OpenSearchCon India 2026

Vector search in OpenSearch offers a rich set of configuration options, but choosing the right combination of engine, algorithm, quantization, and storage tier can be daunting — especially as your workload grows or shifts from semantic search to agentic AI. This session covers both workload types at small, medium, and high scale. You'll learn how to apply the cost-recall-latency curve to choose between Faiss and Lucene, HNSW and IVF, and the right quantization technique for your budget and recall requirements. You'll explore tiered storage from in-memory to disk-based and memory-optimized, and production tuning techniques including bulk indexing strategies, GPU-accelerated builds, and automated parameter optimization. You'll leave with best practices to apply whether you're running thousands of vectors on a single node or billions across a fleet.