A company is trying to decide, based on cost-effectiveness, whether to keep using a fine-tuned custom model on Bedrock. Which is the MOST appropriate cost trade-off of using a custom model?
A question about choosing the cost trade-off of retaining a custom model.
After customization, there is no token-based charge and only a retention cost remains.
Even with a custom model, inference incurs a charge based on tokens and provisioned capacity.
The understanding that the charge disappears is wrong, so this is incorrect.
The unit price is automatically discounted in proportion to the accuracy improvement.
Accuracy and pricing are not linked, and there is no mechanism for automatic discounts from accuracy improvements.
This is wrong as a description of the cost structure, so it is incorrect.
Once you pay the training cost, all subsequent inference is free.
The fine-tuning training cost and the inference cost are separate, and inference does not become free.
This is wrong as a description of the cost structure, so it is incorrect.
It costs money to retain dedicated capacity, but in exchange it can be specialized for a use case to raise expertise and accuracy.
Correct. A custom model has a trade-off: it costs money to provision and retain dedicated capacity, but in exchange it can be specialized for your own use case to raise expertise and accuracy.
The cost trade-off of a custom model is that it costs money to retain dedicated capacity, but in exchange it can be specialized for a use case to raise expertise and accuracy. Looking at cost-effectiveness, decide whether the value of specialization justifies the capacity cost. "Zero retention cost and always cheapest", "accuracy always drops", and "zero electricity cost" are all wrong and contrary to fact.