--- title: "Vector search database: news & 2026 guide" url: https://daily.dev/posts/vector-search-database-news-2026-guide-jn2fx26te source_url: https://redis.io/blog/vector-search-database-news-2026-guide type: article source: "Redis" published: 2026-08-20T01:01:15.664Z updated: 2026-08-20T01:06:20.821Z tags: ["ai-agents", "rag", "redis", "vector-search"] reading_time: 9 upvotes: 0 comments: 0 language: 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. # Vector search database: news & 2026 guide **[Redis](https://daily.dev/sources/redislabs)** · 9 min read · 0 upvotes · 0 comments ## Summary A vendor-authored overview of the vector search database landscape covers how vector embeddings and approximate nearest neighbor algorithms like HNSW work, then surveys 2025-2026 developments: vectors becoming a data type inside multimodel databases, AWS S3 vector buckets cutting RAG storage costs, purpose-built vector databases losing adoption share, and hybrid retrieval (dense plus BM25) becoming the default enterprise strategy. It outlines evaluation criteria for production vector databases (hybrid search, filtered query performance, tail latency, freshness) and positions Redis, including its FT.HYBRID command, HNSW/FLAT indexes, SVS-based compression, and Redis Iris platform (LangCache semantic caching, Agent Memory), as meeting those criteria. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Is hybrid search (dense vector plus keyword) replacing pure vector search for RAG in production? Yes, hybrid retrieval combining dense vector search with keyword or BM25 search has become the consensus enterprise strategy for RAG rather than pure vector search. In one benchmark of 23,088 queries, hybrid retrieval fused with Reciprocal Rank Fusion reported 0.695 Recall@5 versus 0.587 for dense retrieval alone, and results are typically reranked afterward. _Track shifts like hybrid retrieval adoption before they reshape your RAG stack, with daily.dev._ ### Are standalone purpose-built vector databases losing ground to multimodel databases with vector support? Yes, purpose-built vector databases have lost adoption share among enterprise teams building retrieval-augmented generation, as teams increasingly treat vectors as just another data type inside existing multimodel databases rather than standing up a separate specialized store. Gartner projects that by 2028, 80% of GenAI apps will be built on existing data management platforms rather than new specialized ones. _Weigh consolidated versus dedicated vector storage with current comparisons on daily.dev._ ### What throughput and precision has Redis achieved for billion-scale vector search with HNSW? In a billion-vector benchmark, Redis reported 90% precision at approximately 200 ms median latency running 50 concurrent queries retrieving the top 100 neighbors each, and roughly 66,000 vector insertions per second at about 95% precision. Its Intel Scalable Vector Search-based compression also reported up to 144% higher query throughput for FP32 vectors at 0.95 precision in a separate benchmark. _Benchmark vector database options like this before committing your RAG infrastructure, via daily.dev._ ## Similar posts on daily.dev - [Vector Search: A Practical Guide for Developers](https://daily.dev/posts/vector-search-a-practical-guide-for-developers-wpvtprg29) · Redis · 0 upvotes · 0 comments - [Vector Databases Explained in 3 Levels of Difficulty](https://daily.dev/posts/vector-databases-explained-in-3-levels-of-difficulty-pg3waxjnd) · Machine Learning Mastery · 4 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#rag](https://daily.dev/tags/rag), [#redis](https://daily.dev/tags/redis), [#vector-search](https://daily.dev/tags/vector-search) [View this post on daily.dev](https://daily.dev/posts/vector-search-database-news-2026-guide-jn2fx26te)