Scaling vector search operations with MongoDB Atlas Quantization and Voyage AI Embeddings can significantly improve performance while maintaining semantic search capabilities. Vector quantization techniques, such as binary and scalar quantization, can reduce RAM usage by up to 24x and storage footprint by 38%. The most significant benefits are seen at scale, particularly for vector databases exceeding 1M embeddings. Quantization-aware models like Voyage AI's voyage-3-large model retain high representation capacity even after compression. Measuring representational capacity retention is critical to ensure semantic fidelity is preserved after quantization. Experimental results show that scalar quantization achieves near-perfect retention while binary quantization shows a retention-exploration trade-off. The optimal approach depends on the specific use case, with binary quantization suitable for high-scale deployments and scalar quantization providing an effective balance between performance and precision.