Cooperation with Alibaba Cloud
• DevOpsPaaS Service Platform: Three Construction Stages
• DevOpsPaaS Service Platform: Features and Effects
• DevOpsPaaS Service Platform: Features
• DevOpsPaaS Service Platform: Effects
• Cloud IVI
• 5.10 Dongfeng Motor
• Vehicle-Road-Cloud Integration Projects (1)
• Vehicle-Road-Cloud Integration Projects (2)
• 5.11 BAIC
• Vehicle-Road-Cloud Integration
• Internet of Vehicles Cloud Adopts Alibaba Cloud
• 5.12 BMW
• AWS Cloud and Microsoft Azure Cloud
• AWS Cloud
• Microsoft Azure Cloud
• 5.13 Mercedes-Benz
• Access AI through Microsoft Azure Cloud
• Optimize AI with Google Cloud
• Use AWS Cloud for IT Management
• Cloud Technology
• 5.14 Stellantis
• Cooperation History
• Cooperation Cases with AWS Cloud (1)
• Cooperation Cases with AWS Cloud (2): VEW Platform Development
• 5.15 GM