Building serverless vector search with Turbopuffer CEO, Simon Eskildsen

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/SirupsenLinkedIn: https://ca.linkedin.com/in/sirupsenTurbopuffer: https://turbopuffer.comFollow Aaron:Twitter/X:  https://twitter.com/aarondfrancis Database School: https://databaseschool.comDatabase School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g  (Subscribe today)LinkedIn: https://www.linkedin.com/in/aarondfrancisWebsite: https://aaronfrancis.com - find articles, podcasts, courses, and more.Chapters00:00 - Introduction01:11 - Simon’s background and time at Shopify03:01 - The Rails glory days and early developer experiences04:55 - From PHP to Rails and joining Shopify06:14 - The viral blog post that led to Shopify09:03 - Discovering engineering talent through GitHub10:06 - Scaling Shopify’s infrastructure to millions of requests per second12:47 - Lessons from hypergrowth and burnout14:46 - Life after Shopify and “angel engineering”16:31 - The Readwise problem and discovering vector embeddings18:22 - The high cost of vector databases and napkin math19:14 - Building TurboPuffer on object storage21:20 - Landing Cursor as the first big customer23:00 - What TurboPuffer actually is25:26 - Why object storage now works for databases28:37 - How TurboPuffer stores and retrieves data31:06 - What’s inside those S3 files33:02 - Explaining vectors and embeddings35:55 - How TurboPuffer v1 handled search38:00 - Transitioning from search engine to database44:09 - How Turbopuffer v2 and v3 improved performance47:00 - Smart caching and architecture optimizations49:04 - Trade-offs: high write latency and cold queries51:03 - Cache warming and primitives52:25 - Comparing object storage providers (AWS, GCP, Azure)55:02 - Building a multi-cloud S3-compatible client57:11 - Who TurboPuffer serves and the scale it runs at59:31 - Connecting data to AI and the global vision1:00:15 - Company size, scale, and hiring1:01:36 - Roadmap and what’s next for TurboPuffer1:03:10 - Why you should (or shouldn’t) use TurboPuffer1:05:15 - Closing thoughts and where to find Simon

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