As data consolidates into relational databases, the need for text-to-SQL systems grows, democratizing access to information by allowing natural language queries. The potential for improvements brought by Large Language Models (LLMs) in text-to-SQL systems is mostly assessed on monolingual English datasets. However, their performance and robustness with respect to other languages remain vastly unexplored. In this project, we would like to concentrate on a multi-lingual and cross-lingual benchmark for evaluating text-to-SQL systems based on real-world applications. This dataset will comprise natural language/SQL-pairs over big databases with varying levels of complexity for English, German, French, and Italian. We will assess robustness of current state-of-the-art text-to-SQL models, particularly in handling diverse languages such as German, French, and Italian. We aim to address the challenges of achieving fairness in multiple languages, initiating discussions to develop more reliable text-to-SQL systems tailored for multi-lingual environments.