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Vector Podcast

4 min read

Vector Podcast

🎙️ Listen / watch: https://www.vectorpodcast.com/ · YouTube · Apple Podcasts · RSS

Long-form interview podcast on vector search, search engines, and the businesses built around them — founded and hosted by Dima Kan. It launched in October 2021, the same week he published “Not All Vector Databases Are Made Equal”, his side-by-side survey of Milvus, Pinecone, Vespa, Weaviate, Vald, GSI and Qdrant.

Both landed exactly as the vector database category was forming, and the podcast’s stated premise was to talk with the creators and makers of this new search wave. Its self-description is broader than the name suggests — “the depth and breadth of Search Engine Technology, Product, Marketing, Business” — and in practice episodes range from ANN index internals to consulting economics and how to build a search career.

38 episodes as of August 2026. Format: one guest, roughly an hour, engineers and founders rather than analysts. Later seasons add on-site interview batches recorded at Berlin Buzzwords. Official partner of Trey Grainger’s AI-Powered Search.

Why It Matters Here

The back catalogue is a primary-source timeline of the vector search wave rather than a retrospective on it. The founders and engineers behind Weaviate, Milvus, Pinecone and Qdrant were interviewed while those products were still being built, and Yury Malkov — co-author of HNSW, the algorithm most of them rest on — appears in January 2022, before it was the default answer.

It sits alongside the meetups and Slack groups catalogued in Search Communities as one of the field’s standing knowledge channels: a small, specialized discipline where much of the practice is transmitted by conversation rather than publication.

Selected Episodes

DateEpisodeVault links
Aug 2026Berlin Buzzwords 2026 — Charlie Hull on the role of search in the AI eraCharlie Hull · Berlin Buzzwords
Jun 2026Berlin Buzzwords 2026 — Trey Grainger & Doug Turnbull, role of search in modern AITrey Grainger · Doug Turnbull
Nov 2025Trey Grainger — Wormhole VectorsWormhole Vectors · Hybrid Search
Sep 2025Simon Eskildsen (Turbopuffer) — economical vector search workloadsEconomics of Search
Mar 2025Daniel Wrigley & Eric Pugh — adding an ML layer to search: Hybrid Search OptimizerDaniel Wrigley · Hybrid Search
Jan 2025Leo Boytsov (AWS) — debunking myths of vector search and LLMsDense Vector Retrieval
Nov 2024Berlin Buzzwords 2024 — Alessandro Benedetti, LLMs in SolrSolr
Jul 2024Berlin Buzzwords 2024 — Doug Turnbull, learning in publicDoug Turnbull
Jun 2024Eric Pugh — measuring search quality with QuepidQuepid · Search Evaluation
May 2023Atita Arora — revolutionizing e-commerce with vector searchAtita Arora · E-commerce Search
Jan 2023Evgeniya Sukhodolskaya (Toloka) — data at the core of MLEvgeniya Sukhodolskaya
Oct 2022Doug Turnbull (Shopify) — search as a constant experimentation cycleShopify · A-B Testing for Search
Jun 2022Max Irwin — economics of scale in embedding computation (Mighty)Max Irwin · Embeddings
May 2022Daniel Tunkelang — leveraging ML for query and content understandingDaniel Tunkelang · Query Understanding
Apr 2022Jo Bergum — the journey of Vespa from sparse into neural searchJo Kristian Bergum · Vespa
Jan 2022Yury Malkov — author of the most adopted ANN algorithm, HNSWHNSW · Approximate Nearest Neighbor Search
Jan 2022Joan Fontanals — Jina AIJina AI
Dec 2021Bob van Luijt — the Weaviate vector search engineWeaviate · Weaviate Vector DB
Dec 2021Filip Haltmayer (Zilliz) — the Milvus vector databaseMilvus Vector DB
Dec 2021Greg Kogan (Pinecone) — the first episode, announced on Medium in October 2021Pinecone · Pinecone Vector DB

Full list on the YouTube channel and in the RSS feed.

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