SQL and MongoDB are distinct database management systems with fundamental differences. SQL databases follow a structured, tabular data model with a fixed schema, relying on SQL as the query language. They are ideal for applications requiring rigid data consistency and complex querying, such as financial systems. In contrast, MongoDB adopts a NoSQL approach, storing data in flexible JSON-like documents within collections. It offers schema flexibility and horizontal scalability, making it suitable for dynamic and evolving data, such as content management systems and real-time analytics. The choice between SQL and MongoDB hinges on your project's data structure, scalability needs, and querying requirements.

MongoDB can be considered better than traditional SQL databases in scenarios where schema flexibility, horizontal scalability, and rapid development are paramount. Its document-oriented, schema-less design allows for dynamic data structures and agile development, making it an excellent choice for applications with evolving data requirements. MongoDB's native support for complex data structures and its ability to distribute data across multiple servers enhance its suitability for large and growing datasets or high-traffic applications. However, it's important to recognize that MongoDB's trade-offs include a lack of strict ACID transactions, complex queries, and the need for careful data management to maintain data integrity. The choice between MongoDB and SQL should align with your project's specific needs and the trade-offs that best match your use case.