September 2, 2025
Private AI vs. Cloud: How Enterprise Leaders Can Make Smarter Build-or-Buy Decisions
Is your organization ready to own AI or are you better served by leveraging the speed and scale of the cloud? In this episode of the AI Proving Ground Podcast, WWT High-Performance Architecture Director Jeff Fonke and VP of Advanced Technology Solutions Jeff Wynn break down the toughest question facing IT leaders today: should you build or buy your AI capabilities? From the economics of inference costs to hybrid cloud realities, the two Jeffs share practical strategies on private AI, workload orchestration, data readiness and overcoming the enterprise skills gap.
Artificial intelligence is no longer a distant experiment — it's a daily operational reality. But as adoption accelerates, one question keeps surfacing in boardrooms and IT strategy sessions alike: Should we build our own private AI capabilities or buy into the speed and scale of the cloud?
In this episode of the AI Proving Ground Podcast, Jeff Fonke and Jeff Wynn pull back the curtain on how enterprises are approaching this pivotal decision. They explore why the answer isn't as simple as "build vs. buy," but instead starts with a deeper question: why are you pursuing private AI in the first place?
Through real-world examples, Fonke and Wynn lay out a practical framework for IT leaders. Along the way, they reveal what signals indicate an organization is truly "ready" for private AI, why workflow orchestration is emerging as the new DevOps and how bridging the enterprise skills gap is just as critical as deploying GPUs.
Whether you're a CIO under pressure to reduce runaway cloud bills, or a CISO worried about sensitive data leaving your walls, this conversation offers clarity in a market that often feels chaotic and contradictory.
What you'll learn in this episode:
- The "Why" Behind Private AI: Before asking build vs. buy, leaders must first ask why — what business outcomes are you driving, and how do your workloads and data sovereignty needs inform the decision?
- Hybrid is the New Normal: Most enterprises will operate in a mix of private and public AI environments. Success depends on balancing economics, security and workload requirements across both.
- Data is the Lifeblood: Effective AI strategies begin with strong data foundations. Leaders must address data mobility, sovereignty, and technical debt before scaling private AI.
- Workflow & Orchestration Are Critical: Private AI requires more than GPUs — it demands sophisticated workflow management, automation and orchestration tools to deliver business value.
- Bridging the Skills Gap: While most companies have deep business expertise, IT and operations teams often struggle with new AI infrastructure. Building AI centers of excellence and investing in workforce AI training is essential.
- Signals of Readiness: Skyrocketing cloud inference bills, strong data strategies, and modernized infrastructure are clear indicators that an organization may be ready to build private AI.
- Adopt a Continuous Learning Mindset: The pace of AI is relentless. Staying current means engaging in forums, labs, podcasts, and hands-on experimentation—not relying on static playbooks.
-Jeff Wynn, VP of Advanced Technology Solutions
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About the AI Proving Ground Podcast:
AI deployment and adoption are complex. The AI Proving Ground Podcast makes them actionable. Join top experts, IT leaders and innovators as we explore AI's toughest challenges, uncover real-world case studies and reveal practical insights that drive AI ROI. From strategy to execution, we break down what works (and what doesn't) in enterprise AI. New episodes are available every week.