Given how critical AI is, one of the big questions for IT leaders is what type of infrastructure do you need to support the nearly insatiable data- and compute-intensive demands of AI and ML? As importantly, do you host AI/ML infrastructure in the cloud, which offers more flexibility and scalability, or in your data center, where you have greater control?
We asked the IDG Influencer Network, a community of IT professionals, industry analysts, and other experts, about the best way to build an AI infrastructure. While there’s no perfect answer, it’s clear from their responses that both the cloud and the data center can play critical roles.
The one thing many of our experts agreed on is that the right answer is the one that best fits your organizational needs:
Benefits of AI in the cloud
That said, respondents had some very strong arguments in favor of putting AI data and infrastructure in the cloud.
“AI infrastructure benefits most from cloud hosting compared to the data center,” said Scott Schober, president and CEO of Berkeley Varitronics Systems (@ScottBVS). “This is because AI relies on on-demand services that instantly cater to individual users or their devices. Data centers allow storage and collaboration of on-premises data but cannot scale well.”
Benjamin Ajibade, data analytics lead at SHIFT Nigeria (@Benni_aji), expanded on this point: “The cloud can provide developers with a high level of versatility while also ensuring an easy workflow. Although on-premises [systems] can be used for development and testing, it is recommended that a live application be deployed in the cloud for scalability and availability.”
Furthermore, the cloud promises significant cost savings. Using a service provider allows you to avoid buying and maintaining significant amounts of hardware and software. This, in turn, frees up IT teams to focus on strategic operations.
Moving infrastructure to the cloud “eliminates the costs of maintaining data centers on-site, such as hardware and maintenance,” said Audrey Desisto, founder of Digital Marketing Stream (@AudreyDesisto). “These upfront costs can prove prohibitive for AI projects, so using a cloud service provider can make research and development costs easier to manage.”
Will Kelly, technical marketing manager for a container security startup (@willkelly), seconded this point. “Like every data-intensive use case we have, data storage can be costly,” he said. “There are a lot of questions here that an organization needs to answer, especially when it comes to cloud costs. FinOps expertise is a must-have on this cross-functional team.”
However, while saving money is always important, it isn’t the primary factor when it comes to using the cloud.
