Steel City Consulting has witnessed an increase in customers moving beyond AI experimentation, to asking what happens when it becomes part of everyday business operations. As more teams adopt AI tools and those tools become part of regular workflows, the IT infrastructure specialists argue that businesses need to carefully consider how that AI is run and supported. In this guest post, Daniel Birch, the company’s founder and managing director, explains reasons for bringing AI processing away from the public cloud and in-house, and why the wider environment needs to keep up with AI infrastructure, to prevent bottlenecks down the line.

From AI experimentation to production

For many businesses, AI implementation starts in the public cloud because it is quick to access and easy to scale. In simple terms, the computing power sits in a provider’s data centre rather than on your own premises, and you pay to use it as needed. That keeps upfront costs low, but as usage grows, monthly spend can become harder to predict and businesses have less direct control over where data is processed and how the infrastructure is managed.

Another option is bringing some processing in-house. An AI server gives a business dedicated computing power within its own environment, while private AI can give organisations tighter control over how models, data and processing are accessed and managed. That can provide greater control over costs and help keep sensitive data within a more controlled environment, but it also means taking on more responsibility for running the infrastructure.

We’ve seen first-hand why that trade-off matters. One of our customers was spending thousands of pounds each month on cloud-based AI usage. For businesses reaching that point, the question is whether ongoing cloud spend is starting to outweigh the upfront cost and responsibility of running AI in-house.

For businesses looking to bring dedicated AI compute in-house, a GPU server can provide a more focused starting point. One of the main AI adoption challenges is sizing the infrastructure correctly. Over-specifying increases cost, while under-specifying can create performance issues as demand grows.

Avoiding AI infrastructure bottlenecks

Once the compute is chosen, the rest of the environment needs to keep up. AI networking needs to move data between systems quickly, while AI storage needs to feed large datasets to the GPUs without creating delays. If either becomes a bottleneck, expensive hardware can sit underutilised while teams wait longer for results. That is why network capacity and storage performance should be assessed before significant investment is made into AI infrastructure.

Running AI on premises also means taking responsibility for the environment day to day. Power and cooling need to support higher-density hardware, security and compliance policies need to cover the data and models being used, and IT teams need the skills to maintain and troubleshoot the platform. These are ongoing responsibilities that should be understood before workloads move in-house.

How businesses can prepare for AI adoption

For organisations, preparation should start with the workloads themselves. Identify which AI use cases are becoming business-critical, what data they rely on, how frequently they are used and how demand may grow. IT teams can then decide what should stay in the cloud, what could move on premises and what needs to change first.

At Steel City Consulting, we help businesses assess what they need before they invest in private AI. That means looking at workloads, existing infrastructure, budget and operational requirements, then designing the right-sized solution around them. Our AI infrastructure solutions cover the compute, networking, storage and supporting infrastructure needed to help organisations move AI from experimentation into production. 

If you’re exploring how to expand AI use in your business, our team can help you assess whether private AI is the right fit. Contact us today.