Name: 88% Cost Reduction in Vector Search: Real-World Case Study - August 19th at Mhub
Start: 2025-08-19T18:00:00-05:00
End: 2025-08-19T20:00:00-05:00
Location: 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.

Joel Martens

Chicago Search Community - OpenSearch - Elasticsearch

OpenSearch Project

Technology

Open Source

New Technology

Big Data

Web Analytics

Data Analytics

Predictive Analytics

NoSQL

Open Data

Technology Startups

Cloud Analytics

Enterprise Search

**Matt Gross, Senior Elasticsearch & OpenSearch Engineer at Bonsai**
Matt 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](https://softwaredoug.com/) and [David Tippett](https://github.com/dtaivpp). 

Matt Gross

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

Chicago Search Community - OpenSearch - Elasticsearch

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

Chicago Search Community - OpenSearch - Elasticsearch

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