Turbopuffer with Simon Hørup Eskildsen

Vector search has become a foundational technology for AI applications, enabling everything from semantic code search to contextual retrieval for large language models. However, a major challenge with vector databases has been the cost as data storage scales. TurboPuffer is a vector database that focuses on speed, cost, and scalability. It was created by Simon Harroub Eskelson and Justin Lee in 2023 and has seen adoption from high profile companies such as Cursor and Notion. Siman joins the podcast with Gregor Vann to discuss the origin of turbopuffer, its unique technical design, the economics of vector storage, and more. Gregor Vann is a CTO and founder currently working at the intersection of communication, security, and AI and is based in Singapore. His latest venture, wintik.ai, reimagines what email can be in the AI era. For more on Gregor, find him at van.hk or on LinkedIn. Hello, welcome to Software Engineering Daily. My guest today is Simon Eskelson. Thank you for having me and Gregor. Yeah, great to have you here Simon. We're here today to talk all about TurboPuffer, which is a company some of our audience may have heard of equally, maybe quite a few haven't yet, and it's gonna be why we're here today and talking all about it. However, you probably are using this product behind the scenes without you realizing. So we're gonna get into that as well. But I think to begin with, Simon, you've got a really interesting kind of backstory, and we always dive into those with our guests. So just maybe talk us through very briefly how you kind of got to turbopuffer, and I'll just call out you did spend quite a chunk of time at Shopify, and I think that would be interesting to understand how that's helped lead into what TurboPuffer is today as well. Yeah. That's right. I started my career at Shopify and moved to Canada as a result where Shopify was built. I moved from Denmark where I grew up and worked on infrastructure at Shopify for almost a decade. When I joined in 2013, it was in the hundreds of requests per second. And when I left, we regularly saw peaks in north of 1,000,000 requests per second. And as part of that, I worked on more or less every single aspect of the infrastructure. Generally, the bottlenecks for that kind of scale tend to appear in the data layer. Those are the most persistent ones. So I spent, yeah, almost the entire time working on that layer between the Rails app and the databases and sometimes inside the databases themselves, but mostly on top of them. So, yeah, almost ten years working on every single aspect of database scalability at Shopify. And then after that, I spent about two years working in small increments at my friends' companies on whatever infrastructure problems they had. It turns out in 2022, 2023, it's mostly tuning Postgres auto vacuum was the persistent infrastructure problem. And it was …