Global AI Infrastructure Market Size, Share & Industry Trends Analysis Report By Offering (Hardware and Server Software), By End User, By Deployment Type, By Function (Inference and Training), By Technology, By Regional Outlook and Forecast, 2022 - 2028

Global AI Infrastructure Market Size, Share & Industry Trends Analysis Report By Offering (Hardware and Server Software), By End User, By Deployment Type, By Function (Inference and Training), By Technology, By Regional Outlook and Forecast, 2022 - 2028

The Global AI Infrastructure Market size is expected to reach $122.8 billion by 2028, rising at a market growth of 24.7% CAGR during the forecast period.

Platforms for building intelligent applications that really are predictive, self-healing, and need little human interaction are known as artificial intelligence (AI) infrastructure. Innovative technologies such as Mobility, IoT, and Big Data are straining IT infrastructure. More than ever, the requirement for intelligent infrastructure is essential for maximizing the potential of AI systems. Every stage of the machine learning workflow is supported by AI infrastructure.

It enables the management and availability of computer resources for the development, testing, and deployment of AI algorithms by data engineers, data scientists, DevOps teams, and software developers. The workload is mapped to the appropriate setup of servers and virtual machines using AI infrastructure. Organizations can now concentrate on resource use, capacity planning, storage management, anomaly detection,

The market for AI infrastructure is expanding as a result of rising adoption of cloud machine learning platforms in businesses and rising demand for AI hardware in strong computing data centres. The market for AI infrastructure is anticipated to grow as the use of Ai technologies in vertical industries like healthcare, BFSI, automotive, and tourism increases. The market for AI infrastructure is also expanding for other reasons.

As businesses increase operational effectiveness and cut costs by automating process flows, they are coming to see the value of integrating artificial intelligence (AI) into their business operations. As a result, businesses have started utilizing autonomous processes to enhance operations and transform customer service (for instance, through chatbots driven by AI), all the while catalyzing creativity to new heights.

COVID-19 Impact Analysis

Due to the dynamic nature of cloud services and their ability to handle unanticipated surges in service demand, the COVID-19 outbreak led many enterprises to expedite their migrations to public cloud solutions. In the era of COVID-19, cloud migrations let enterprises reimagine how they run their operations. Numerous cloud providers now deliver AIaaS and MLaaS due to the increase in demand for AI services. Due to this, the healthcare sector of the cloud market had significant growth in 2020. A lot of AI and ML technology is being employed to combat COVID-19.

Market Growth Factors

Increasing Focus On Parallel Computing In Ai Data Centers

In data centres, CPUs are utilised for serial computing to keep track of a number of memory regions where data and instructions are kept. A processor analyses the instructions and data at the memory locations to perform computations in serial. The steps of a computation are logically ordered and sequential in serial computation. In addition, a processor at a data centre divides a single task into a number of distinct instructions sets that are carried out in a serial fashion. This frequently causes latency issues in data centres, especially when performing AI-based calculations with large data and instruction sets.

Rising Demand For Ai Software And Hardware Among Enterprises

In order to keep up with the growing volume of data created by applications, advanced AI solutions frequently necessitate new hardware and software. These AI-based solutions, for instance, require updates regarding the annotation and collection of data sources, as well as the creation, processing, and fine-tuning of models when more data becomes available. Deep learning, a branch of AI technology, has grown to be one of the most significant computational workloads for businesses and will increase the use of AI infrastructure.

Market Restraining Factors

Absence Of Skilled Professionals In The Market

Companies need expertise and a competent team to create, manage, and integrate AI systems because they are a complicated system. People working with AI systems must be knowledgeable about technologies including machine intelligence, deep learning, machine learning (ML), image recognition, and cognitive computing. Additionally, it is a difficult undertaking that necessitates well-funded internal R&D and patent filing to integrate AI technology into already-existing systems. Even small mistakes can result in system failure or solution malfunction, which can have a significant impact on the result and intended results.

Offering Outlook

On the basis of offering, the AI infrastructure market is segmented into Hardware and Server Software. Hardware segment procured the highest revenue share in the AI infrastructure market in 2021. Processors, storage, memory, and interconnects are hardware components needed to create an AI infrastructure. Smaller, more effective, and more potent xeromorphic chip-based systems are anticipated to displace huge hardware devices in the upcoming years due to the rapid advancement of technology.

Technology Outlook

Based on technology, the AI infrastructure market is classified into Machine Learning and Deep Learning. Machine Learning segment registered the largest revenue share in the AI infrastructure market in 2021. It is because it aids in the development of new goods and provides businesses with a picture of trends in consumer behaviour and operational business patterns. A significant portion of the operations of numerous of today's top businesses, like Google, Facebook, and Uber, revolve around machine learning.

Function Outlook

By function, the AI infrastructure market is bifurcated into Training and Inference. Training segment registered a significant revenue share in the AI infrastructure market in 2021. The cloud is a great place to train algorithms because it gives users access to enormous data repositories across many servers. The more data an AI application evaluates during training, the more effective its algorithm would become.

Deployment Type Outlook

On the basis of deployment, the AI infrastructure market is fragmented into On-premises, Cloud and Hybrid. The cloud deployment mode segment procured the highest revenue share in the AI infrastructure market in 2021. Reduced operational expenses, fuss-free deployment, high scalability, speedy data accessibility, quicker access to crucial data, and low capital requirements are just a few advantages of cloud deployment mode.

End-User Outlook

Based on end-user, the AI infrastructure market is categorized into Enterprises, Government Organizations and Cloud Service Providers. The enterprises segment procured a significant revenue share in the AI infrastructure market in 2021. Through the usage of artificial intelligence, numerous businesses around the world are finding new insights, revenue, and efficiencies (AI). Companies are also learning that by changing the way they approach their infrastructure, they may speed up their initiatives.

Regional Outlook

Region-wise, the AI infrastructure market is analyzed across North America, Europe, Asia Pacific and LAMEA. Asia Pacific registered a promising revenue share in the AI infrastructure market in 2021. The development of AI data centres in China is continuing to change as more domestic and international businesses switch to cloud service providers (CSPs) and co-location options. Due to companies looking for better connection and scalable solutions for their expanding enterprises, the need for AI data centres in the nation has surged.

The major strategies followed by the market participants are Acquisitions. Based on the Analysis presented in the Cardinal matrix; Google LLC is the forerunners in the AI Infrastructure Market. Companies such as Intel Corporation, Samsung Electronics Co., Ltd., IBM Corporation are some of the key innovators in AI Infrastructure Market.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Oracle Corporation, Intel Corporation, Samsung Electronics Co., Ltd. (Samsung Group), IBM Corporation, Google LLC, Amazon Web Services, Inc. (Amazon.com, Inc.), Hewlett-Packard Enterprise Company, Cisco Systems, Inc., Dell Technologies, Inc., and Toshiba Corporation.

Recent Strategies Deployed in AI Infrastructure Market

Partnership, Collaboration and Agreement:

Jul-2022: Google entered into a partnership with Northwell Health, the largest healthcare system in New York. This partnership aimed to use Google’s machine learning (ML), artificial intelligence (AI), and cloud capabilities. Additionally, the partnership would escalate the health system’s digital transformation by utilizing cloud technology and artificial intelligence (AI), to enhance clinician experience, patient care, and operational efficiency.

Product Launch and Product Expansion:

May-2021: Google Cloud announced Vertex AI, a new managed machine learning platform that is meant to make it easier for developers to deploy and maintain their AI models. it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work. Ultimately, our goal with Vertex is to reduce the time to ROI for these enterprises, to make sure that they cannot just build a model but get real value from the models they’re building.

Apr-2021: Intel released its most advanced artificial intelligence-based data center platform. This platform would offer a substantial performance boost as compared with the previous generation, with an average 46% advancement on popular data center workloads.

Acquisition and Merger:

Dec-2021: Oracle Corporation acquired Cerner Corporation, an American supplier of health information technology services, devices, and hardware. This acquisition aimed to assist Oracle to scale up its cloud business in the hospital and health system market.

Nov-2021: IBM today announced the acquisition of SXiQ, an Australian digital transformation services company specializing in cloud applications, cloud platforms and cloud cybersecurity. IBM's acquisition of SXiQ brings additional hybrid and multicloud expertise that is at the core of open innovation for clients. SXiQ will enhance IBM Consulting's capabilities in Australia and New Zealand to modernize applications and technology infrastructure in the cloud.

Jun-2021: Hewlett Packard Enterprise completed the acquisition of Determined AI, a San Francisco-based startup. This acquisition aimed to provide a strong and robust software stack to train AI models quicker, at any scale, utilizing its open source machine learning (ML) platform.

Jun-2021: IBM today announced the closing of its acquisition of Turbonomic, Inc., an Application Resource Management (ARM) and Network Performance Management (NPM) software provider based in Boston, MA. Now that Turbonomic is a part of our portfolio, IBM is the only company providing a one-stop shop of AI-powered automation capabilities, all built on Red Hat OpenShift to run anywhere.

Jan-2020: Samsung acquired TeleWorld Solutions, a strategic wireless engineering and consulting firm. This acquisition aimed to allow Samsung to fulfill mobile carriers’ increasing requirements for enhancing their 4G and 5G networks, and eventually develop new opportunities to improve its service capabilities to the customers.

Business Expansion:

May-2021: Dell introduced the flagship Apex services brand. Under this expansion, Dell launched Apex Custom Solutions, Apex Data Storage Services, and the Apex Console. Apex Data Storage Services provides three performance tiers of the block along with file enterprise storage, and capacity starting as low as 50 terabytes.

Scope of the Study

Market Segments covered in the Report:

By Offering

• Hardware
• Server Software

By End User

• Cloud Service Providers
• Enterprises
• Government Organizations

By Deployment Type

• On-premise
• Hybrid
• Cloud

By Function

• Inference
• Training

By Technology

• Machine Learning
• Deep Learning

By Geography

• North America
• US
• Canada
• Mexico
• Rest of North America
• Europe
• Germany
• UK
• France
• Russia
• Spain
• Italy
• Rest of Europe
• Asia Pacific
• China
• Japan
• India
• South Korea
• Singapore
• Malaysia
• Rest of Asia Pacific
• LAMEA
• Brazil
• Argentina
• UAE
• Saudi Arabia
• South Africa
• Nigeria
• Rest of LAMEA

Companies Profiled

• Oracle Corporation
• Intel Corporation
• Samsung Electronics Co., Ltd. (Samsung Group)
• IBM Corporation
• Google LLC
• Amazon Web Services, Inc. (Amazon.com, Inc.)
• Hewlett-Packard Enterprise Company
• Cisco Systems, Inc.
• Dell Technologies, Inc.
• Toshiba Corporation

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