Selecting the most appropriate large language model is always a daunting task as several models are available in the market from different vendors. In addition to that, new models are invented in almost every week. Hence, an adoption framework is the need of the hour which will guide the user to select the most appropriate model based on the business requirement. However, the basis of selection should be designed carefully as this is the key to success of the framework. Initially, 11 parameters are chosen which include modality, model type, model parameters, model size, task/use case type, knowledge cut-off, context window size, multilingualism, latency & throughput, resource utilization, and model accuracy; based on which the right model will be recommended to the user. The paper presents an innovative decision tree-based framework which will receive the user requirements, transform those in the form of above 11 implicit variables, and recommend the most effective model after analyzing the same.