Elliott Gluck discusses the challenges of scaling vector search, particularly in balancing factors like accuracy, cost, and throughput. To address these issues, the MongoDB Benchmark for Atlas Vector Search was released, offering optimization strategies for handling large-scale datasets and reducing friction in initial testing. The benchmark uses the Amazon Reviews 2023 dataset to examine the performance impacts of various factors such as quantization, vector dimensionality, and concurrency on recall, latency, and throughput. Key findings include that higher-dimensional vectors maintain better recall, and that scalar quantization generally achieves higher queries per second due to less work per query. Despite the complexities involved, these benchmarks aim to guide users in optimizing their vector search performance by providing a starting point and context for evaluations.