From Lexical to Semantic: How Vector Databases Enhance Enterprise Search
Enterprises are sitting on massive amounts of data they can’t fully use. Not because it doesn’t exist, but because traditional keyword search can’t surface it in ways that matter for AI.
This 451 Research special report, commissioned by The Linux Foundation for the OpenSearch Project, lays out what’s changed, why hybrid search is the approach that works, and what the vector database market looks like through 2030.
What’s inside:
- Why lexical search falls short for modern AI workloads
- How vector embeddings and similarity search unlock semantic understanding
- The role of RAG in grounding LLMs with your proprietary data
- Market data and forecasts for the vector database segment through 2030
- What hybrid search means for your architecture and your AI roadmap
About OpenSearch
OpenSearch is the trusted, open source platform for AI-powered search, analytics, and vector database solutions. With built-in hybrid and vector search, an extensible ML architecture, and no licensing fees or vendor lock-in, OpenSearch gives you everything you need to build intelligent search applications and keep full control of your data.
About the Report
Authored by James Curtis, Research Director for Data, AI and Analytics at 451 Research (S&P Global), and commissioned by the Linux Foundation. Survey data drawn from nearly 600 enterprise respondents.
This report was commissioned by the Linux Foundation and produced by 451 Research, part of S&P Global. All rights reserved by S&P Global Inc. Report statistics and findings are referenced for informational purposes.
