OpenSearchCon India 2026

Your users search for “comfortable shoes for standing all day” and get shoe polish. 30% of searches return zero results even when products exist. This is keyword hell, costing you customers daily.

This session reveals how we transformed Adobe Commerce search using OpenSearch’s vector search capabilities. Learn the complete technical journey, including text embeddings, k-NN similarity, and hybrid search that combines keyword precision with semantic understanding.

We’ll cover the architecture, including vector indexing with FAISS and Lucene, ingest pipelines for automatic embedding generation, and query-time optimization.

Honest challenges included computational costs, embedding model selection through sentence-transformers comparisons, dimension tradeoffs between 384 and 768, cold start problems, and explainability to stakeholders.

You’ll get actionable knowledge such as exact OpenSearch configurations, PPL queries for semantic search, hybrid scoring strategies, open-source model recommendations, and cost optimization for Indian startups, with $30 per month infrastructure compared to $200 or more for managed vector databases.

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.