OpenSearchCon India 2026

Building billion-scale vector indexes on OpenSearch has traditionally meant days of CPU-intensive processing. OpenSearch 3.0 changes this with GPU-accelerated index building using CAGRA and auto-optimize for automated hyperparameter tuning.

This session shares hands-on lessons building billion-scale vector indexes on OpenSearch 3.0. We cover the end-to-end journey: selecting embedding models, choosing between HNSW and IVF algorithms, leveraging GPU-accelerated builds for up to 9x faster indexing, and using auto-optimize to balance recall, latency, and cost without manual tuning of ef_construction and M parameters.

We dive into production realities including quantization strategies (scalar, product, binary) to reduce memory footprint, disk-optimized vector search for cost-sensitive workloads, and hybrid search combining vector similarity with BM25 keyword matching. We cover operational patterns for zero-downtime index rebuilds and monitoring vector search performance.

Attendees leave with a decision framework for vector index configuration, benchmarks across deployment sizes, and guidance on when GPU acceleration pays off versus CPU-only approaches.