As AI applications scale from proofs of concept to production systems serving millions, they encounter challenges related to scalability, latency, and resource utilization, particularly in vector search. High memory usage, increased latency, and rising infrastructure costs become significant bottlenecks as systems need to process vast amounts of high-precision data. Vector quantization provides a solution by compressing high-dimensional embeddings into more compact representations, significantly reducing memory requirements and speeding up retrieval without sacrificing accuracy. MongoDB Atlas facilitates this process by automatically handling the creation, storage, and indexing of compressed vectors, allowing easier scaling and management of AI workloads. Quantization-aware models, such as those offered by Voyage AI, are optimized for this compressed data, maintaining effectiveness across different precision levels. This approach not only addresses the core challenges of memory, latency, and cost but also enables organizations to scale AI applications efficiently.