Beijing: Alibaba’s cloud computing unit is making its Apsara operating system compatible with processors based on Arm, x86, RISC-V, among other architectures, the company has said.

Alibaba Cloud is one of the fastest-growing businesses for the Chinese e-commerce giant and the world’s fourth-largest public cloud service in the second half of 2020, according to market research firm IDC.

“The IT ecosystem was traditionally defined by chips, but cloud computing fundamentally changed that,” Zhang Jianfeng, president of Alibaba Cloud’s Intelligence group, said at the conference on Friday.

A cloud operating system can standardise the computing power of server chips, special-purpose chips and other hardware, so whether the chip is based on x86, Arm, RISC-V or a hardware accelerator, the cloud computing offerings for customers are standardized and of high-quality,” Jianfeng added.

The global chip market has mostly been dominated by Intel’s x86 in personal computing and Arm for mobile devices.

But RISC-V, an open-source chip architecture competitive with Arma’s technologies, is gaining popularity around the world, especially with Chinese developers.

Started by academics at the University of California, Berkeley, RISC-V is open to all to use without licensing or patent fees and is generally not subject to America’s export controls.

The Trump Administration’s bans on Huawei and its rival ZTE over national security concerns have effectively severed ties between the Chinese telecom titans and American tech companies, including major semiconductor suppliers.

Arm was forced to decide its relationships with Huawei and said it could continue licensing to the Chinese firm as it’s of UK origin, the report said.

But Huawei still struggles to find fabs that are both capable and allowed to manufacture the chips designed using the architecture, it added.

The US sanctions led to a burst in activity around RISC-V in China’s tech industry as developers prepare for future tech restrictions by the US, with Alibaba at the forefront of the movement.

Alibaba Cloud, Huawei and ZTE are among the 13 premier members of RISC-V International, which means they get a seat on its Board of Directors and Technical Steering Community.

What Is Serverless Computing?

Despite the name, serverless computing still runs on servers — the difference is that developers no longer provision, scale, or manage them. Cloud providers handle all infrastructure concerns, and code runs in stateless, event-driven functions that scale automatically with demand. Billing is based on the number of invocations and the duration of execution rather than reserved instance hours.

Common serverless primitives include Function-as-a-Service (FaaS) platforms (such as AWS Lambda, Azure Functions, and Google Cloud Functions), managed backends for databases, authentication, and messaging, and fully serverless container platforms.

Market Size and Growth Trajectory

Market research published around 2020 estimated the global serverless computing market at approximately USD 6.5 billion in 2019, with projections to reach roughly USD 13.4 billion by 2026 — implying a compound annual growth rate (CAGR) in the region of 19–20% over that period. These figures originated from a third-party market intelligence report (originally cited from newdaylive.com, sourcing a Reports Intellect study) and should be treated as estimates rather than audited data.

The primary drivers behind that growth include rising cloud adoption, cost pressure to eliminate idle compute capacity, the explosion of event-driven microservices architectures, and developer productivity gains from abstracting infrastructure concerns.

Leading Market Players

The serverless computing market is dominated by major hyperscale cloud providers, each offering a mature FaaS platform and a surrounding ecosystem of managed services:

• Amazon Web Services (AWS) — AWS Lambda, the first widely adopted FaaS platform (launched 2014), remains the market leader. It integrates tightly with API Gateway, EventBridge, Step Functions, and hundreds of other AWS services.
• Microsoft Azure — Azure Functions supports multiple programming models and integrates with Azure Logic Apps for low-code workflows. The Durable Functions extension enables stateful orchestration patterns.
• Google Cloud — Google Cloud Functions and the newer Cloud Run (container-based, serverless) platform give developers flexible options from fine-grained functions to full containerized workloads.
• Alibaba Cloud — Function Compute is Alibaba’s FaaS offering, prominent in Asia-Pacific markets and deeply integrated with Alibaba’s e-commerce and logistics ecosystem.
• IBM Cloud — IBM Cloud Functions, built on the open-source Apache OpenWhisk project, targets enterprise customers with hybrid and regulated-workload requirements.
• Rackspace — Primarily a managed cloud services provider, Rackspace helps enterprises architect and operate serverless workloads across multiple cloud platforms rather than offering a proprietary FaaS runtime.

Deployment Types

Public Cloud

The vast majority of serverless workloads run on public cloud platforms. Developers invoke functions through managed endpoints and pay per execution. This model provides the lowest operational overhead and the fastest time-to-market, making it the default choice for startups and cloud-native teams.

Private Cloud

Regulated industries — financial services, healthcare, defense — often require workloads to run within a private environment. Private cloud serverless deployments use platforms such as OpenFaaS, Knative, or vendor-managed on-premises offerings. While operationally heavier than public cloud, private deployments enable organizations to meet data residency, compliance, and security requirements while still benefiting from event-driven, pay-per-use architecture patterns.

Key Industry Verticals

Serverless computing is being adopted across a wide range of industries. The segments most frequently cited in market research include:

• Banking, Financial Services, and Insurance (BFSI) — Event-driven transaction processing, fraud detection pipelines, and real-time risk calculations benefit from the fine-grained scaling serverless provides.
• Telecommunications and IT — Network event processing, API backends, and DevOps automation pipelines are common serverless use cases in this segment.
• Retail and eCommerce — Flash-sale traffic spikes are a textbook serverless scenario: functions scale from zero to thousands of concurrent executions in seconds and scale back down just as fast, avoiding over-provisioning costs.
• Healthcare and Life Sciences — Genomics data processing, medical imaging pipelines, and patient notification systems leverage serverless for cost-efficient, intermittent compute workloads.
• Government and Public Sector — Citizen-facing digital services and data processing workflows benefit from serverless economics, though compliance requirements often push deployments toward private or sovereign cloud environments.
• Manufacturing — IoT sensor data ingestion, predictive maintenance event streams, and supply-chain automation are increasingly built on serverless event pipelines.
• Media and Entertainment — Video transcoding triggers, content delivery personalization, and live-event audience scaling are well-suited to serverless architectures.

Regional Landscape

North America historically accounts for the largest share of serverless compute spending, driven by the concentration of cloud-native enterprises and the headquarters of the dominant FaaS providers. Europe follows, with growth accelerating as GDPR compliance encourages migration to managed, auditable cloud services. Asia-Pacific — particularly China, India, Japan, and Southeast Asia — represents the fastest-growing regional market, fueled by rapid digital transformation, mobile-first commerce, and the expanding footprint of Alibaba Cloud and local cloud providers. South America and the Middle East & Africa regions show emerging adoption, with growth tied to broader cloud infrastructure investment.

Key Growth Drivers and Challenges

Drivers

• Reduced operational overhead — No server provisioning, patching, or capacity planning means smaller ops teams can support larger systems.
• Granular cost model — Organizations pay only for actual execution time, making serverless economically attractive for intermittent or unpredictable workloads.
• Developer velocity — Abstracting infrastructure lets development teams focus on business logic rather than platform management.
• Native event-driven integration — Cloud providers offer deep integration between FaaS and messaging, storage, database, and streaming services, accelerating event-driven architecture adoption.

Challenges

• Cold starts — Functions that haven’t been invoked recently incur a startup latency penalty, which can affect latency-sensitive applications.
• Vendor lock-in — FaaS APIs and event source integrations differ significantly between providers, making portability difficult without an abstraction layer.
• Observability complexity — Distributed, short-lived functions make distributed tracing and log correlation more demanding than traditional service architectures.
• Execution duration limits — Most FaaS platforms cap individual function execution time, limiting suitability for long-running batch jobs.

Further Reading

For hands-on guidance, refer to the official documentation for AWS Lambda, Azure Functions, and Google Cloud Functions. The Cloud Native Computing Foundation (CNCF) also maintains a landscape of serverless and FaaS projects including Knative and OpenFaaS.

Overview

The cloud computing chips market encompasses the specialized semiconductors—GPUs, FPGAs, and ASICs—that power modern cloud infrastructure. As hyperscalers and enterprises push workloads ranging from AI inference to real-time analytics into the cloud, demand for purpose-built silicon has accelerated sharply. A market research report summarized by whatech.com profiles the competitive landscape, regional growth patterns, investment activity, and application-segment breakdown for this sector, covering the period from 2019 through a 2026 forecast horizon.

Key Players in the Cloud Computing Chips Market

The report identifies the following companies as major participants in the global cloud computing chips market:

• Intel – dominant in general-purpose server CPUs; also competes in FPGAs (via the 2015 Altera acquisition) and AI accelerators.
• NVIDIA – leads the GPU segment for cloud AI training and inference workloads.
• AMD – growing presence in both CPUs (EPYC) and GPUs for cloud deployments.
• Xilinx – a primary FPGA vendor for cloud workloads (subsequently acquired by AMD in 2022).
• Amazon – develops custom ASICs such as Graviton (Arm-based CPU) and Inferentia (AI inference) for AWS.
• Google – designs Tensor Processing Units (TPUs), purpose-built ASICs for machine learning on Google Cloud.
• Microsoft – deploys FPGAs (Project Catapult) and custom silicon (Azure Maia, Cobalt) for Azure infrastructure.
• Alibaba – invests in custom cloud chips (e.g., Yitian 710 Arm-based CPU) for Alibaba Cloud.
• Huawei – produces Kunpeng (Arm CPU) and Ascend (AI) chips for its cloud platform.
• Baidu – develops the Kunlun AI chip for cloud inference and training.
• Cambricon – Chinese AI chip startup focused on neural network accelerators.
• Unisoc – Chinese semiconductor company with growing cloud and mobile chip portfolios.
• Samsung Electronics – major foundry and chip designer supplying memory and logic for cloud servers.

For each player, the report examines company profiles, recent market activity, competitive positioning via SWOT analysis, revenue figures, investment levels, and strategic partnerships.

Chip Types: Market Segments by Technology

The cloud computing chips market is segmented by three primary chip architectures, each suited to different cloud workloads:

Graphics Processing Unit (GPU)

GPUs excel at massively parallel computation, making them the dominant choice for AI/ML model training, high-performance computing (HPC), and graphics rendering in the cloud. NVIDIA holds a commanding position in this segment, with AMD competing through its Instinct accelerator line.

Field Programmable Gate Array (FPGA)

FPGAs are reconfigurable chips that can be programmed post-manufacture to accelerate specific workloads—including network packet processing, real-time data analytics, and AI inference. Intel (via Altera) and Xilinx are the primary suppliers. Microsoft and Amazon have both deployed FPGAs at scale inside their data centers.

Application-Specific Integrated Circuit (ASIC)

ASICs are custom chips designed for a single, well-defined task. They deliver the best performance-per-watt for that task but cannot be reprogrammed. Hyperscalers including Google (TPU), Amazon (Inferentia, Trainium, Graviton), and Baidu (Kunlun) have invested heavily in proprietary ASICs to reduce costs and improve efficiency at cloud scale.

Application Verticals

Demand for cloud computing chips spans a wide range of industry verticals. The report breaks these down as follows:

• BFSI (Banking, Financial Services & Insurance) – fraud detection, algorithmic trading, risk modeling.
• Manufacturing – predictive maintenance, quality inspection via computer vision, supply-chain optimization.
• Government – defense, intelligence analytics, citizen-services digitization.
• IT & Telecom – network virtualization, 5G core processing, CDN acceleration.
• Retail – recommendation engines, demand forecasting, inventory management.
• Transportation – autonomous vehicle data processing, logistics optimization.
• Energy & Utilities – grid management, predictive equipment maintenance, smart-meter analytics.
• Others – healthcare, media & entertainment, education, and more.

Regional Breakdown

The report segments global market data by geography to reflect regional growth rates, revenue contributions, and market-share dynamics:

• North America – home to AWS, Azure, and Google Cloud; currently the largest regional market driven by hyperscale data center investment.
• Asia-Pacific – fastest-growing region, led by Chinese cloud players (Alibaba, Huawei, Baidu, Tencent) and strong semiconductor manufacturing bases in Taiwan, South Korea, and Japan.
• Europe – driven by GDPR-compliant cloud adoption and growing sovereign cloud initiatives.
• South America – emerging market with rising enterprise cloud adoption.
• Middle East & Africa – nascent but growing, supported by government digitization programs and new hyperscale data center investments.

What the Report Covers

According to the summary, the research report addresses the following analytical dimensions:

• Global market status and future forecasts through 2026.
• Revenue rankings and market-share data for top manufacturers.
• Revenue growth rates by chip type and application segment (2020 vs. 2026).
• Import/export dynamics, supply and demand figures, pricing, and gross margins.
• Upstream raw materials and equipment analysis; downstream demand analysis.
• Competitive profiles, SWOT analyses, and strategic development plans for key players.
• Funding and investment analysis, and market capitalization data for leading companies.
• Feasibility assessment for new investment projects in the sector.
• Marketing channels and industry development trends.

Why Cloud-Specific Silicon Matters for DevOps Teams

For DevOps and platform engineers, understanding the chip landscape is increasingly relevant. The type of compute instance you select—CPU-optimized, GPU-optimized, or FPGA-backed—directly affects workload performance, cost, and availability. Hyperscaler investment in custom ASICs (Graviton, TPU, Trainium) has already changed instance pricing models on AWS and Google Cloud, often offering better price-performance for specific workloads than x86 alternatives. Tracking which chip vendors are gaining or losing ground in this market helps engineers make more informed infrastructure decisions and anticipate upcoming instance types or capabilities on their preferred cloud platform.

Key Takeaways

• The cloud computing chips market is competitive and rapidly evolving, with both established semiconductor giants (Intel, NVIDIA, AMD) and hyperscaler-designed silicon (Amazon Graviton, Google TPU) shaping the landscape.
• GPUs dominate AI/ML workloads; FPGAs offer reconfigurable acceleration; ASICs deliver maximum efficiency for fixed tasks at scale.
• Asia-Pacific, driven by China-based cloud and chip companies, is a key growth region alongside North America.
• Vertical adoption is broad—virtually every major industry segment is a consumer of cloud silicon capacity.
• Hyperscaler investment in proprietary chip design is a long-term structural trend that will continue to reshape vendor rankings and pricing dynamics.

This article is based on a market report summary originally published on whatech.com in January 2020. Specific revenue figures and forecast numbers cited in the original report tables have not been independently verified.

Major Cloud Vendors Serving Retail Banking

The competitive landscape in cloud services for retail banking is broad. Hyperscalers and specialised enterprise software vendors both compete for bank workloads. Prominent players include:

• Amazon Web Services (AWS) – Offers dedicated financial-services competency programmes, compliance frameworks (PCI-DSS, SOC 2), and managed database and AI/ML services widely used by banks.
• Microsoft Azure – Provides strong integration with enterprise productivity tools and Azure Government/Sovereign Cloud options for regulated data residency requirements.
• IBM – Long-standing banking partner with hybrid cloud offerings built on Red Hat OpenShift, plus managed services for mainframe workload migration.
• Google Cloud – Increasingly targeting banking analytics and AI workloads, with dedicated financial-services industry solutions.
• Alibaba Cloud, Tencent Cloud, Huawei Cloud, Baidu AI Cloud, China Telecom, China Unicom, Kingsoft Cloud, and UCloud – Dominant cloud providers serving Asia-Pacific retail banks, particularly in China where domestic regulations shape cloud procurement.
• Infosys, TCS, and Wipro – Systems integrators and managed-service providers that deliver cloud migration, DevOps tooling, and banking-platform modernisation on top of hyperscaler infrastructure.
• Oracle, SAP, Salesforce, Workday, and Intuit – SaaS and PaaS vendors supplying core banking, CRM, ERP, and financial-management capabilities as cloud-native or cloud-hosted services.
• Medidata, Veeva Systems, and Ellie Mae – Vertical SaaS providers whose platforms intersect with banking and financial-services data management use cases.
• BBVA and Bankinter – Notable examples of retail banks that have invested heavily in internal cloud platforms and open-banking APIs, making them both consumers and, in some cases, providers of cloud-based financial technology.

Market Segmentation

By Service Type

• Infrastructure as a Service (IaaS) – Compute, storage, and networking resources consumed on demand; commonly used for disaster-recovery, batch processing, and dev/test environments.
• Platform as a Service (PaaS) – Managed runtime environments, databases, messaging queues, and integration layers that reduce operational overhead for development teams.
• Software as a Service (SaaS) – Fully managed applications such as core-banking systems, CRM, fraud-detection engines, and regulatory-reporting tools.

By Deployment Model

• Public Cloud – Shared infrastructure managed by a hyperscaler; cost-efficient for non-sensitive workloads.
• Private Cloud – Dedicated infrastructure operated on-premises or in a co-location facility; preferred for sensitive customer and payment data.
• Hybrid Cloud – Combination of public and private environments connected by orchestration tooling; increasingly the dominant model in banking because it allows regulatory data-residency controls alongside cloud-native scalability.

By Application

• Core banking system modernisation
• Customer relationship management (CRM) and personalisation
• Fraud detection and anti-money-laundering (AML) analytics
• Regulatory compliance and reporting
• Mobile and digital banking platforms
• Risk management and stress testing
• Data warehousing and business intelligence

Regional Landscape

Cloud adoption patterns in retail banking vary significantly by region, driven by local regulation, infrastructure maturity, and competitive pressure from fintech entrants.

North America (United States, Canada, Mexico)

North America represents one of the most mature markets. Large US banks have invested heavily in multi-cloud strategies, and regulators such as the OCC and FFIEC have issued guidance on cloud risk management rather than outright restrictions. Canada’s OSFI and Mexico’s CNBV each impose their own data-residency and third-party risk requirements.

Europe (Germany, France, UK, Russia, Italy)

GDPR, EBA outsourcing guidelines, and the EU Digital Operational Resilience Act (DORA) heavily influence cloud procurement decisions. Many European banks favour hybrid or sovereign-cloud arrangements to maintain data control. The UK’s post-Brexit regulatory environment, managed by the PRA and FCA, adds additional complexity for cross-border deployments.

Asia-Pacific (China, Japan, South Korea, India, Southeast Asia)

Asia-Pacific is one of the fastest-growing regions for cloud adoption in banking. China’s domestic cloud providers (Alibaba, Tencent, Huawei, Baidu, China Telecom, China Unicom) dominate the local market due to data-localisation requirements. India’s RBI has issued cloud-adoption frameworks encouraging managed migration. Southeast Asian markets show rapid growth driven by digital-only bank licensing regimes.

South America (Brazil, Argentina, Colombia)

Brazil’s LGPD privacy law and the Central Bank of Brazil’s open-finance initiatives are accelerating cloud investment. Hyperscalers have expanded local infrastructure to meet data-residency requirements.

Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa)

Cloud adoption is growing from a lower baseline. The UAE and Saudi Arabia have established clear cloud-first government policies that indirectly influence banking. Nigeria and South Africa represent the leading African markets, with central banks developing cloud-risk guidance.

Key Growth Drivers

• Legacy system debt – Decades-old mainframe and on-premises applications are expensive to maintain and difficult to integrate with modern APIs, creating a strong business case for cloud migration.
• Fintech competition – Digital-native challengers operate at lower cost bases using cloud-native architectures, pressuring incumbent banks to modernise.
• Customer experience demands – Real-time payments, personalised offers, and always-on mobile services require the elastic scalability that cloud platforms provide.
• Regulatory reporting volume – Increasing regulatory complexity demands analytics platforms capable of processing large, rapidly changing datasets—a strength of cloud data warehousing.
• AI and machine learning adoption – Fraud detection, credit scoring, and customer segmentation models require compute resources that are cost-prohibitive to provision on-premises at scale.
• Operational resilience requirements – Regulators increasingly demand active-active disaster-recovery capabilities that public cloud regions and availability zones can deliver more economically than traditional data-centre pairs.

Key Restraints and Challenges

• Data sovereignty and residency rules – Regulations in many jurisdictions restrict where customer financial data can be stored or processed, limiting hyperscaler flexibility.
• Concentration risk – Regulators worry that widespread dependence on a small number of hyperscalers creates systemic risk for the financial sector.
• Security and compliance complexity – Banks must maintain PCI-DSS, SOC 2, ISO 27001, and local regulatory certifications across cloud environments, increasing operational overhead.
• Skills gaps – Cloud-native development, DevSecOps, and FinOps expertise remain scarce in many banking organisations.
• Migration risk – Moving mission-critical core-banking systems carries significant operational risk and requires extensive testing and rollback planning.

Technology and Innovation Trends

• API-led open banking – Cloud platforms provide the managed API gateways and event-streaming infrastructure (e.g., Kafka-as-a-service) that open-banking mandates require.
• Containerisation and Kubernetes – Banks are increasingly packaging workloads in containers to gain portability across cloud providers and reduce vendor lock-in.
• Serverless and event-driven architectures – Reducing idle compute costs for transaction-processing pipelines with variable load profiles.
• Confidential computing – Hardware-based trusted execution environments (TEEs) allow sensitive banking data to be processed in the cloud without exposing plaintext to the cloud provider.
• Generative AI integration – Banks are piloting large-language models for customer service automation, document processing, and code generation, typically through managed cloud AI services.

Conclusion

Cloud computing is no longer an emerging concept in retail banking—it is an operational reality for institutions of all sizes. The combination of legacy modernisation pressure, fintech competition, and demand for real-time, data-driven services means that cloud adoption will continue to accelerate across all regions. For DevOps and platform engineering teams working in financial services, understanding the regulatory context, vendor landscape, and architectural patterns specific to banking is essential for designing resilient, compliant, and cost-effective cloud environments.