--- title: Decoupled by Design: Billion-Scale Vector Search | daily.dev description: Databricks redesigned its vector search infrastructure to handle billion-scale datasets by decoupling storage from compute. The new Storage Optimized endpoints... canonical: https://daily.dev/posts/decoupled-by-design-billion-scale-vector-search-puyxn3eah twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Decoupled by Design: Billion-Scale Vector Search | daily.dev og:description: Databricks redesigned its vector search infrastructure to handle billion-scale datasets by decoupling storage from compute. The new Storage Optimized endpoints... og:url: https://daily.dev/posts/decoupled-by-design-billion-scale-vector-search-puyxn3eah og:image: https://api.daily.dev/og/posts/pUyXn3eaH.png og:image:alt: Decoupled by Design: Billion-Scale Vector Search og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Decoupled by Design: Billion-Scale Vector Search **[databricks](https://daily.dev/sources/databricks)** · 15 min read · 26 upvotes · 0 comments ## Summary Databricks redesigned its vector search infrastructure to handle billion-scale datasets by decoupling storage from compute. The new Storage Optimized endpoints use IVF (Inverted File Index) instead of HNSW, distributed K-means and Product Quantization built on PySpark with JAX, and a Rust-based dual-runtime query engine separating async I/O from CPU-bound computation. Key results: billion-vector indexes built in under 8 hours (20x faster), up to 7x lower serving costs, and 90%+ recall at 1 billion vectors. Query latency is ~300–500ms versus 20–50ms for the memory-resident Standard endpoints — a deliberate trade-off favoring scale and cost over ultra-low latency. The architecture relies on three interdependent bets: storage-compute separation, distributed indexing with a compatible index format, and aggressive compression via Product Quantization. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [The story of BigQuery vector search](https://daily.dev/posts/the-story-of-bigquery-vector-search-tmmmlqwoo) · Google Cloud · 1 upvotes · 0 comments - [ScyllaDB Releases Integrated Vector Search: 1B Vectors with 2ms P99s and 250K QPS Throughput](https://daily.dev/posts/scylladb-releases-integrated-vector-search-1b-vectors-with-2ms-p99s-and-250k-qps-throughput-f9apunvt1) · SD Times · 0 upvotes · 0 comments - [Scaling Vector Search Performance: From Millions to Billions](https://daily.dev/posts/scaling-vector-search-performance-from-millions-to-billions-c5dvchkfu) · BigData Boutique blog · 3 upvotes · 0 comments - [How to Integrate Vector Search in Columnar Storage](https://daily.dev/posts/how-to-integrate-vector-search-in-columnar-storage-srxkxulw1) · freeCodeCamp · 1 upvotes · 0 comments --- Tags: [#apache-spark](https://daily.dev/tags/apache-spark), [#rag](https://daily.dev/tags/rag), [#vector-search](https://daily.dev/tags/vector-search), [#databricks](https://daily.dev/tags/databricks) [View this post on daily.dev](https://daily.dev/posts/decoupled-by-design-billion-scale-vector-search-puyxn3eah) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Decoupled by Design: Billion-Scale Vector Search","url":"https://daily.dev/posts/decoupled-by-design-billion-scale-vector-search-puyxn3eah","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/decoupled-by-design-billion-scale-vector-search-puyxn3eah"},"datePublished":"2026-03-09T18:54:07.834Z","dateModified":"2026-03-09T18:54:38.071Z","description":"Databricks redesigned its vector search infrastructure to handle billion-scale datasets by decoupling storage from compute. 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