Automating AI model selection in production is essential for adapting to the dynamic nature of AI performance, where factors like cost, latency, and model quality can change over time. This approach involves using performance monitoring, routing logic, and tools such as Eden AI's unified API to ensure the most efficient model is selected for each use case. By defining measurable performance indicators and implementing a unified API layer, developers can standardize inputs and outputs, enabling real-time model switching without code duplication. The use of routing and fallback logic allows for automatic rerouting in case of model failure, while continuous monitoring ensures ongoing optimization. Eden AI simplifies this automation process by providing infrastructure that supports the integration and management of multiple AI models efficiently. This results in an adaptive system that maintains flexibility and delivers consistent AI performance without manual intervention.