When a machine learning model goes into production, it is very likely to be idle most of the time. There are a lot of use cases, where a model only needs to run inference when new data is available. If we do have such a use case and we deploy a model on a server, it will eagerly be checking for new data, only to be disappointed for most of its lifetime and meanwhile you pay for the live time of the server. Now the cloud era has arrived, we can deploy a model serverless. Meaning we only pay for the compute we need, and spinning up the resources when we need them.