Everyone is deploying AI.
Almost no one is measuring what it truly costs.

AI workloads behave fundamentally differently from traditional microservices — they are GPU-hungry, memory-intensive, bursty, and often poorly autoscaled. On Kubernetes, that translates into idle GPUs, oversized nodes, inefficient bin-packing, runaway inference scaling, and hidden infrastructure waste.

And the scary part?
Most teams don’t even see it.

In this deep-dive session, we’ll explore what actually happens when AI workloads hit production Kubernetes clusters — especially in cloud environments like Azure.

We’ll break down:

• Why traditional autoscaling strategies fail for inference workloads
• GPU scheduling, bin-packing, and resource fragmentation problems
• Cost traps in model serving architectures
• Observability patterns to detect waste before it becomes a budget crisis
• Designing cost-aware AI platforms on Kubernetes
• Practical architecture patterns that balance performance, scalability, and cost

This session is not about hype. It’s about infrastructure reality.

If you are a cloud engineer, architect, or platform builder deploying AI systems — this talk will give you the mental models and architectural patterns to run AI workloads responsibly at scale.

Because scaling AI without cost visibility is not innovation.
It’s liability.