What is embedding dimensionality and how does it affect performance?

Embedding dimensionality is the number of components in each embedding vector, typically ranging from 384 to 4096 for modern models. Higher dimensions can capture more nuance but increase storage cost and search latency. Many production systems use dimension reduction techniques like Matryoshka embeddings to allow flexible tradeoffs between quality and cost.