{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Database School","title":"Building serverless vector search with Turbopuffer CEO, Simon Eskildsen","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/2ae920a0\"></iframe>","width":"100%","height":180,"duration":4008,"description":"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.\nFollow Simon:\nTwitter: https://twitter.com/Sirupsen\nLinkedIn: https://ca.linkedin.com/in/sirupsen\nTurbopuffer: https://turbopuffer.com\nFollow Aaron:\nTwitter/X:  https://twitter.com/aarondfrancis \nDatabase School: https://databaseschool.com\nDatabase School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g  (Subscribe today)\nLinkedIn: https://www.linkedin.com/in/aarondfrancis\nWebsite: https://aaronfrancis.com - find articles, podcasts, courses, and more.\nChapters\n00:00 - Introduction\n01:11 - Simon’s background and time at Shopify\n03:01 - The Rails glory days and early developer experiences\n04:55 - From PHP to Rails and joining Shopify\n06:14 - The viral blog post that led to Shopify\n09:03 - Discovering engineering talent through GitHub\n10:06 - Scaling Shopify’s infrastructure to millions of requests per second\n12:47 - Lessons from hypergrowth and burnout\n14:46 - Life after Shopify and “angel engineering”\n16:31 - The Readwise problem and discovering vector embeddings\n18:22 - The high cost of vector databases and napkin math\n19:14 - Building TurboPuffer on object storage\n21:20 - Landing Cursor as the first big customer\n23:00 - What TurboPuffer actually is\n25:26 - Why object storage now works for databases\n28:37 - How TurboPuffer stores and retrieves data\n31:06 - What’s inside those S3 files\n33:02 - Explaining vectors and embeddings\n35:55 - How TurboPuffer v1 handled search\n38:00 - Transitioning from search engine to database\n44:09 - How Turbopuffer v2 and v3 improved performance\n47:00 - Smart caching and architecture optimizations\n49:04 - Trade-offs: high write latency and cold queries\n51:03 - Cache warming...","thumbnail_url":"https://img.transistorcdn.com/QPpKhRBWtM56k1sC7Y7j7aqGy8sd-XKWw6mEKgA1jmw/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9lMmRl/ZTM5ZTAxOTMzNTgy/MmJmZTA4MWE1ZjMw/YjE4Ny5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}