Billion-Scale Vector Search on Object Storage

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Practical skills & tools to accelerate your career

Billion-Scale Vector Search on Object Storage

Hosted by Jason Liu and Simon Hørup Eskildsen

Wed, Aug 27, 2025

5:00 PM UTC (1 hour)

Virtual (Zoom)

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Systematically Improving RAG Applications

Jason Liu

View syllabus

Wed, Aug 27, 2025

5:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

Invite your network

Go deeper with a course

Systematically Improving RAG Applications

Jason Liu

View syllabus

What you'll learn

Object Storage Vector Search Architecture

Students will learn to design vector search systems using object storage over traditional disk storage.

Scaling Economics for Petabyte Data

Students will learn cost-effective strategies for scaling vector search to petabyte-scale datasets affordably.

Real-World Implementation Analysis

Students will analyze case studies from Cursor, Notion, and Linear to apply vector search design principles.

Why this topic matters

Vector search infrastructure powers modern AI applications. As companies integrate LLMs with massive datasets, traditional storage hits economic walls. Learning turbopuffer's object storage approach gives you cutting-edge skills for cost-effective, petabyte-scale systems. You'll see how Cursor, Notion, and Linear solve real challenges, positioning you to architect next-gen AI search systems.

You'll learn from

Jason Liu

Consultant at the intersection of Information Retrieval and AI

Jason has built search and recommendation systems for the past 6 years. He has consulted and advised a dozens startups in the last year to improve their RAG systems. He is the creator of the Instructor Python library.

Simon Hørup Eskildsen

turbopuffer co-founder

Simon Hørup Eskildsen is co-founder of turbopuffer, a fast search engine on object storage. Previously, Simon was a principal engineer at Shopify, scaling the system from 1K to 1M requests per second. When time allows, he writes about napkin math on his or the turbopuffer blog and maintains a popular reference on the topic on Github: sirupsen/napkin-math.

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