Matching AI Infrastructure to Your Business Needs
One of the biggest benefits of hybrid infrastructure is that it lets you place workloads where they make the most sense. The public cloud works well for experimentation or when you need a burst of computing power. On-premises GPU clusters can handle predictable, steady workloads and keep sensitive data under your control.
For smaller IT teams, this flexibility is especially valuable. You don’t have to commit to a single approach upfront. You can start in the cloud, learn what your AI actually needs, then make smarter infrastructure decisions as you go.
“Whether an AI workload is running in the cloud or on-premises is going to depend on the organization’s specific needs for artificial intelligence,” says Mariano Carro, principal field solutions architect for Microsoft hybrid infrastructure at CDW. “Most of the time, we are going to need some resources in the cloud to do the training and for the high level of compute that we need. But once we get that training complete, we may move a workload on-premises to improve performance or protect data privacy.”
Hybrid infrastructure also helps manage costs and supports compliance with data regulations such as HIPAA and the EU’s General Data Protection Regulation — without requiring a massive upfront investment.
LEARN MORE: See how a strategic approach to infrastructure can optimize your AI initiatives.
Deciding Where To Run Your AI Workloads
Workload placement isn’t a one-time decision. As your use of AI matures, where it makes sense to run different applications will likely shift.
“Workload placement is going to take into consideration things like latency as well as accessibility,” says Eryn Brodsky, server and storage practice lead at CDW. “Businesses need to think about what AI outcomes they want to leverage the data. You need to have quick access to it, but you also have to ensure that you have proper access to it.”
Many SMBs start in the cloud — and that’s a smart place to begin. But as cloud bills climb, bringing some workloads back in-house often makes financial sense.
“It’s not uncommon for us to see customers starting in the cloud, as the majority of our customers are, then bringing those applications back on-premises. We call it repatriation,” Brodsky says. “They repatriate those workloads on-prem because they have to consider the cost of maintaining those applications and those workloads in the cloud versus the cost of being able to invest in the architecture to support them.”
Performance is part of the equation too, particularly as AI takes on more demanding tasks in your organization.
“What we’re looking for is lowest cost per function or lowest cost per token,” Gutierrez says. “Leveraging all of the available optimizations — whether it’s through power and cooling, accelerated infrastructure, better data pipelines, better data posture — is really important to achieving the desired outcomes, reducing latency and getting users the information that they need, when they need it.”
