How To Store And Serve AI Data At Scale
Interested in being a guest? Email us at admin@evankirstel.com Everyone is obsessed with bigger models and faster GPUs, but the part that decides whether AI actually works in the real world is the data behind it. We talk with Paul Speciale CMO at Scality, about what happens when enterprises try to store, protect, and serve AI data at petabyte and exabyte scale, and why the rise of huge context windows turns “just a chat” into a massive storage and latency problem. We walk through the full enterprise AI pipeline, from data collection and cleansing to inference and the long archive tail most teams never plan for. Paul breaks down which AI phases truly need the fastest storage, why inference can demand microsecond access, and how tiered designs (hot, warm, cool) align to modern GPU stacks. We also dig into what’s changing in the market right now: flash price spikes, constrained supply, and the reality that power per rack often matters more than raw capacity. From there, we get practical about operations and risk. We explore autonomous infrastructure as “tell the system what you want, not how to do it,” including human-in-the-loop recommendations that can move data to cheaper tiers and cut power bills. We also cover cyber resilient storage, immutability for ransomware defense, and the growing pressure around data sovereignty and even code transparency. If you’re building an AI infrastructure plan, this is the blueprint mindset that keeps GPUs busy and data trustworthy. Subscribe, share this with your infrastructure team, and leave a review with the biggest AI data challenge you’re facing. Support the show More at https://linktr.ee/EvanKirstel
