Are rising AI workloads pushing your infrastructure to the limit—and leaving you wondering whether cloud, edge, or on-prem is the smarter investment? As companies rush to deploy generative AI and analytics everywhere, leaders face mounting pressure to balance performance, cost, and reliability. This episode explores the hidden expenses of AI infrastructure and why simplicity, scalability, and smart architecture are key to long-term success.

In this episode of Full Tech Ahead, host Amanda Razani interviews Bruce Kornfeld, Chief Product Officer at StorMagic, about how organizations can optimize edge and on-prem environments to support AI without breaking the bank. Kornfeld shares practical insights on building simple, reliable systems, avoiding over-engineering, and using hyperconverged infrastructure to lower costs and latency. He also discusses the evolution of AI at the edge—from retail use cases to hybrid models that run inference locally while training in the cloud—and offers actionable guidance for IT leaders looking to achieve ROI and agility in their AI strategy.

TIMESTAMPS

[00:00] Introduction and Guest Overview

[01:16] Why Some Organizations Stay On-Prem

[02:33] Simplicity, Cost, and Reliability at the Edge

[04:17] Aligning Teams and Avoiding Miscommunication

[06:06] Cloud vs. Edge Architecture Decisions

[08:02] The Growing Role of AI in Infrastructure Planning

[08:27] Measuring ROI and Building a Sustainable Edge Strategy

[10:25] Edge AI in Action—Retail Use Cases

[12:33] Hybrid AI: Blending Cloud Learning and Edge Inferencing

[14:57] The Core Takeaway – Simple, Smart, and Scalable Edge

Quotes

• “Simplicity is king. If you try to build the edge like a data center, you’ll overspend and overcomplicate.”
• “The cloud can’t solve every problem—sometimes you need real-time performance that only the edge can deliver.”
• “AI doesn’t have to mean massive GPU farms. Smart architecture lets you do more with less.”
• “Leave the big models in the cloud; bring the intelligence you need to the edge.”
• “Building on-prem infrastructure for the edge doesn’t have to be expensive or complicated.”

Takeaways

1. Simplify your infrastructure: Avoid over-engineering; focus on easy, reliable systems suited for edge environments.
2. Adopt hybrid AI models: Keep training in the cloud but run inference locally for faster, cost-effective results.
3. Leverage HCI technology: Combine compute, storage, and networking into smaller, more efficient systems.
4. Measure total cost of ownership: Use ROI and TCO modeling tools (like stormagic.com/tco) before deploying AI infrastructure.

Find Amanda Razani on LinkedIn. https://www.linkedin.com/in/amanda-razani-990a7233/