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

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

Standard logs store text. Why might you want to store the embeddings of your production inputs and outputs in a vector database for monitoring?

When would you choose to run a model locally on a user's device (using WebLLM or ONNX) instead of the cloud? Focus on privacy and cost.