AI is shaping each Industry with new functionalities and capabilities. Cloud providers are uniquely positioned in this space to get triple the benefit:

1. Provide cloud computing to meet the increasing demand for AI.
2. Enhance internal operational efficiency with AI.
3. Offer AI services in the cloud.

In this article, we will discuss the benefits of AI for cloud computing platforms. We will explore how cloud providers can use AI to enhance their operations. We will also cover how they can quickly enhance their offerings by providing AI in the cloud as an additional service.

What is AI?

Artificial intelligence, or AI, refers to computer systems that have capabilities close to human intelligence in understanding and creating text, images, and videos. In some ways, it even outpaces human intelligence, especially in tasks involving large data sets, complex calculations, or anomaly detection, where high precision is crucial, according to Oracle (The Role and Benefits of AI in Cloud Computing, Jeffrey Erickson, Oracle, 2024)

This new technology, which comprises machine learning, natural language processing (NLP), generative AI, and many other fields, promises to solve many real-life business problems and challenges that no other technology has been capable of solving. While the true value is yet to be discovered, AI is already creating value in customer service, operational efficiency, and stronger cybersecurity in cloud computing. AI-powered systems are enhancing operational efficiency by automating processes and providing smarter insights for decision-making (HashStudioz), and improving cybersecurity by detecting and responding to threats in real-time (TechTarget, 2024, AdNovum, 2024).

To fully maximize the benefits of AI, many businesses are looking for ways to integrate this new technology into their own tools and products. Cloud providers are in a perfect place to bridge that gap by providing AI services in their cloud platforms while increasingly using AI to make their own operations more efficient, easing this transition.

What is Cloud Computing?

Cloud computing is a GPU hardware and infrastructure service offered by cloud providers. It means that companies don’t have to purchase their own hardware facilities and hire professionals for maintenance but can instead acquire computing power from a third-party provider of these services. In simple terms, cloud computing providers offer all the computing requirements businesses need with on-demand availability and simple scalability to scale up or down when demand increases or decreases.

Cloud computing providers offer a complete package that includes server maintenance, hardware like servers and storage, connections to databases, and many additional services that make the cloud compelling for businesses. Nowadays, their offerings also include AI and generative AI applications and services, allowing businesses to integrate AI into their operations without having to move their data away from their cloud provider. For example, cloud providers can offer AI capabilities such as semantic search and document summarization without requiring companies to migrate their data elsewhere.

The Synergy of AI and Cloud Computing:

There has been a massive transition from on-premises to cloud computing in the past decade, and for good reason: businesses want a simpler, more secure way to store their data without added complexity, and the cloud offers that. While some companies have transitioned back to on-premises, cloud computing powered by AI demand is still growing at massive rate.

Market Size of $647 billion

According to Grand View Research estimates the global Cloud AI market size to reach $647.60 billion by 2030, growing at a CAGR of 39.7% from 2025 to 2030.

Key drivers for this growth:

1. AI explosion – Particularly generative AI growth which begin with launch of chatGPT by OpenAI. Now businesses seeking to use generative AI systems to reduce operational costs and increase revenue.
2. Transition to Cloud – In the last decade, there has been a massive transition from on-prem to cloud for businesses. Now, with AI and limited computing capacity, there is even larger demand for AI in the cloud.
3. Data generation –  Each day, more data is generated and collected by businesses from various sources: emails, social media, ERPs, CMS. By 2030, it is projected that 660 zettabytes of data will be generated worldwide every day (UBS, 2023). Its like downloading 50 million high-definition movies every second.

Challenges of AI for cloud computing platforms

Each new technology creates challenges, and AI, specifically generative AI, is no different. Here are three main challenges of AI for cloud computing providers:

• Data Privacy – ​The biggest concern with cloud-based AI solutions is the possibility of data leakage into AI models. Businesses are increasingly opting for private cloud providers who utilize open-source LLM models to guarantee data protection. McKinsey notes that privacy concerns, particularly with private AI solutions, have increased significantly in the past few years (McKinsey & Company, 2023).
• Implementation Cost & Time – Building secure AI infrastructure requires a range of highly skilled professionals. It can take up to 2 years and cost millions, as reported by Markku Räsänen, CEO of ConfidentialMind. However, businesses need to move fast, as delaying AI adoption risks falling behind competitors who adopt AI early.​
• Integration Challenges – Many cloud providers face difficulties integrating AI into their current systems, especially considering the numerous components that need to work seamlessly together for AI systems to scale effectively.

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Benefits of AI in Cloud Computing:

Breaking through the above-mentioned challenges provides many benefits for AI-powered cloud computing providers, some of which are:

1. Enhancing Data Center Operations

Predictive Maintenance – AI can find potential infrastructure and hardware issues before they cause serious problems. For example, machine learning algorithms can analyze big data in real time to detect anomalies and predict potential failures, reducing costs and enhancing efficiency (Cyber Defense Advisors).

Energy Optimization – AI can monitor energy consumption patterns, optimize resource allocation, and implement predictive maintenance strategies, leading to reduced energy waste and operational costs. This approach aligns with sustainability goals by minimizing the carbon footprint of cloud operations (Digital Realty).

Workload Management - Implementing AI workload management strategies helps in optimizing efficiency, reducing latency, and ensuring scalability and flexibility in deploying AI applications (Hyperstack).

Intelligent Cooling – AI can help reduce energy consumption by optimizing cooling based on real-time needs. Advanced cooling technologies, such as liquid cooling, are essential to manage the intense heat generated by AI computing resources. Liquid cooling is emerging as a vital technology for AI workloads, optimizing performance, energy efficiency, and hardware reliability in AI-driven data center environments (Airsys North America, Datacenter Frontier).

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2. Offering AI Functionalities in the Cloud as an Added Service

As AI enhances operations for your cloud operations, it does the same for clients internal processes and products, businesses who store data in your cloud. According to the State of AI in the Nordics by AI Finland and Silo AI, businesses believe they will see the most value of generative AI when they integrate it into their products and services. For cloud computing platforms, this means integrating AI capabilities into their offerings through software as additional services (SaaS).

It’s important because there is such high demand for AI features that companies are even willing to change providers to access them. That leaves cloud providers with only one option: adopt AI as quickly as possible to avoid losing ground.

3. Providing Cloud GPU Hardware

The growth of Nvidia and their valuation reaching $3.28 trillion in 2024, fueled by AI, further continues to show the demand and market expansion (Reuters, 2024). This is mainly fueled by the demand for AI chips to run and train AI models, build AI-powered applications, or modernize applications with AI features. Cloud providers can invest in Nvidia's H1 and H2 chips and offer specialized services to attract businesses seeking cost-effective and scalable AI solutions in the cloud.

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Here Are Three Reasons Why AI in the Cloud Is Compelling for Businesses

1. Fear of data leakage: Many businesses have chosen public or private cloud solutions for their data storage. Now, with AI, they don’t want to move data away from these environments for security reasons. Instead, they seek a solution that brings AI to where the data is.
2. GPU Limitation: Businesses are often faced with high and expensive upfront costs to acquire hardware, as well as ongoing expenses for storing and managing it. The cloud provides an ideal solution, especially when they want to test AI to ensure its benefits.
3. Simple scalability: AI workloads require varying amounts of computing power depending on the tasks being performed. This can be challenging to maintain, especially since they may need significant more resources when testing new systems. Cloud computing offers simple scalability and flexible resource allocation based on demand.

A Real-Life Case Study of Implementing AI in a Cloud Platform

This case study is about Elastx, a Swedish cloud provider that operates a Kubernetes-based cloud platform for local Swedish SaaS companies.

Problem

Their team identified a growing demand for AI among their clients. Specifically, customers were asking for a more efficient way to index documents to improve search accuracy. Upon research, they realized that retrieval-augmented generation (RAG) could meet these needs by enhancing search capabilities with AI. However, they also recognized that building a complete RAG stack internally would require significant time and budget, particularly ensuring secure connectivity and data privacy within their cloud.

Solution

After meeting with ConfidentialMind and witnessing a demonstration of its AI platform, their leaders realized this was the solution they needed. Many features on their wishlist, such as deploying shared LLMs, client-specific RAGs, and APIs, were already part of the ConfidentialMind platform. This led to a partnership, allowing them to quickly integrate AI features into their Kubernetes cloud platform. The new AI-powered cloud platform now enables their clients to handle document chunks provided as plain text with metadata, with support for shared models across multiple accounts. Other use cases are in the pipeline.

Impact

The impact was immediate. Within just a few weeks, their first customer was utilizing RAG-based search with segregated databases, significantly improving search accuracy and reducing time spent on document indexing.

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Future of AI and cloud computing

Artificial intelligence is being adopted across many business divisions, and this trend is expected to continue. Soon, AI is expected to enhance all processes across industries. Cloud computing providers will play a crucial role in this AI revolution by offering the hardware and infrastructure to support this growth.

For cloud providers, we are already witnessing a shift from using AI merely to optimize internal operations to delivering AI-powered services that empower external business use cases.

In the future, autonomous robots, drones, bots, and AI agents will most likely rely on cloud computing for storing data and making decisions from it.

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How ConfidentialMind Helps Cloud Providers Capture the $647 Billion Opportunity

When you need to implement AI in your cloud computing platform quickly and securely, consider the ConfidentialMind AI platform. Its Kubernetes-based infrastructure easily integrates with any existing cloud computing environment, as demonstrated by the case study with Elastx. The AI platform offers three core benefits for cloud providers:

• Fastest Time to Market – The platform allows you to deploy AI systems in your environment almost instantly. Whether it's deploying large language models (LLMs) or building end-to-end applications, ConfidentialMind reduces the time to market for AI applications from months to minutes.
• Cost Savings – Since the platform delivers a comprehensive AI infrastructure, you can receive it at very affordable pricing. By optimizing inference and model management, the platform can cut AI implementation costs by up to 50-70%.
• Data Protection – ConfidentialMind is built using industry-leading open-source projects, allowing it to integrate into your cloud platform with no external connections, providing a completely closed-box solution. This ensures that your clients’ data remains secure and, combined with the ability to run AI models where the data resides (within your cloud), provides the highest level of data protection.

AI will continue to reshape industries such as healthcare, finance, manufacturing, government, and software development. Cloud providers that embrace AI will benefit significantly from the growing demand. If you feel the time is now to integrate AI into your cloud computing platform to meet the increasing demand for AI-driven solutions and need help, reach out to us and we will help with the journey.

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References

HashStudioz. (2024, July 15). AI in Cloud Computing: Enhancing Scalability, Efficiency & Security for Modern Enterprises. Retrieved from https://www.hashstudioz.com/blog/ai-in-cloud-computing-enhancing-scalability-efficiency-and-security-for-modern-enterprises/

AdNovum. (2024, June 10). AI in Cloud Security: Revolutionizing Defense Against Cyber Threats. Retrieved from https://www.adnovum.com/blog/ai-in-cloud-security-outsmarting-hackers-and-fortifying-the-cloud

TechTarget. (2024, September 17). Understanding the Role of AI in Cloud Computing. Retrieved from https://www.techtarget.com/searchcloudcomputing/tip/Understanding-the-role-of-AI-in-cloud-computing

Oracle. (n.d.). Artificial Intelligence and Cloud Computing. Retrieved from https://www.oracle.com/fi/artificial-intelligence/ai-cloud-computing/

Google Cloud. (n.d.). What is Cloud Computing? Retrieved from https://cloud.google.com/learn/what-is-cloud-computing?hl=en

Grand View Research. (2023, January 2). Global Cloud AI Market Size, Share & Trends Analysis Report By Application, By End Use, By Region, And Segment Forecasts, 2023 - 2030. Retrieved from https://www.grandviewresearch.com/press-release/global-cloud-ai-market

UBS. (2023). The global datasphere and the role of cloud infrastructure. Retrieved from https://www.ubs.com/us/en/wealth-management/insights/market-news/article/_jcr_content.0116445243.file/PS9jb250ZW50L2RhbS9pbXBvcnRlZC9jaW9yZXNlYXJjaC9wZGYvMTUvOTYvMzIvOS8xNTk2MzI5L2VuL3VzLzE1OTYzMjkucGRm

Reuters. (2025, January 2). Artificial Intelligence Market to Reach $X Trillion by 2025, Report Says. Retrieved from https://www.reuters.com/technology/artificial-intelligence/global-markets-marketcap-pix-2025-01-02/

McKinsey & Company. (2023). The growing importance of data privacy in AI. Retrieved from https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-growing-importance-of-data-privacy-in-ai

Cyber Defense Advisors. (2023). AI-Driven Predictive Maintenance: The Future of Data Center Reliability. Retrieved from https://cyberdefenseadvisors.com/ai-driven-predictive-maintenance-the-future-of-data-center-reliability/

Hyperstack. (2024). AI Workload Management in Data Centres: Learn How to Optimise Efficiency. Retrieved from https://www.hyperstack.cloud/blog/case-study/ai-workload-management-in-data-centres

Digital Realty. (2024). Energy Efficiency Using AI for Sustainable Data Centres. Retrieved from https://www.digitalrealty.co.uk/resources/articles/sustainable-data-centre-ai

AIRSYS. (2025). Understanding the Design & Cooling of AI Data Centers. Retrieved from https://airsysnorthamerica.com/understanding-the-design-cooling-of-ai-data-centers/

Iceotope. (2024). Cooling the AI Revolution in Data Centers. Retrieved from https://www.datacenterfrontier.com/sponsored/article/55040910/iceotope-cooling-the-ai-revolution-in-data-centers‍

AI Finland & Silo AI. (2025). NordicState of AI. Retrieved from https://aifinland.fi/wp-content/uploads/2025/03/Silo_NSOAI4_INLAY_update_spreads.pdf