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Artificial intelligence (AI) is becoming increasingly important as organizations across industries look to gain insights from their data and automate processes all while keeping pace with innovation. However, adopting AI can be challenging, especially for organizations that may lack the budget, resources and expertise to implement AI solutions on their own data center infrastructure. 

In general, AI models can be trained and deployed in three primary ways, depending on the specific requirements and resources available:

  • On-premises: Training and deploying AI models on-premises involves setting up the necessary infrastructure, such as servers, GPUs, storage and connectivity within an organization's own data center. This approach offers complete control over the hardware and data, ensuring data privacy and security.
  • Cloud: Training and deploying AI in the cloud involves leveraging cloud-based services and infrastructure from public cloud hyperscalers like Amazon Web Services (AWS), Microsoft Azure, Google Cloud and Oracle Cloud. Cloud-based AI solutions typically offer scalability, flexibility and ease of use thanks to their pre-configured environments and resources for training and deploying models.
  • Hybrid: In a hybrid model, organizations combine on-prem and cloud-based approaches. This allows them to leverage the benefits of both worlds, such as reserving on-premises infrastructure for sensitive data or specific workloads while leveraging the public cloud for its scalability and cost-effectiveness for less demanding workloads.

The choice of AI training and deployment method depends on factors such as data sensitivity, computational requirements, cost considerations and organizational preferences.

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Why cloud for AI? 

So, what makes cloud one of the more compelling options? There are a number of factors:


Cloud's agile nature allows organizations to quickly spin up the necessary infrastructure for model training, experimentation and testing. By significantly reducing the time required for provisioning and de-provisioning infrastructure, organizations can use cloud to supercharge the speed at which IT can deliver outcomes.


Scalability is another key benefit of cloud that shouldn't be overlooked. AI workloads often involve processing vast amounts of data and performing computationally intensive tasks. Additionally, training large neural networks requires access to thousands of CPUs and GPUs. Cloud allows organizations to seamlessly scale compute capacity based on real-time needs, which is crucial due to AI's unpredictable demands on infrastructure over time. 


Cost savings are also significant as building and maintaining on-premises AI infrastructure requires massive upfront investments and ongoing costs that are prohibitive for many organizations. Using cloud can shift these expenses to a more affordable "pay-as-you-go" model that charges only for the resources consumed. It also allows organizations to pause low-return AI experiments to prevent wasted spending. In other words, cloud lowers the barrier to experimenting with and adopting emerging AI technologies. 

ESG impact

Lastly, the scale and sustainability of ongoing cloud hyperscaler innovations can help lower the environmental costs associated with developing and deploying AI technologies. Whether via a public, private or hybrid deployment model, cloud computing is one of the most impactful investments a business can make to minimize its carbon footprint through the IT and operational efficiencies cloud can offer. 

Several key considerations should be discussed to effectively harness cloud services and solutions for AI. 

Securing AI in cloud infrastructure 

Security is critical when it comes to AI. The quality and integrity of the data fed into AI models directly impact their performance. While it is tempting to pull in as much data as possible, this approach is far from secure without the proper level of scrutiny. That's why some may argue that hosting servers on-premises provides the highest level of security and privacy. 

But this perspective doesn't necessarily hold true. 

In fact, the major cloud hyperscalers have massive budgets that enable them to invest in building hyper-secure environments. Their substantial resources, extensive experience and scalability also allow them to implement robust security measures, safeguarding both data and infrastructure. When safeguarding data and resources in shared cloud environments, organizations can leverage several security features and best practices, such as:   

Virtual private clouds

Hyperscalers provide virtual private clouds (VPCs) to prevent unauthorized access between organizations implementing AI solutions in the public cloud. VPCs allow customers to logically isolate their cloud resources within a cloud provider's infrastructure. One can think of VPCs as virtual data centers.

With VPCs, organizations can define virtual network perimeters and subnets, and control who and what gets access through granular access management rules. This virtual network segmentation separates an organization's workloads, databases, applications and other services from those of other customers hosted on the same physical hardware. 

Security automation

Hyperscalers operate under the principle of automation, where security measures remove the human factor — a critical vulnerability. Following cloud-first best practices allows organizations to create secure cloud environments with the necessary operational expertise for AI workloads. Properly built cloud environments avoid the trade-off between speed and security, enabling a focus on the business value of AI efforts.

Identity and access management

While cloud hyperscalers have extensive security controls and expertise, organizations are still responsible for properly securing their own workloads and data. To that end, organizations should carefully configure their identity and access management rules to restrict unauthorized access to AI projects. They also need to implement appropriate data governance policies to ensure sensitive training data and model outputs are kept private and not misused. 


Maintaining control and visibility over cloud resources through ongoing monitoring can help address AI security concerns in the cloud. With proper precautions and by leveraging cloud-native security features, organizations can reap the benefits of AI in the cloud while adequately addressing information assurance considerations.

Cloud AI services and tools 

Finding experts qualified to grow and optimize cloud capabilities as fast as the business requires has been a constant challenge for IT leaders. Because of this, a shortage of talent and skills within internal teams may pose a barrier to adopting AI. To help bridge this gap, cloud hyperscalers offer AI services and tools that can help IT teams effectively leverage cloud computing resources to carry out AI initiatives.

Through managed AI services, organizations can leverage the cloud providers' extensive engineering resources rather than staff expertise internally. Moreover, pre-built AI tools and no-code/low-code interfaces in the cloud lower technical requirements, empowering non-specialists to contribute to the development of AI solutions. 

Cloud hyperscalers and third parties also offer extensive online documentation, tutorials, certifications and user communities to upskill existing staff more efficiently compared to building custom training programs. Furthermore, the cloud's agile development practices and experimentation features reduce pressure to have a fully formed, large-standing AI team from the outset, allowing resources to grow over time as needs evolve. 

As hyperscalers continually develop and enhance their AI portfolios, IT leaders should take the time to explore each provider's suite of AI offerings to uncover potential solutions that can accelerate their AI initiatives by reducing the need for implementation work. One way to do so is to leverage WWT's cutting-edge AI Proving Ground, which provides leaders with a unique lab environment to explore the diverse AI services and offerings provided by hyperscalers. 

Maximize cloud investments 

Training complex machine learning models requires significant compute power from GPUs, CPUs and other infrastructure over extended periods of time. This level of resource usage can be costly if not properly managed. 

By adopting cloud FinOps fundamentals, organizations can better understand and manage their cloud usage and costs through cost optimization strategies such as monitoring spending trends, optimizing reservations for steady workloads and rightsizing resources as requirements change. Integrating AI into the FinOps practice can also enhance cost management and governance, allowing organizations to reinvest savings into refining AI solutions. 

Additionally, cloud provides the ability to free up cycles to proactively optimize spend and resource allocation through automation, not only for day-to-day operations but also for higher-level cost optimization and management practices. By leveraging these extra cycles gained from cloud's management of basic infrastructure, organizations can strengthen financial governance and extract additional value from cloud over time. 

Recommended webinar: WWT Experts | Optimizing Cloud Spend with FinOps

Proving AI's value

When it comes to assigning resources and budgets to execute AI initiatives, sometimes the biggest hurdle is the organization's decision-makers and key stakeholders. To convince those skeptical or fatigued by the non-stop onslaught of AI hype, cloud can help leaders better demonstrate the value of AI. 

Cloud's agile development environment supports rapidly building proofs-of-value to demonstrate potential applications and benefits to stakeholders, helping them visualize returns on investment more effectively than theoretical business cases. This allows for incremental development through smaller pilot projects rather than large multi-year budget requests to achieve quick wins without massive resource allocation upfront. 

IT leaders can also leverage cloud-native monitoring and reporting tools to provide measurable outcomes. By tracking metrics such as usage, costs and performance, regular reporting on tangible results helps prove the value proposition to stakeholders and justify further funding over time. 

Furthermore, cloud's flexible spending model can reduce risk by dynamically scaling budgets based on project needs as opposed to taking on fixed hardware costs from potential stranded on-premises assets. 

It's important to remember that aligning cloud resources with defined business needs will help accelerate the time-to-market for AI initiatives.

Recommended case study: Discover how Freeport-McMoRan partnered with WWT to better harness big data and unlock hidden productivity through solutions that tie into modern data science, AI and cloud.

How to get started

It's clear that the cloud offers an attractive environment to train and deploy AI models, helping organizations overcome barriers to costs, scaling and flexibility. However, navigating the nuances of cloud computing can be complex, especially in multifaceted IT environments.

WWT is one of the few partners that can help organizations at every step of the cloud journey and the AI journey.

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