DiskANN is a Microsoft-developed solution for large-scale vector search that optimizes costs and performance. It uses Approximate Nearest Neighbor (ANN) algorithms to find the closest vectors quickly, trading off accuracy for speed in many cases. Key ANN techniques include ANNOY, Locality-Sensitive Hashing (LSH), and Quantization. DiskANN is a cost-effective solution that works with SSD memory, reducing hardware costs and offering fast and accurate searches on large datasets. It has applications in recommendation engines, document indexing, search engines, time-series data, and streaming data.