Algorithmic complexity, often referred to as computational complexity, measures the efficiency of an algorithm in terms of time and space resources. It helps determine how the performance of an algorithm scales with the size of the input data. The two main types of complexity are time complexity, which assesses how the execution time grows with input size, and space complexity, which evaluates the amount of memory required. Understanding algorithmic complexity is crucial for developers and computer scientists, as it guides them in selecting the most efficient algorithms for specific tasks. By analyzing complexity, one can predict how an algorithm will perform under different conditions, ensuring optimal use of resources in software development.