Financial institutions are accelerating AI adoption, but hyperscaler GPU stacks often introduce tradeoffs that become harder to ignore at scale, including unpredictable costs, data residency and sovereignty risk, and platform lock-in that limits how modern workloads can be deployed across the enterprise.
In this educational session featuring Arc Compute and WEKA, we will explore a practical operating model for AI infrastructure designed for regulated financial environments. You will learn how finance teams can preserve the cloud-like experience their users expect, while regaining sovereign control over data and compute, and creating a clear path to predictable ROI for inference and production AI.
This is not a product demo. The focus is on architecture, operating models, and decision criteria that stand up to real-world constraints across security, compliance, and scale.
What You Will Learn
- How financial institutions deliver a cloud-like AI and ML experience without relying solely on hyperscalers
- What data sovereignty looks like in practice as inference and training workloads scale
- How to run bare metal, managed LLM services, and agentic systems within one cohesive operating model
- How to identify the key cost drivers behind AI infrastructure, and build a predictable ROI framework
- Real-world patterns, tradeoffs, and moderated live Q&A