[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-zilliz-vector-lakebase-unified-ai-data-platform-en":3,"article-related-zilliz-vector-lakebase-unified-ai-data-platform-en":32,"series-industry-372f6e06-007b-4110-93dc-851c736aaae9":77},{"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":24,"views":28,"created_at":29,"published_at":30,"topic_cluster_id":31},"372f6e06-007b-4110-93dc-851c736aaae9","zilliz-vector-lakebase-unified-ai-data-platform-en","Zilliz Vector Lakebase turns vector search into one platform","\u003Cp data-speakable=\"summary\">Vector Lakebase combines vector search, storage, and analytics in one AI data platform.\u003C\u002Fp>\u003Cp>Zilliz says its new Vector Lakebase extends the company’s \u003Ca href=\"\u002Ftag\u002Fvector-database\">vector database\u003C\u002Fa> into a broader AI data platform, giving teams one place to store, search, and analyze data across retrieval and \u003Ca href=\"\u002Fnews\u002Fset-up-ai-agent-workflows-5-practical-steps-en\">agent workflows\u003C\u002Fa>. The launch builds on a market where vector databases are already core infrastructure for AI, and Zilliz points to its roots in the VLDB 2022 paper “Manu: A Cloud Native Vector Database.”\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Item\u003C\u002Fth>\u003Cth>What it adds\u003C\u002Fth>\u003Cth>Best fit\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Zilliz Vector Lakebase\u003C\u002Ftd>\u003Ctd>Vector search + storage + analytics\u003C\u002Ftd>\u003Ctd>Teams building AI apps with mixed data needs\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Zilliz Cloud\u003C\u002Ftd>\u003Ctd>Managed vector database service\u003C\u002Ftd>\u003Ctd>Teams that want hosted Milvus infrastructure\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>\u003Ca href=\"https:\u002F\u002Fmilvus.io\u002F\">Milvus\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd>Open source vector database\u003C\u002Ftd>\u003Ctd>Teams that need self-managed control\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>\u003Ca href=\"https:\u002F\u002Fwww.zilliz.com\u002F\">Zilliz\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd>Vendor platform and services\u003C\u002Ftd>\u003Ctd>Organizations standardizing on one AI data stack\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>1. Vector Lakebase\u003C\u002Fh2>\u003Cp>Vector Lakebase is the headline move: Zilliz is packaging vector retrieval, data storage, and analytics into a single platform instead of treating them as separate systems. That matters for AI teams that want fewer handoffs between ingestion, retrieval, and downstream analysis.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782339470615-jdtd.png\" alt=\"Zilliz Vector Lakebase turns vector search into one platform\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>The practical angle is simpler architecture. Rather than stitching together a vector store, a lake, and an analytics layer, teams can keep more of the workflow inside one vendor stack.\u003C\u002Fp>\u003Cul>\u003Cli>Vector search for semantic retrieval\u003C\u002Fli>\u003Cli>Data storage for AI-ready content\u003C\u002Fli>\u003Cli>Analytics for inspection and reporting\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>2. Zilliz Cloud\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.zilliz.com\u002F\">Zilliz Cloud\u003C\u002Fa> is the managed path for teams that want vector infrastructure without running it themselves. It is the most direct way to adopt Zilliz’s database technology if your team values hosted operations over self-management.\u003C\u002Fp>\u003Cp>For product teams, the appeal is speed to production. You can focus on embedding pipelines, retrieval logic, and app behavior while the service handles the database layer.\u003C\u002Fp>\u003Cul>\u003Cli>Managed deployment model\u003C\u002Fli>\u003Cli>Operational overhead shifted to the vendor\u003C\u002Fli>\u003Cli>Useful for production AI search and RAG apps\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>3. Milvus\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fmilvus.io\u002F\">Milvus\u003C\u002Fa> remains the \u003Ca href=\"\u002Fnews\u002Fopen-source-agent-orchestrators-parallel-coding-autonomy-en\">open source\u003C\u002Fa> core most people associate with Zilliz. If your organization wants more control over deployment, tuning, and infrastructure choices, Milvus is the option that keeps the stack in-house.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782339473775-vmfb.png\" alt=\"Zilliz Vector Lakebase turns vector search into one platform\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>That makes it a better fit for teams with platform engineering resources or strict governance rules. It also gives developers a familiar vector database foundation if they want to build around \u003Ca href=\"\u002Fnews\u002Fun-open-source-week-2026-ai-priorities-en\">open source\u003C\u002Fa> instead of a fully managed service.\u003C\u002Fp>\u003Cul>\u003Cli>Open source vector database\u003C\u002Fli>\u003Cli>Self-managed deployment options\u003C\u002Fli>\u003Cli>Suitable for custom infrastructure policies\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>4. The VLDB paper lineage\u003C\u002Fh2>\u003Cp>Zilliz is also anchoring the announcement in research credibility, citing its VLDB 2022 paper, “Manu: A Cloud Native Vector Database.” That matters because it shows the product line did not appear overnight; it evolved from a published systems design with academic review behind it.\u003C\u002Fp>\u003Cp>For buyers, this is a signal about engineering depth. It suggests the company is trying to connect the new platform story to the database architecture work that made its earlier products credible in the first place.\u003C\u002Fp>\u003Cul>\u003Cli>Paper title: “Manu: A Cloud Native Vector Database”\u003C\u002Fli>\u003Cli>Conference: VLDB 2022\u003C\u002Fli>\u003Cli>Useful signal for technical buyers evaluating vendor maturity\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>5. Unified AI data platform\u003C\u002Fh2>\u003Cp>The bigger claim is not just that Zilliz added another product, but that it wants to be the place where AI data lives and moves. A unified platform can reduce duplication across teams that currently split embeddings, object storage, and analytics across separate tools.\u003C\u002Fp>\u003Cp>That is especially relevant for retrieval-augmented generation, \u003Ca href=\"\u002Ftag\u002Fagent\">agent\u003C\u002Fa> systems, and search products, where the same data often needs to be indexed, queried, and inspected in multiple ways. Zilliz is betting that one platform is easier to operate than a patchwork of specialized services.\u003C\u002Fp>\u003Cul>\u003Cli>One platform for ingestion and retrieval\u003C\u002Fli>\u003Cli>Cleaner path for RAG and agent workflows\u003C\u002Fli>\u003Cli>Less tool sprawl across data and AI teams\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>How to decide\u003C\u002Fh2>\u003Cp>If you want the least operational work, start with Zilliz Cloud and see whether the managed path fits your app and compliance needs. If you want full control, Milvus is the better choice. If your team is trying to simplify a stack that already mixes vector search, storage, and analytics, Vector Lakebase is the product to watch.\u003C\u002Fp>\u003Cp>For technical buyers, the decision comes down to control versus consolidation. Zilliz is offering both, but Vector Lakebase is clearly aimed at teams that want one AI data layer instead of several separate systems.\u003C\u002Fp>","Vector Lakebase adds a unified AI data layer to Zilliz, combining vector search, object storage, and analytics in one platform.","www.businesswire.com","https:\u002F\u002Fwww.businesswire.com\u002Fnews\u002Fhome\u002F20260621822926\u002Fen\u002FZilliz-Launches-Vector-Lakebase-Extending-the-Worlds-Most-Adopted-Vector-Database-into-a-Unified-Data-Platform-for-AI",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782339470615-jdtd.png","industry","en","a7139e35-f8f1-498d-84d8-5a3c1c7c0192",[17,18,19,20,21,22,23],"Zilliz","Vector Lakebase","Milvus","vector database","AI data platform","Zilliz Cloud","VLDB 2022",[25,26,27],"Vector Lakebase expands Zilliz from vector search into a broader AI data platform.","Milvus is still the open source option for teams that want self-managed control.","The launch targets teams trying to reduce tool sprawl across retrieval, storage, and analytics.",0,"2026-06-24T22:17:21.447456+00:00","2026-06-24T22:17:21.44+00:00","d19fc184-5852-4c4d-9ec0-db0c4841ac17",{"tags":33,"relatedLang":36,"relatedPosts":40},[34],{"name":20,"slug":35},"vector-database",{"id":15,"slug":37,"title":38,"language":39},"zilliz-vector-lakebase-unified-ai-data-platform-zh","Zilliz 把向量搜尋收進單一 AI 資料層","zh",[41,47,53,59,65,71],{"id":42,"slug":43,"title":44,"cover_image":45,"image_url":45,"created_at":46,"category":13},"15f7894f-9382-43bd-adef-effd2c22f4e8","cloudflare-rivals-web-security-infrastructure-en","Cloudflare’s main rivals and what each one does 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Wales","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782345768727-o94m.png","2026-06-25T00:02:22.940982+00:00",{"id":66,"slug":67,"title":68,"cover_image":69,"image_url":69,"created_at":70,"category":13},"b10159ab-4111-48da-bc2a-f64cbff423ef","chen-liwu-intel-packaging-materials-podcast-en","陈立武把英特尔改成材料公司","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782342206068-n3vd.png","2026-06-24T23:02:57.988319+00:00",{"id":72,"slug":73,"title":74,"cover_image":75,"image_url":75,"created_at":76,"category":13},"118ac217-a559-4f44-b0d2-70e1ef77e7f3","apples-gemini-siri-deal-ai-app-strategy-en","Apple’s Gemini Siri deal rewrites AI app 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