Bringing enterprise AI inference on premises with Scality
Show notes
What should a business consider before moving AI inference onto infrastructure it owns?
In this episode of Tech Talks Daily, I speak with Paul Speciale, Chief Marketing Officer at Scality, about the decisions behind enterprise AI infrastructure. Following the launch of Scality AI Inference Factory, we discuss why organizations are assessing on-premises inference alongside cloud services, with attention to costs, model control and the location of sensitive data. Paul expects a hybrid approach, with different workloads running in the environments that suit their requirements.
The conversation begins with the work a GPU repeats when an AI session needs to process familiar context. Paul explains how saving and retrieving that information can reduce recomputation, and why storage and networking deserve attention alongside compute. We discuss where the economics change as usage grows and why owning the infrastructure also means taking responsibility for utilization, performance and the operating bill.
We then consider data placement across storage tiers. Paul argues that there is no single best location for every workload, with performance, cost and sovereignty requirements shaping the decision. I ask where automation should stop and human approval should begin. He describes automating health checks, forecasts and recommendations while retaining approval for consequential changes such as deleting data or changing policies.
Cyber resilience is another part of the discussion. Paul explains why an immutable copy needs to be supported by tested recovery, and why sovereignty rules should be checked through attempts to perform actions the system is meant to refuse. We close with the responsibilities infrastructure teams take on as they manage inference directly, from tracking time to first token and context reuse to deciding when a model should change. For leaders assessing their next AI deployment, the conversation offers practical questions about control and the evidence behind the business case. Which workloads would you consider running locally, and what would you want to prove before making that decision? Please share your thoughts with me.