AI Infrastructure Market Report scope & Overview:

AI Infrastructure Market Size was valued at USD 36.14 billion in 2022 and is expected to reach USD 222.42 billion by 2030, and grow at a CAGR of 25.5% over the forecast period 2023-2030.

AI infrastructure refers to the platforms on which organisations can build intelligent applications that are predictive, self-healing, and require minimal human intervention. New age technologies such as IoT, Mobility, and Big Data are putting a strain on IT infrastructure. The need for intelligent infrastructure to harness the power of AI platforms is more apparent than ever.

AI Infrastructure Market Revenue Analysis

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Every stage of the machine learning workflow is covered by AI infrastructure. It enables data engineers, data scientists, DevOps teams, and software engineers to manage and access computing resources for training, deploying, and testing artificial intelligence algorithms. Using AI infrastructure, the workload is mapped to the appropriate configuration of virtual machines and servers. Organizations can use AI infrastructure to work on capacity planning, storage management, resource utilisation, anomaly detection, threat detection, and analysis.



  • Cloud machine learning platform adoption is increasing.

  • Increasing data traffic necessitates the use of powerful computing resources.

  • Increasing cross-industry collaborations and partnerships.


  • A scarcity of AI hardware experts and skilled workforce.


  • AI-based tools for elderly care have a growing potential.

  • FPGA-based accelerators are in high demand.


  • AI algorithm unreliability

  • Concerns about data privacy in AI platforms.


During the COVID-19 pandemic, digital transformation using artificial intelligence and hybrid cloud is widely used. During quarantine, a new internet infrastructure procedure was critical in supporting retail supply chains. Companies are shifting their investments in order to capitalise on the opportunity provided by new infrastructure, and they are being flexible and open-minded in their approach. The pandemic is expected to have a negative impact on the global AI Infrastructure market overall.

Based on Offering, the AI infrastructure market is segmented into Hardware, and Software. To keep up with the increasing amount of data generated by applications, advanced AI solutions require new software and hardware on a regular basis. These AI-based solutions, for example, require updates around annotation and collation of data sources, as well as accessible creating, processing, and fine-tuning models as newer data becomes available. AI technology, particularly deep learning, has become one of the most important computational workloads for organisations, and its use will increase.

Based on Deployment, the AI infrastructure market is segmented into On-Premises, Cloud, and Hybrid. During the forecast period, the hybrid deployment model has the second largest market share in the AI infrastructure market. Because of the increased agility of a hybrid cloud, it is widely accepted by enterprises seeking a competitive advantage. Organizations in the automotive, healthcare, and industrial sectors have begun to use hybrid infrastructure, which combines various technologies and methodologies such as virtualization, private clouds, and other internal IT resources.

Based on Technology, the AI infrastructure market is segmented into Deep Learning, and Machine Learning. Based on function, the AI infrastructure market is segmented into Inference, and Training. Based on end-user, the AI infrastructure market is segmented into Government Organizations, Cloud Service Providers, and Enterprises.



  • Hardware

  • Software


  • Deep Learning

  • Machine Learning


  • On-premises

  • Cloud

  • Hybrid


  • Inference

  • Training


  • Government Organizations

  • Cloud Service Providers

  • Enterprises

AI Infrastructure Market Segmentation Analysis

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The Asia Pacific market has the largest market share and the highest growth rate, and it is expected to maintain its position during the forecast period. The presence of the most populous countries, such as China and India, accounts for the rapid growth.

China's market has the largest market share and the highest growth rate among APAC countries, and it is expected to maintain its position during the forecast period. China's AI infrastructure market is rapidly expanding. The growth of AI data centres in China continues to evolve as multinational and domestic enterprises increasingly shift to cloud service providers (CSPs) and co-location solutions. The country's demand for AI data centres has increased due to organisations seeking improved connectivity and scalable solutions for their expanding businesses.

India is one of the world's fastest-growing economies, with a keen interest in the global development of artificial intelligence. The Indian government recognises the potential and is taking all necessary steps to steer the country and position it among the leaders in artificial intelligence. Despite a favourable ecosystem, the government is attempting to overcome obstacles in order to achieve rapid progress in AI. Similarly, the Chinese government is hastening the construction of new infrastructure projects such as 5G networks and data centres, which will improve information services for the rapidly expanding market.


  • North America

    • USA

    • Canada

    • Mexico

  • Europe

    • Germany

    • UK

    • France

    • Italy

    • Spain

    • The Netherlands

    • Rest of Europe

  • Asia-Pacific

    • Japan

    • south Korea

    • China

    • India

    • Australia

    • Rest of Asia-Pacific

  • The Middle East & Africa

    • Israel

    • UAE

    • South Africa

    • Rest of Middle East & Africa

  • Latin America

    • Brazil

    • Argentina

    • Rest of Latin America


The key players in the AI infrastructure market are Amazon Web Services, Google LLC, IBM Corporation, Microsoft Corporation, Oracle Corporation, Cisco Systems Inc., Intel Corporation, Micron Technology Inc., Nvidia Corporation and Samsung Electronics.

Google LLC-Company Financial Analysis

Company Landscape Analysis

AI Infrastructure Market Report Scope:
Report Attributes Details
Market Size in 2022 US$ 36.14 Billion
Market Size by 2030 US$ 222.42 Billion
CAGR CAGR of 25.5% From 2023 to 2030
Base Year 2022
Forecast Period 2023-2030
Historical Data 2020-2021
Report Scope & Coverage Market Size, Segments Analysis, Competitive  Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook
Key Segments • By Product (Lighting Controls, Hvac Controls, Surveillance Products, Access Controls)
• By Standard (Wi Fi And Infrared, En Ocean, Bac Net, Z Wave, Zigbee, Dali, Knx)
Regional Analysis/Coverage North America (USA, Canada, Mexico), Europe
(Germany, UK, France, Italy, Spain, Netherlands,
Rest of Europe), Asia-Pacific (Japan, South Korea,
China, India, Australia, Rest of Asia-Pacific), The
Middle East & Africa (Israel, UAE, South Africa,
Rest of Middle East & Africa), Latin America (Brazil, Argentina, Rest of Latin America)
Company Profiles Amazon Web Services, Google LLC, IBM Corporation, Microsoft Corporation, Oracle Corporation, Cisco Systems Inc., Intel Corporation, Micron Technology Inc., Nvidia Corporation and Samsung Electronics.
Key Drivers • Cloud machine learning platform adoption is increasing.
• Increasing data traffic necessitates the use of powerful computing resources.
RESTRAINTS • A scarcity of AI hardware experts and skilled workforce.

Frequently Asked Questions

The market value is expected to reach USD 222.42 billion by 2030.

The market has been segmented with respect to offering, technology, deployment, function and end-user.

Asian pacific region is expected to dominate the AI infrastructure Market.

Yes, and they are Raw material vendors, Distributors/traders/wholesalers/suppliers, Regulatory authorities, including government agencies and NGO, Commercial research & development (R&D) institutions, Importers and exporters, Government organizations, research organizations, and consulting firms, Trade/Industrial associations, End-use industries.

Manufacturers, Consultants, Association, Research Institutes, private and university libraries, suppliers, and distributors of the product.

Table of Content:

1. Introduction

1.1 Market Definition

1.2 Scope

1.3 Research Assumptions

2. Research Methodology

3. Market Dynamics

3.1 Drivers

3.2 Restraints

3.3 Opportunities

3.4 Challenges

4. Impact Analysis

4.1 COVID-19 Impact Analysis

4.2 Impact of Ukraine- Russia war

4.3 Impact of ongoing Recession

4.3.1 Introduction

4.3.2 Impact on major economies US Canada Germany France United Kingdom China Japan South Korea Rest of the World

5. Value Chain Analysis

6. Porter’s 5 forces model

7.  PEST Analysis


8. AI infrastructure Market Segmentation, by offering


8.2 Hardware

8.3 Software

9. AI infrastructure Market Segmentation, by equipment technology


9.2 Deep Learning

9.3 Machine Learning

10. AI infrastructure Market Segmentation, by deployment

10.1 Introduction

10.2 On-premises

10.3 Cloud

10.4 Hybrid

11. AI infrastructure Market Segmentation, by function

11.1 Introduction

11.2 Inference

11.3 Training

12. AI infrastructure Market Segmentation, by end-user

12.1 Introduction

12.2 Government Organizations

12.3 Cloud Service Providers

12.4 Enterprises

13. Regional Analysis

13.1 Introduction

13.2 North America

13.2.1 USA

13.2.2 Canada

13.2.3 Mexico

13.3 Europe

13.3.1 Germany

13.3.2 UK

13.3.3 France

13.3.4 Italy

13.3.5 Spain

13.3.6 The Netherlands

13.3.7 Rest of Europe

13.4 Asia-Pacific

13.4.1 Japan

13.4.2 South Korea

13.4.3 China

13.4.4 India

13.4.5 Australia

13.4.6 Rest of Asia-Pacific

13.5 The Middle East & Africa

13.5.1 Israel

13.5.2 UAE

13.5.3 South Africa

13.5.4 Rest

13.6 Latin America

13.6.1 Brazil

13.6.2 Argentina

13.6.3 Rest of Latin America

14. Company Profiles

14.1 IBM Corporation

14.1.1 Financial

14.1.2 Products/ Services Offered

14.1.3 SWOT Analysis

14.1.4 The SNS view

14.2 Amazon Web Services

14.3 Google LLC

14.4 Microsoft Corporation

14.5 Oracle Corporation

14.6 Cisco Systems Inc.

14.7 Intel Corporation

14.8 Micron Technology Inc.

14.9 Nvidia Corporation

14.10 Samsung Electronics

15. Competitive Landscape

15.1 Competitive Benchmark

15.2 Market Share analysis

15.3 Recent Developments

16. Conclusion

An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.

Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.


The 5 steps process:

Step 1: Secondary Research:

Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.

Secondary Research

Step 2: Primary Research

When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data.  This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.

We at SNS Insider have divided Primary Research into 2 parts.

Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.

This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.

Primary Research

Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.

Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.

Step 3: Data Bank Validation

Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.

Data Bank Validation

Step 4: QA/QC Process

After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.

Step 5: Final QC/QA Process:

This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.

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