88% Cost Reduction Vector Search in Vector Search Real World Case Study

Whether you're struggling with vector search costs, evaluating AI and Vector search implementations, or optimizing existing deployments, this session reveals battle-tested strategies with quantified results you can apply immediately.Agenda

• 6:00-6:30 PM: Networking, food, and drinks
• 6:30-7:00 PM: "How We Reduced Vector Search/AI Powered Search Cost by 88%" - Real-world case study with technical deep-dive
• 7:00-8:00 PM: Q&A and continued networking

Why Vector Search Matters for Better Search ResultsTraditional search works like a dictionary lookup - it finds exact word matches but misses the meaning behind your query. Vector search transforms your content into mathematical representations that capture semantic meaning, enabling search systems to understand context and intent. This means when you search for "car repairs," vector search can surface results about "automotive maintenance" or "vehicle servicing" even if those exact words weren't in your query. It's the technology powering modern AI assistants, recommendation engines, and enterprise search systems that actually understand what you're looking for rather than just matching keywords.This approach delivers dramatically better search results, but the computational requirements can make it expensive to run at scale - which is exactly what our cost optimization techniques address.

Speaker

Matt Gross, Senior Elasticsearch & OpenSearch Engineer at BonsaiMatt manages massive-scale search deployments at Bonsai, overseeing clusters with hundreds of nodes processing billions of queries daily. As one of the industry's leading experts in search cluster optimization, he has deep hands-on experience solving the performance and cost challenges that come with any scale, from small to enterprise-scale search implementations.

Matt is the next in line to speak at the Chicago Search Community, following on the heels of Doug Turnbull and David Tippett.