How vector database storage is calculated
Vector databases store dense numeric representations of unstructured data, known as embeddings. Sizing these databases requires accounting for raw vector data, index overheads, and optional payload data (metadata stored alongside the vectors).
A standard single-precision float32 vector requires 4 bytes of memory per dimension. For example, a 1536-dimension embedding (common for OpenAI models) consumes 6,144 bytes of raw memory per vector. When you scale to millions of vectors, memory management becomes a critical operational cost.