Overview
Keep the Cloud Experience, Gain Sovereign Control, Build Predictable ROI
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 explored a practical operating model for AI infrastructure designed for regulated financial environments.
Watch the session on-demand to 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.
What You'll Learn
In this session, we covered:
- 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
Who Should Watch
This session was built for technical and operational leaders in financial services, including:
- CTOs, CIOs, and senior technical executives
- Heads of Infrastructure, Platform Engineering, Cloud, or AI Enablement
- AI and ML platform leaders responsible for production inference
- Security, risk, and compliance stakeholders
- Institutions currently running GPU workloads on AWS, Azure, or GCP, and evaluating alternatives
