Observed Signal · Aug 24, 2026 · Market Signal · Source: Milvus · Impact: 3/5
Milvus External Collection: Index and Retrieve Lake-Resident Data Without Moving It
Engineering Aug 24, 2026 - Milvus External Collection: Index and Retrieve Lake-Resident Data Without Moving It
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Zilliz Releases Milvus 3.0, Lake-Native Vector Database
Zilliz announced Milvus 3.0, a major technical release that makes the open-source Milvus vector database lake-native and expands its retrieval engine. Milvus 3.0 enables indexing and serving directly over object storage and open data formats, introduces a new manifest-based storage engine (Loon), External Collections for formats like Lance and Iceberg, snapshots, a Spark connector, and a more expressive retrieval stack including StructList for multi-vector retrieval and an optimized sparse index. The release is Apache 2.0 licensed, remains a graduated LF AI & Data project, supports Kubernetes/Docker and major cloud object stores, and is available via Python, Go, and Node.js SDKs. Zilliz Cloud will use Milvus 3.0 as its managed Vector Lakebase core.
Zilliz Launches Vector Lakebase for Unified AI Data Platform
Zilliz announced the public preview of Vector Lakebase on June 22, 2026. Available now on Zilliz Cloud, Vector Lakebase pairs the Milvus production vector database with a shared, lake-native storage foundation (built on Vortex) to enable zero-copy workflows across real-time serving, interactive discovery, and large-scale batch analytics. Key capabilities include tiered real-time serving tiers, on-demand pay-as-you-go search, a zero-copy External Collection mode for indexing Lance/Iceberg/Parquet/Vortex tables, full-spectrum AI search (dense/sparse vectors, text, JSON, geospatial), and unified lake-native storage with object-storage-aware indexes. Zilliz positions the product to remove data duplication and reduce cost/complexity for AI teams and cites existing Milvus/Zilliz Cloud customers and more than 10,000 enterprise users. The release emphasizes compute-that-scales-to-zero billing and integrations with third-party rerankers like Cohere and Voyage AI.
Vector Database vs Knowledge Graph for LLMs
A practical guide comparing vector databases and knowledge graphs as storage backends for LLM applications. The article explains that vector databases (e.g., Qdrant, pgvector, Pinecone) store embeddings and excel at semantic similarity queries, while knowledge graphs (e.g., Neo4j, RDF stores) model entities and relationships and support exact, multi-hop relational queries. The author argues most real-world LLM systems need a hybrid: vectors for retrieval and graphs for precise reasoning and auditability (a pattern exemplified by Microsoft’s GraphRAG). Tool-level trade-offs, operational costs, and a four-question decision rule are provided to help teams choose the right architecture for their queries.
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