Have we reached the end of ‘too expensive’ for enterprise software?

In many cases, this eliminates the need for specialized teams, extensive data labeling, and complex machine-learning pipelines. The extensive pre-trained knowledge of the LLMs enables them to effectively process and interpret even unstructured data. 

An important aspect of this democratization is the availability of LLMs via easy-to-use APIs. Today, almost every developer knows how to work with API-based services, which makes integrating these models into existing software ecosystems seamless. This allows companies to benefit from powerful models without having to worry about the underlying infrastructure. Alternatively, several models can be operated on-premises if there are specific security or data protection requirements. However, this comes at the cost of some of the advantages offered by the leading frontier models. 

Take, for example, an app for recording and managing travel expenses. Traditionally, such an application might have used a specially trained ML model to classify uploaded receipts into accounting categories, such as DATEV. This required dedicated infrastructure and ideally a full MLOps pipeline (for model training, deployment and monitoring) to manage data collection, training and model updates.