Which metric best measures the runtime efficiency of serving AI models in production?

Average response time is the most direct measure of runtime efficiency for serving AI models in production because it quantifies how quickly the model processes requests and returns results to users. A lower average response time indicates better efficiency and a more responsive user experience. Customer satisfaction score (CSAT) is a business metric, not a technical measure of model performance or efficiency. Training time per epoch measures the efficiency of the model's training phase, not its inference (serving) phase. Number of training instances relates to the dataset size and training process, not the runtime performance of the deployed model.