Stop Using Cosine for Everything: 5 Distance Metrics That Unlock Hidden Powers in Your Vector Database

Everyone uses cosine similarity. Tutorials use it. Frameworks default to it. If you ask "which distance metric should I use?", the answer is always "cosine, probably." But here is the thing: your vector database supports other metrics. And those metrics unlock use cases that cosine literally cannot handle. This is not a math lecture. This is a practical guide. Five metrics, five real-world problems, working code you can run in two minutes. By the end, you will look at your vector database differently. A quick mental model (no math degree required) Before we dive in, let's build some intuition. Imagine you have two arrows on a piece of paper: Cosine asks: "Do these arrows point in the same direction?" It does not care how long they are. Euclidean asks: "How far apart are the tips of these arrows?" It cares about both direction and length. Dot Product asks: "Do they point the same way, AND are they both strong signals?" Direction plus intensity. Hamming asks: "How many switches are flipp