Vector quantization is a technique used to compress high-dimensional embeddings into compact representations, reducing memory requirements and accelerating similarity computations. This approach is crucial for large-scale AI workloads that process millions of vector embeddings, as it addresses the challenges of scalability, latency, and resource utilization. By storing embeddings in reduced-precision formats such as int8 or binary, organizations can dramatically cut memory usage and speed up retrieval, while maintaining retrieval accuracy through compression and rescoring techniques. Vector quantization is particularly valuable for high-volume scenarios, real-time responses, and systems requiring low-latency queries under high user concurrency. Quantization-aware training models, like those from Voyage AI, help maintain accuracy while reaping cost savings at scale. MongoDB Atlas supports automatic vector quantization, enabling developers to run large-scale vector workloads on smaller, more cost-effective clusters.