Quantum AI and Quantum Machine Learning (QML) are among the most promising and dynamic research fields, with a vast variety of QML models. However, standardized benchmarking is lacking, and end users often struggle to determine whether quantum AI—and which specific approach—is suitable for their use cases. Addressing these challenges is essential to evaluate the current state of quantum AI and advance toward quantum utility. We have developed Quant \(^\textit{2}\) AI, a holistic benchmarking framework for systematic comparisons of quantum AI pipelines using high performance clusters and both quantum simulators and hardware. Our end-to-end approach evaluates not just QML models but the whole pipeline, including e.g., preprocessing and hyperparameter variations. Its modular design enables easy integration of new components, such as alternative data preparation. We provide standardized and real-world datasets, quantum and classical AI reference pipelines, state-of-the-art evaluation metrics, and intuitive visualizations. Our framework offers benchmarking as a service for both researchers for testing their newly developed quantum AI components as well as end users for an intuitive way to identify promising quantum AI applications.