Economical way of serving vector search workloads with Simon Eskildsen, CEO Turbopuffer
About this episode
In this episode, Simon Eskildsen discusses the economical aspects of serving vector search workloads with Turbopuffer.
Turbopuffer search engine supports such products as Cursor, Notion, Linear, Superhuman and Readwise. Craft decaf & half caf coffee, 25% discount: https://savorista.com/discount/VECTOR This episode on YouTube: https://youtu.be/I8Ztqajighg Medium: https://dmitry-kan.medium.com/vector-podcast-simon-eskildsen-turbopuffer-69e456da8df3 Dev: https://dev.to/vectorpodcast/vector-podcast-simon-eskildsen-turbopuffer-cfa If you are on Lucene / OpenSearch stack, you can go managed by signing up here: https://console.aiven.io/signup?utm_source=youtube&utm_medium=&&utm_content=vectorpodcast Time codes: 00:00 Intro 00:15 Napkin Problem 4: Throughput of Redis 01:35 Episode intro 02:45 Simon's background, including implementation of Turbopuffer 09:23 How Cursor became an early client 11:25 How to test pre-launch 14:38 Why a new vector DB deserves to exist? 20:39 Latency aspect 26:27 Implementation language for Turbopuffer 28:11 Impact of LLM coding tools on programmer craft 30:02 Engineer 2 CEO transition 35:10 Architecture of Turbopuffer 43:25 Disk vs S3 latency, NVMe disks, DRAM 48:27 Multitenancy 50:29 Recall@N benchmarking 59:38 filtered ANN and Big-ANN Benchmarks 1:00:54 What…
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