When using Amazon Bedrock to generate inferences with a large language model, which factor primarily determines inference cost?

The number of tokens consumed is the primary factor determining inference cost in Amazon Bedrock. You are charged based on the input tokens sent to the model and the output tokens generated by the model. Larger inputs and longer outputs directly translate to higher costs. The chosen temperature setting influences the creativity of the output but does not directly impact the billing metric. The volume of data used to train the LLM and the total time spent training the model are factors related to the model's development and pre-training, not the cost of performing inferences with an already deployed model on Bedrock.