How do you track which specific feature or user in your app is driving the most "Token Spend"?

How do you monitor for "Concept Drift" in an LLM application? If the model's output starts getting shorter over time, is that a deployment failure or a data failure?

Can you use "Spot" or "Preemptible" GPU instances for real-time inference? What happens to the user's request if the cloud provider reclaims the GPU mid-generation?

How does reducing the precision of model weights from 16-bit to 4-bit impact your infrastructure costs?