Using Cloud Networking to Support AI Applications: Advantages and Considerations

Shaktiman Kumar Mall, Principal Product Manager at Aviatrix, explains some of the advantages and disadvantages of using cloud networking to support your AI applications. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The rapid growth and transformation of Artificial Intelligence (AI) has reshaped the way businesses approach data processing and storage. As AI systems evolve to handle more complex tasks and larger datasets, traditional methods of computation and storage have become increasingly inadequate and/or costly for most enterprises. In the past, many AI platforms relied on a single, unified infrastructure where data storage and computation occurred. While this configuration was fine for small-scale AI projects, it proved to be an often expensive bottleneck as the scale and complexity of modern AI projects surged. As a result, new solutions were needed to meet the rising demands of AI-driven enterprises. 

The architecture of data and AI platforms needed to change so that computation and storage were separate. Thus, companies decided to shift AI applications to the cloud to reap the infrastructure advantages such as improved efficiency, flexibility and scalability. Despite the benefits the cloud offers AI applications and tools, enterprises will encounter several integration challenges. 

The Challenges of Integrating AI with Existing Cloud Infrastructure 

Integrating AI tools into existing cloud infrastructure is not easy, and there are multiple factors businesses will need to consider, with one challenge often leading to another. Consider data; data enriches AI, but when data is spread across different places within an organization, it can be difficult to harness it effectively. Adequate data storage must exist so that AI applications can readily draw from static data and their own database of information. Nevertheless, data storage isn’t cheap, nor will it provide quality AI integration. 

Another notable challenge is dynamically scaling network bandwidth. When many employees use the same AI application, the network must scale to accommodate demand. If the bandwidth can’t scale, the network will become slow and possibly unusable. AI operations can also be CPU-intensive, further complicating scaling initiatives. 

Likewise, there is the issue of security. Enterprises must ensure the cloud infrastructure complies with the necessary standards and requirements relevant to their AI applications. Lastly, organizations may have employees resistant to change. Without the proper training and awareness, these employees won’t leverage AI solutions, regardless of how advanced (or expensive) they are. 

How Multi-cloud Networking Can Help Accommodate AI Applications 

Recognizing the challenges of integrating AI applications, many enterprises turned to the cloud to better support their growing AI needs. Some opted for a single cloud provider, while others embraced a multi-cloud strategy, using services from multiple cloud service providers (CSPs). Even though this approach introduces complexity, it offers flexibility by allowing organizations to tap into a range of specialized services from different CSPs. Others still opt for hybrid environments, keeping some of their data on-premises while moving others to the cloud, where it becomes challenging to have both high throughput and secure data transmission.  

That said, many cloud environments were developed in silos, which has led to technical challenges. Managing these environments requires specialized expertise and resources to ensure smooth integration across different platforms. While cost-efficiency is an important factor, businesses are also seeking the aforementioned agility and operational flexibility. When carefully implemented, multi-cloud strategies can help avoid vendor lock-in and offer companies greater control over their AI solutions. By spreading workloads across CSPs, organizations reduce the risk of dependency on a single provider and gain leverage in negotiations. This must be balanced with the need for seamless operations and reliability. Matching specific AI workloads to the most appropriate infrastructure can enhance performance and cost management. 

Achieving Greater Visibility and Control  

While cloud strategies offer numerous advantages for AI deployments, they also introduce significant complexity. One key challenge is managing the intricate networks that span different cloud environments. Although cloud infrastructure provides greater flexibility and scalability, it often comes at the cost of visibility and control. 

Fragmented control mechanisms refer to the difficulty of managing different cloud environments simultaneously. Each cloud provider has its own set of tools, interfaces, and protocols, which makes it challenging for IT teams to monitor and control AI applications across multiple platforms. Without a cohesive management approach, optimizing the performance of AI applications and maintaining operational efficiency becomes an uphill task. 

In a traditional on-premise setup, IT teams can monitor and manage their networks directly. However, this visibility is often reduced in the cloud—especially when using multiple providers. This fragmentation means that organizations may struggle to gain a clear, unified view of their network performance, making it difficult to detect real-time issues like latency or misconfigurations. 

To address this, enterprises can benefit from deploying a topology platform in their cloud architecture. In cloud computing, a topology platform is a tool that provides a unified control plane, giving IT teams a centralized view of their entire cloud network. This platform maps out the connections and data flow between various cloud services, offering real-time insights into network performance, such as latency and throughput, so businesses can quickly identify and troubleshoot issues like network bottlenecks, configuration errors, or connectivity problems. For example, suppose there’s a latency issue affecting an AI application. In that case, IT teams can immediately pinpoint the source, whether a specific cloud resource or a network misconfiguration, enabling faster resolution and minimizing downtime. 

Security Considerations 

Ensuring data security and compliance across multiple cloud providers when integrating AI tools can be tricky. Consider AWS and its AI engine, Amazon Bedrock, which have different security requirements from Azure and Microsoft Copilot. Thankfully, a topology mapping platform can rectify this conundrum by allowing users to create an orchestration layer and automatically align security and network requirements to the CSP, irrespective of application programming interfaces or underlying architecture. Such a platform can also provide security visualization components for real-time troubleshooting of AI applications while enhancing security and vulnerability protection. 

In addition to a topology platform, businesses should consider implementing a distributed cloud firewall (DCF). Note that AI engines are inside a public subnet, meaning they can access the internet, exposing them to cyber-attacks. A DCF can sit on a public and private subnet to enable greater security for these AI engines. Moreover, a quality DCF will add centralized policy management and distributed enforcement points across different regions and CSPs, between data centers and CSPs, and even between a random site and the cloud. 

The Intersection of AI, Cloud, and Security 

The shift toward multi-cloud and the larger intersection between AI, cloud, and security will bring incredible cost and efficiency benefits. Although integrating these complex technologies poses various challenges, having the right solutions, such as a topology mapping platform and DCF, will empower enterprises to become more agile, automated, and resilient.