[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-vector-databases-work-in-production-en":3,"article-related-vector-databases-work-in-production-en":33,"series-industry-d1119980-1ee8-49c9-8cda-c22e9d6e9cfd":78},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":25,"views":29,"created_at":30,"published_at":31,"topic_cluster_id":32},"d1119980-1ee8-49c9-8cda-c22e9d6e9cfd","vector-databases-work-in-production-en","Vector Databases That Work in Production","\u003Cp>Which \u003Ca href=\"\u002Ftag\u002Fvector-database\">vector database\u003C\u002Fa> should I use for production \u003Ca href=\"\u002Ftag\u002Frag\">RAG\u003C\u002Fa>?\u003C\u002Fp>\u003Cp data-speakable=\"summary\">Four production-ready vector database paths compared by filtering, latency, and build time.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Item\u003C\u002Fth>\u003Cth>Best fit\u003C\u002Fth>\u003Cth>Filtering\u003C\u002Fth>\u003Cth>Operational profile\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>PostgreSQL + pgvector\u003C\u002Ftd>\u003Ctd>Most teams\u003C\u002Ftd>\u003Ctd>Strong with SQL WHERE clauses\u003C\u002Ftd>\u003Ctd>Lowest ops overhead\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Elasticsearch + dense_vector\u003C\u002Ftd>\u003Ctd>Teams already on Elastic\u003C\u002Ftd>\u003Ctd>Very strong hybrid search\u003C\u002Ftd>\u003Ctd>Higher resource use\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Pinecone\u003C\u002Ftd>\u003Ctd>Managed scale\u003C\u002Ftd>\u003Ctd>Good, but vendor-tied\u003C\u002Ftd>\u003Ctd>Simple to start, pricier later\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Qdrant\u003C\u002Ftd>\u003Ctd>Low-latency search\u003C\u002Ftd>\u003Ctd>Best-in-class filtering\u003C\u002Ftd>\u003Ctd>Fast, smaller ecosystem\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>1. PostgreSQL + pgvector\u003C\u002Fh2>\u003Cp>For many teams, \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fpgvector\u002Fpgvector\">pgvector\u003C\u002Fa> is the safest first move because it keeps vector search inside a database you already know. You get SQL, ACID transactions, backups, and the ability to mix similarity search with normal filters in one query.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783846971639-3ywd.png\" alt=\"Vector Databases That Work in Production\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>The tradeoff is scale. Once you get into very large vector counts or need aggressive latency targets, PostgreSQL starts to feel like an extension of your app rather than a specialized search engine. That is fine for a lot of production RAG systems, especially when the real problem is hybrid retrieval, not raw ANN throughput.\u003C\u002Fp>\u003Cul>\u003Cli>Best when your team already runs PostgreSQL\u003C\u002Fli>\u003Cli>Works well for metadata-heavy RAG\u003C\u002Fli>\u003Cli>Easy to back up and replicate\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>2. Elasticsearch + dense_vector\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.elastic.co\u002Felasticsearch\u002F\">Elasticsearch\u003C\u002Fa> is a strong choice when keyword search already matters and vector search is just one more retrieval signal. Its dense_vector support and Lucene-based BM25 make hybrid search feel native, which is useful for legal, support, and product search workloads.\u003C\u002Fp>\u003Cp>The cost is operational heft. Elasticsearch can be resource-hungry, and you will pay for that convenience in memory, storage, and tuning time. If your team already depends on Elastic, this is a practical path; if not, it is often more system than you need.\u003C\u002Fp>\u003Cul>\u003Cli>Best hybrid search story in an existing search stack\u003C\u002Fli>\u003Cli>Good for exact terms plus semantic matching\u003C\u002Fli>\u003Cli>Heavier infrastructure footprint than pgvector\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>3. Pinecone\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.pinecone.io\u002F\">Pinecone\u003C\u002Fa> is the easiest managed option to get running, which is why many teams use it for their first serious vector deployment. The serverless model removes a lot of setup work, and the developer experience is polished enough that you can move quickly.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783846972173-modw.png\" alt=\"Vector Databases That Work in Production\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>The downside is economics and control. Pinecone gets expensive as usage grows, and you do not get a self-hosted escape hatch. If you want minimal infrastructure work and can accept vendor lock-in, it is a clean option; if you need deep tuning or cost control, it can become hard to justify.\u003C\u002Fp>\u003Cul>\u003Cli>Fastest path from prototype to production\u003C\u002Fli>\u003Cli>Managed infrastructure with low setup overhead\u003C\u002Fli>\u003Cli>Can become costly at scale\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>4. Qdrant\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fqdrant.tech\u002F\">Qdrant\u003C\u002Fa> is the pick for teams that care most about latency and filtering behavior. It is written in \u003Ca href=\"\u002Ftag\u002Frust\">Rust\u003C\u002Fa>, and that shows up in query speed and predictable performance under load. The article source also notes that its filtering is among the strongest in the category.\u003C\u002Fp>\u003Cp>What you give up is ecosystem depth. Qdrant has fewer integrations and less community gravity than Pinecone or Weaviate, so your team may need to do a bit more on its own. Still, if your workload is filter-heavy and speed matters more than brand familiarity, Qdrant is easy to take seriously.\u003C\u002Fp>\u003Cul>\u003Cli>Strong choice for low-latency retrieval\u003C\u002Fli>\u003Cli>Good when filters are part of every query\u003C\u002Fli>\u003Cli>Smaller ecosystem than the biggest vendors\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>5. Weaviate\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fweaviate.io\u002F\">Weaviate\u003C\u002Fa> is the most balanced dedicated vector database in this set because it handles hybrid search well without making it feel bolted on. The source article calls out its ability to combine vector and keyword search in a single query, which is exactly what many RAG systems need.\u003C\u002Fp>\u003Cp>It is also mature enough for real deployments, including larger collections. The main reason to choose it is not a single \u003Ca href=\"\u002Ftag\u002Fbenchmark\">benchmark\u003C\u002Fa> win but the combination of good hybrid search, solid client tooling, and a deployment story that teams can live with over time.\u003C\u002Fp>\u003Cul>\u003Cli>Strong hybrid search support\u003C\u002Fli>\u003Cli>Good fit for document retrieval apps\u003C\u002Fli>\u003Cli>Balanced mix of features and maturity\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>How to decide\u003C\u002Fh2>\u003Cp>If you want the least risky default, start with PostgreSQL + pgvector. If search is already central to your stack, Elasticsearch is the natural extension. If you want managed simplicity, Pinecone gets you there fastest. If latency and filtering are the main pain points, Qdrant deserves a hard look, while Weaviate is the best all-around dedicated option for hybrid retrieval.\u003C\u002Fp>\u003Cp>The source article’s benchmark advice is worth following: test recall@10, filtered query latency, and index build time on your own embeddings. Public scores are useful, but production behavior is what decides whether the system stays useful after launch.\u003C\u002Fp>","4 production-ready vector database paths, compared on filtering, latency, and build time for real RAG workloads.","sivaro.in","https:\u002F\u002Fsivaro.in\u002Farticles\u002Fvector-database-comparison-2026-what-actually-works-in\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783846971639-3ywd.png","industry","en","6e790897-c9af-402c-a928-f2b0cc02f4e6",[17,18,19,20,21,22,23,24],"vector database","pgvector","elasticsearch","pinecone","qdrant","weaviate","RAG","hybrid search",[26,27,28],"pgvector is the safest default for teams already on PostgreSQL.","Qdrant is the best fit when filtered query latency is the top concern.","Pinecone is easiest to start with, but long-term cost can rise quickly.",0,"2026-07-12T09:02:23.486712+00:00","2026-07-12T09:02:23.479+00:00","92b57db9-5bb1-4f4f-a18e-ea0d789f7bd9",{"tags":34,"relatedLang":37,"relatedPosts":41},[35],{"name":17,"slug":36},"vector-database",{"id":15,"slug":38,"title":39,"language":40},"vector-databases-work-in-production-zh","4 種能上線的向量資料庫選擇","zh",[42,48,54,60,66,72],{"id":43,"slug":44,"title":45,"cover_image":46,"image_url":46,"created_at":47,"category":13},"dea6021b-f740-4ccb-b3b6-6f2c09c64414","opensearch-vector-search-benchmark-5-parts-en","OpenSearch’s vector search benchmark in 5 parts","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783850566828-irjz.png","2026-07-12T10:02:22.749784+00:00",{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":13},"ab709f35-6efc-46f5-a4f7-620ecafd8b42","eu-ai-act-hits-business-systems-aug-2-2026-en","EU AI Act hits business systems on Aug. 2, 2026","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783845181822-ltkc.png","2026-07-12T08:32:24.922422+00:00",{"id":55,"slug":56,"title":57,"cover_image":58,"image_url":58,"created_at":59,"category":13},"ce6a6c1a-7b80-49c6-9f50-57303c322335","us-ai-law-2026-compliance-overview-en","US AI law in 2026: what teams must track","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783843394912-rmzm.png","2026-07-12T08:02:51.043257+00:00",{"id":61,"slug":62,"title":63,"cover_image":64,"image_url":64,"created_at":65,"category":13},"108a5b32-e52f-4e35-935c-6f1269ef188b","webx-2026-agenda-stablecoins-ai-speakers-sponsors-en","WebX 2026’s agenda centers on stablecoins and AI","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783841566991-twsg.png","2026-07-12T07:32:24.674229+00:00",{"id":67,"slug":68,"title":69,"cover_image":70,"image_url":70,"created_at":71,"category":13},"8e744e2e-f133-4a0f-8953-e3468010030f","gpt-5-6-best-coding-model-chatgpt-work-en","GPT-5.6 Is OpenAI’s Best Coding Model, But the Real Story Is ChatGPT …","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783837978818-pa0c.png","2026-07-12T06:32:32.961207+00:00",{"id":73,"slug":74,"title":75,"cover_image":76,"image_url":76,"created_at":77,"category":13},"3e0651b7-5a37-4615-8462-5c695356154f","half-price-ai-real-frontier-smarter-models-en","Half-price AI is the real frontier, not smarter 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