Building serverless vector search with Turbopuffer CEO, Simon Eskildsen

Database School

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8d ago 1:06:48

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In this episode, Aaron Francis talks with Simon Eskildsen, co-founder and CEO of TurboPuffer, about building a high-performance search engine and database that runs entirely on object storage. They dive deep on Simon's time as an engineer at Shopify, database design trade-offs, and how TurboPuffer powers modern AI workloads like Cursor and Notion.

Follow Simon:
Twitter: https://twitter.com/Sirupsen
LinkedIn: https://ca.linkedin.com/in/sirupsen
Turbopuffer: https://turbopuffer.com

Follow Aaron:
Twitter/X: https://twitter.com/aarondfrancis
Database School: https://databaseschool.com
Database School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g (Subscribe today)
LinkedIn: https://www.linkedin.com/in/aarondfrancis
Website: https://aaronfrancis.com - find articles, podcasts, courses, and more.

Chapters
00:00 - Introduction
01:11 - Simon’s background and time at Shopify
03:01 - The Rails glory days and early developer experiences
04:55 - From PHP to Rails and joining Shopify
06:14 - The viral blog post that led to Shopify
09:03 - Discovering engineering talent through GitHub
10:06 - Scaling Shopify’s infrastructure to millions of requests per second
12:47 - Lessons from hypergrowth and burnout
14:46 - Life after Shopify and “angel engineering”
16:31 - The Readwise problem and discovering vector embeddings
18:22 - The high cost of vector databases and napkin math
19:14 - Building TurboPuffer on object storage
21:20 - Landing Cursor as the first big customer
23:00 - What TurboPuffer actually is
25:26 - Why object storage now works for databases
28:37 - How TurboPuffer stores and retrieves data
31:06 - What’s inside those S3 files
33:02 - Explaining vectors and embeddings
35:55 - How TurboPuffer v1 handled search
38:00 - Transitioning from search engine to database
44:09 - How Turbopuffer v2 and v3 improved performance
47:00 - Smart caching and architecture optimizations
49:04 - Trade-offs: high write latency and cold queries
51:03 - Cache warming and primitives
52:25 - Comparing object storage providers (AWS, GCP, Azure)
55:02 - Building a multi-cloud S3-compatible client
57:11 - Who TurboPuffer serves and the scale it runs at
59:31 - Connecting data to AI and the global vision
1:00:15 - Company size, scale, and hiring
1:01:36 - Roadmap and what’s next for TurboPuffer
1:03:10 - Why you should (or shouldn’t) use TurboPuffer
1:05:15 - Closing thoughts and where to find Simon