What should CIOs know before adopting vector search?

See how vector search drives faster AI deployment, lower costs, and better search results backed by independent research from GigaOM.

Legacy search infrastructure isn’t built for what AI demands. As organizations race to deploy RAG pipelines, intelligent agents, and multimodal search, fragmented keyword-based systems create cost, complexity, and risk.

This GigaOM brief gives CIOs and technology leaders a clear framework for evaluating vector database adoption, including the business case, organizational impact, investment outlook, and a phased deployment timeline.

What’s inside:

  • The business case for vector search, including search relevance gains of 40-60% and infrastructure cost reductions of 30-50%
  • Why delaying action compounds technical debt and competitive risk
  • How OpenSearch unifies full-text and vector search in a single open source platform
  • ROI timelines, deployment phases, and best practices for going from pilot to production
  • GigaOM’s analyst take on why OpenSearch merits serious evaluation for enterprise AI workloads

About OpenSearch

OpenSearch is the trusted, open source platform for AI-powered search, analytics, and vector database solutions. With no licensing fees, flexible deployment options, and native integrations with leading embedding providers and LLMs, OpenSearch gives enterprises the speed and control to build intelligent applications without vendor lock-in.

About the Brief

Authored by Howard Holton, analyst at GigaOM, with three decades of IT experience including executive leadership roles as CIO and CTO. Commissioned by OpenSearch.

Download the Brief