88% Cost Reduction in Vector Search: Real-World Case Study - August 19th at Mhub

What if you could cut your AI-powered Vector search on OpenSearch and Elasticsearch costs by 88% without sacrificing performance? Join Chicago's search community for a deep-dive case study revealing the exact techniques that transformed our vector search economics.

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 Results
Traditional 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.