[S1.07] AI Infrastructure as a Service

“Infrastructure creates the form of a city and enables life to go on in a city, in a certain way.”
— Paul Goldberger, an American author, architecture critic and lecturer, widely known as contributing editor at Vanity Fair, architectural critic for the New York Times and columnist of Sky Line for The New Yorker

In real life, the infrastructure is both the backbone and blood vessel of each city, and each community. This definition is true for AI Infrastructure. Millions of AI platforms nowadays are running and operating successfully thanks to the great AI Infrastructure as a Service.

This topic is the 7th topic of our series of AI Business Models, with a detailed guide and explanation.

AI Business Model Blog Series

After years of serving our clients in the AI domain, we are impressed with many different AI approaches to products/services from our clients. So we would love to classify them as a Blog Series for AI Business Models, our 1st Blog Series (S1) to bring a summary on this topic and share it with any AI founders, or entrepreneurs out there who dream of building their own AI kingdom. Some may already having their business but they can also refer on market research to grow their AI business.
Series name: AI Business Models, a detail guide and explanation
Series blogs:
- S1.01: AI Business Models Overview
- S1.02: AI Education Business Model
- S1.03: AI Consultant Business Model
- S1.04: AI Development Agency Business Model
- S1.05: AI Software as a Service (AI SaaS)
- S1.06: AI Platform as a Service (AI PaaS)
- S1.07: AI Infrastructure as a Service (AI IaaS) (this blog)
- S1.08: AI Business Models transition possibilities

NVIDIA to the top of the world

That is incredible, according to what we can say about NVIDIA's stock pricing recently. Statistics show that it takes this company 30 years, from 1993 to mid-2023, to be evaluated (market cap) of $1,000,000,000,000, one trillion USD. But with the rise of AI, in just need 08 more months, from mid-2023 to Q1.2024, NVIDIA will double its market cap to be evaluated at $2 trillion USD.

NVIDIA is the best example of AI IaaS, the core chip technology from this company is empowering millions of AI Models, bringing a great foundation of everything needed for AI companies to grow.

AI IaaS components

AI IaaS contains 02 main components: Hardware & Software

The hardware segment dominated the global Artificial Intelligence (AI) infrastructure market in 2023.

GPUs are essential to artificial intelligence (AI) infrastructure because of their parallel processing power, which makes operations like AI model inference and training faster. This market is dominated by top GPU manufacturers like NVIDIA, whose Tesla and Quadro series are made especially for AI workloads. FPGAs provide flexible and performant programmable hardware that can be tailored for certain AI workloads. Prominent participants in this space include Intel (with its Intel Arria and Stratix series) and Xilinx.

CPUs are still necessary for general-purpose computing and are frequently used in artificial intelligence (AI) infrastructure, especially for preprocessing and post-processing operations, even if they are not as specialized for AI tasks as GPUs or TPUs are.

Similar to CPUs in the Cloud trending, now GPUs are in the Cloud with a lot of options, and choices from many different providers.

The AI software segment is projected to expand substantially during the forecast period. A cloud-based platform called NVIDIA's NGC offers GPU-optimized software containers for high-performance computing, machine learning, and deep learning. It consists of pre-trained models, optimized libraries, and frameworks such as TensorFlow, PyTorch, and MXNet.

Workflows for data science, data engineering, and machine learning are made possible by Databricks' Unified Data Analytics Platform, which is based on Apache Spark. Integrations with well-known AI frameworks are provided, and MLflow is included to handle the entire machine learning lifecycle.

Scalable and distributed machine learning is the focus of H2O.ai's open-source H2O.ai machine learning platform. It has autonomous AI for automated feature engineering and model creation, as well as AutoML features for automating model selection and hyperparameter tuning.

Highlight on market

The global artificial intelligence (AI) infrastructure market size was valued at $47.23B in 2024 and is anticipated to reach around $421B by 2033, growing at a CAGR of 27.53% from 2024 to 2033. The market estimated at 2030 is valued at $203B, 10 times bigger than AI PaaS but equal to 13% compared to AI SaaS.

Sharing between Hardware and Software, they both grow equality and maintain the share of 2/3 for Hardware and 1/3 for Software. An increasing amount of strong artificial intelligence (AI) infrastructure is needed to serve these applications as companies in a variety of industries realize how AI can boost productivity, creativity, and competitive advantage.

The statistics also show the 2nd market share belongs to Asia Pacific, also the fastest growth market. As there is a huge need for infrastructure, especially in Asia, we have no doubt that Asia Pacific market will continue growing and may lead the market in the near future.

AI cloud chips are led by USA and China, there are not many data statistics from China, but seems that China is now leading the world in AI Chip, powered by their manufacturing capacity.

Sometimes AI IaaS and PaaS are mixed where the 04 tech giants provide a lot of IaaS and PaaS in their list of solutions. This is still a new market for startups and SMEs, especially for GPUs so we need more time to reassess the success stories.

Action Recommendation

With the participants and domination of the 04 tech giants, AI IaaS is the most challenging market among the 06 Business Models presented in our series. But this is not meaning impossible.

• Condition to start: select your AI IaaS components: Hardware or Software. For startups, SMEs, we don't think Hardware is an option here because it requires a lot of investment and key secret on-chip technology. So Software is more the option in this case where we can focus more on providing Cloud services with good strategy.
• Condition to scale: a good network and sustainable, strong quality standard with be the condition to scale. As this is purely B2B business it's very important to maintain your retention and optimize it to 99% if possible.
• Condition to success: data privacy will be the shield of your business. So the speed is super important to success. It will take time for the tech giants to set up Data Centers or businesses in your region, so you can profit this time to dominate your market. M&A is also considered a great success here where your company can be acquired by a very large tech company in this business.
• Skill needs: a deep understanding of AI infrastructure technologies, this is still new and frequently updated.

Ready to start your own AI IaaS Business? From Investidea, we would love to talk more with other AI IaaS firms to open an AI Alliance. Together, we can fulfill the diversification of technologies, domains, and user cases, which can help us to grow together, and serve our clients better.