Choosing the Right Cloud Deployment Model for AI Transformation

Choosing the right cloud deployment model is critical to the success of your AI transformation. While this may not be your first consideration, having the right platforms is crucial. This article offers tech experts and business leaders key insights into selecting the best Hybrid Cloud for AI.

This article is part of the “CTO’s Guide to AI Transformation” series. Did you miss the first articles of the series? Read articles here.

The cloud has become essential for AI, offering scalability, flexibility, and access to powerful computing resources. However, choosing the right model from public, private, and hybrid cloud options can significantly impact the success of an AI initiative.

Why Hybrid Cloud for AI?

A hybrid cloud approach is often the most suitable solution for AI transformation. It combines the benefits of public and private cloud environments, allowing organisations to leverage the scalability and cost-effectiveness of the public cloud while maintaining control and security over sensitive data in a private cloud.  

Interestingly, many organisations are “hybrid by accident,” having transitioned to the cloud without closing their data centres. This often leads to modernised on-site infrastructure alongside cloud adoption, resulting in a hybrid environment. However, the current AI wave highlights the increasing need to become “hybrid by design.” In other words, this means planning where to handle specific workloads and why.

8 Considerations for Cloud Deployment Model Selection

When choosing a cloud deployment model for AI, several factors warrant careful consideration:

Data Security and Compliance in the Cloud

1. Data Privacy and Security: Data sensitivity in AI workloads is paramount. Private cloud environments offer greater control over data access and protection, making them suitable for handling sensitive information and complying with regulations like GDPR. While public cloud providers invest heavily in security measures, organisations dealing with highly confidential data may prefer the enhanced control of a private cloud. However, this requires investment to achieve the right security level. Investigating the current state of public cloud security is also advisable.
2. Compliance and Data Sovereignty: For AI applications involving cross-border data transfers or sensitive information subject to jurisdictional regulations, sovereign cloud solutions, whether through hyperscalers or local providers, are crucial. These private clouds are designed to meet data residency and compliance requirements within specific geographic boundaries. The key here is ensuring contractual coverage, as technically much can be achieved regardless of the provider.

Cost and Performance Optimisation

3. Cost Optimisation: Evaluating the long-term cost implications of different cloud models is essential. Public cloud can be cost-effective, but costs can escalate with extensive AI usage due to data transfer fees and API charges. Private cloud, especially combined with efficient resource management, may offer better cost predictability and potential savings at scale. Good design in service usage is key. A hybrid model can be very effective with initial data processing on the edge and combined datasets in a public cloud environment.
4. Performance and Latency: AI workload performance requirements vary. Private cloud allows for customisation and infrastructure optimisation, potentially leading to faster processing and lower latency. Public cloud performance can fluctuate depending on factors like network congestion and shared resources. Designing for different performance levels based on the best cost and performance ratio is crucial.

Scalability, Integration, and Long-Term Strategy

5. Scalability and Flexibility: Public cloud excels in providing rapid scalability and on-demand resources, making it suitable for fluctuating workloads and experimentation. Private cloud offers more control over infrastructure customisation, allowing for tailored solutions optimised for specific AI tasks.
6. AI Skillset and Resources: Public cloud providers offer pre-built AI models and services that are more accessible to organisations with limited in-house AI expertise. Private cloud requires more specialised skills for managing and maintaining infrastructure and models. However, the increasing ability to bring your own model to the cloud makes public cloud increasingly attractive, freeing up resources to focus on key priorities.
7. Integration with Existing Systems: Hybrid cloud models facilitate seamless integration between on-premises systems and cloud-based AI services, particularly important for organisations with legacy systems. While most use a hybrid model, seamless integration, especially for those “hybrid by accident,” requires good architecture and design.
8. Long-Term AI Strategy: The choice of cloud deployment model should align with the overall AI strategy and business objectives. Factors like AI initiative maturity, future scalability needs, and the desired balance between innovation and control should guide the decision. More information on this topic can be found in the 2nd article of the series.

The Benefits of a Hybrid Cloud Approach

A hybrid cloud approach offers the flexibility to leverage the strengths of both public and private cloud environments. For example, you can use the public cloud for tasks like AI model training and development, while sensitive data and critical AI applications reside in a secure private cloud. This approach allows for optimised cost, performance, and security based on specific AI workload requirements.

The key to reaping these benefits is to become hybrid by design, making solid architectural and design choices.

Partnering for Success

Organisations like Devoteam can help companies navigate the complexities of AI transformation. Our cloud technologies and AI expertise enable us to deliver tailored solutions that meet specific business needs and accelerate innovation.

For instance, our experts can guide clients in selecting the optimal cloud deployment model, ensuring secure, scalable, and cost-effective AI deployments.

Make the most of Hybrid Cloud

Choosing the right cloud deployment model is crucial for successful AI transformation. Organisations can make informed decisions that align with their AI strategy and business objectives by carefully considering factors like data privacy, cost optimisation, performance, scalability, and integration needs.

A hybrid cloud approach often provides the ideal balance between flexibility, control, and innovation. As a result, organisations can harness the power of AI while mitigating risks and maximising the value of their cloud investments.

This article is part of a blog series, “CTO’s Guide to AI Transformation”