A Guide for IT Leaders

By Richard Sonnen, Sentinel Senior Enterprise Architect and Innovation

Artificial Intelligence (AI) is rapidly transforming the business landscape, and its impact on cloud strategies is profound. For small and medium-sized businesses (SMBs), understanding the interplay between AI, cloud computing, and hybrid infrastructure is crucial for staying competitive and innovative. This article explores how AI should influence your organization’s strategies and provides practical guidance for IT leaders navigating this evolving landscape.

It's Raining AI

Many organizations have embraced a cloud-first mindset for their business-critical workloads.  After all, the cloud means best-of-breed reliability, unlimited scalability, and no more hardware refreshes.

However, the advent of AI has introduced new considerations for infrastructure decisions. AI workloads, characterized by their demand for substantial computational resources and data processing capabilities, are reshaping how businesses approach cloud adoption. IT leaders must recognize that AI is not just a technological advancement but a strategic shift that affects infrastructure decisions.

AI development often requires scalable and flexible resources, which cloud platforms are well-equipped to provide. The ability to scale resources up or down based on demand allows organizations to optimize costs and performance, making the cloud an attractive option for AI prototyping and development.  Furthermore, many early AI services have been developed with the intent of operating within SaaS and/or cloud-native environments. 

As AI moves deeper into the enterprise however, the total cost of ownership of AI-in-the-cloud is causing many organizations to revisit their workload placement assumptions.

AI and Cloud: A Perfect Match?

The relationship between AI and cloud is symbiotic. Cloud platforms offer the scalability, flexibility, and advanced tools necessary for the rapid AI development and deployment cycles which have been the hallmark of the AI tidal wave. For SMBs, leveraging cloud services can accelerate AI innovation without the need for significant upfront investments in hardware and infrastructure.

Cloud providers offer a range of AI services and tools, such as pre-built machine learning models and data analytics platforms, which can expedite the development process. These tools enable organizations to experiment, iterate, and deploy AI solutions more efficiently, driving innovation and competitiveness.

If you’re going to try out AI, chances are the cloud is where you should do it.

Storms are Brewing

While the cloud offers numerous benefits for AI development, it is not always the most cost-effective solution for all stages of the AI lifecycle. High (and unpredictable) IaaS bills are causing many organizations to either abandon or re-think their AI projects.

For stable, long-term AI workloads, on-premises solutions can be more cost-effective. The initial investment in hardware may be higher, but the total cost of ownership can be lower over time compared to ongoing cloud expenses.

Most organizations should adopt a hybrid approach, balancing cloud and on-premises solutions based on AI workload characteristics. This strategy allows for cost optimization while maintaining the agility needed for AI innovation.

Build in the cloud, deploy on the ground.

Data Gravity and AI Deployments

Another factor that should influence your AI workload placement is data gravity - the idea that data attracts more data and services. The location of data can significantly impact the performance and cost of AI solutions. Organizations must consider data locality when deciding between cloud and on-premises deployments both at a strategic and tactical level.

If the data (or the data source) is already on prem, dragging it into the cloud to power AI may not make sense.  Similarly, if the data already resides in-cloud – say as part of a data lake – repatriating it for on-premises inferencing may incur dramatic bandwidth charges and slow performance.

Practical Steps for IT Leaders

To effectively integrate AI, cloud, and hybrid strategies, SMB IT leaders should consider the following:

Evaluate Data Gravity: Assess where your data resides and how it is used. Understanding data gravity can help determine whether cloud or on-premises solutions are more suitable for your AI workloads. Prioritize data locality to optimize performance and reduce costs.

Leverage Cloud for Prototyping: Utilize cloud platforms for AI development and prototyping to take advantage of scalable resources and advanced tools. This approach allows for rapid experimentation and iteration without significant upfront investments.  Remember that many AI POCs never make it into production.

Regularly Compare Costs: Calculate and compare the total cost of ownership (TCO) for both cloud and on-premises AI deployments as they move through their lifecycles. Include factors such as hardware investments, cloud service fees, maintenance, and potential useful lifespan of on-prem resources.

Optimize On-Premises for Stable Workloads: For stable, long-term AI workloads, on-premises solutions should be your go-to. Investing in on-premises infrastructure can provide better control, security, and cost-efficiency over time.

Continuous Monitoring and Adaptation: Regularly review and adapt your on-prem and cloud strategies to align with evolving AI use cases. This market moves fast.

By following these steps, SMB IT leaders can develop an adaptable strategy that maximizes the benefits of AI while optimizing cloud and on-premises resources.

Embracing Infrastructure Evolution

The advent of AI is fundamentally reshaping how businesses approach their infrastructure strategies. As AI workloads become more prevalent, they introduce unique demands that necessitate a reevaluation of existing cloud, on-premises, and hybrid workloads. Recognizing AI's impact is crucial for IT leaders aiming to optimize their infrastructure and drive innovation.

AI workloads often require substantial computational resources, efficient data processing, and low-latency environments. These requirements challenge traditional cloud strategies and highlight the need for a more nuanced approach. By considering AI's influence, IT leaders can better balance cloud and on-premises resources, ensuring that their infrastructure supports both current needs and future growth.

To navigate this growing landscape effectively, consider seeking expert guidance. Partnering with specialists who understand the complexities of AI and cloud integration can provide valuable insights and support. Sentinel has the expertise to help you adapt your strategies to accommodate AI workloads, ensuring that your infrastructure remains robust, flexible, and in alignment with your business objectives.

By embracing this evolutionary shift, IT leaders can ensure that their technology investments are well-positioned to support the evolving needs of their organizations, driving innovation and maintaining a competitive edge.