1. In-memory caching vs numerical computing: dogpile.cache is primarily used for in-memory caching to improve performance by reducing the need to repeatedly fetch data. On the other hand, Numpy is a powerful library for numerical computing in Python, providing support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

2. Data storage: While dogpile.cache is focused on storing and retrieving data efficiently in memory or through different backends like Memcached or Redis, Numpy deals with storing and manipulating numerical data effectively, allowing for complex operations and computations on arrays.

3. Purpose: The key difference lies in their purpose; dogpile.cache is used for caching data to improve performance and reduce database queries, whereas Numpy is designed for advanced numerical computations and data manipulation, making it essential for scientific computing and data analysis tasks.

4. Community and support: Numpy has a larger and more active community compared to dogpile.cache. This results in more comprehensive documentation, tutorials, and support forums available for Numpy users, facilitating easier troubleshooting and learning resources.

5. Implementation complexities: dogpile.cache involves setting up caching strategies, dealing with cache expiration, and managing cache keys, while Numpy focuses on providing optimized functions and methods for numerical operations without the need for such manual caching implementations.

6. Performance optimization: Numpy is optimized for numerical computing speed and efficiency through its implementation in C, making it much faster for numerical operations compared to regular Python code. Conversely, dogpile.cache focuses on optimizing data fetching from the cache rather than numerical computations.

In Summary, dogpile.cache is primarily used for in-memory caching to improve performance, while Numpy excels in advanced numerical computing and data manipulation tasks with optimized speed and efficiency.