Zilliz, the company behind the leading open-source vector database Milvus, recently announced the General Availability (GA) of Milvus 2.6.x on Zilliz Cloud. This major update not only brings the architectural breakthroughs of the latest Milvus release to a fully managed environment but also introduces a series of cloud-only optimizations, offering developers and enterprises a streamlined path to building production-grade Generative AI applications with significantly reduced Total Cost of Ownership (TCO).
As organizations transition AI projects from prototypes to large-scale production, infrastructure costs and operational complexity have become critical bottlenecks. Milvus 2.6.x on Zilliz Cloud addresses these challenges head-on by combining the power of the core vector engine with the elasticity and ease of use of a cloud-native platform.
“The next phase of AI adoption requires infrastructure that is not only fast but also economically viable at scale,” said James Luan, VP of Engineering at Zilliz. “With the launch of Milvus 2.6.x on our fully managed, production-ready service, we are democratizing access to high-performance vector search. We have engineered this release to ensure that your engineering teams focus on innovation rather than infrastructure maintenance.”
Key Capabilities of Milvus 2.6.x on Zilliz Cloud
Three-Layer Tiered Storage with Near-S3 Economics
Zilliz Cloud introduces a cloud-native multi-layer storage architecture that automatically places data across memory, local SSD, and object storage based on access patterns. Hot data stays fast, cold data stays c****—delivering over 90% cache hit rates in production and up to 87% lower storage costs with reduced compute overhead.
Index Build Level for Cost-Aware Accuracy Control
Zilliz Cloud adds Index Build Level, allowing teams to automatically balance recall, performance, and storage efficiency. Workloads can prioritize precision, choose a balanced profile, or optimize for capacity, while a built-in quantization engine manages index compression without manual tuning.
High-Performance Metadata Filtering for AI Workloads
With JSON Shredding and JSON Path indexing, Milvus 2.6.x on Zilliz Cloud accelerates metadata filtering by up to 100×. This enables fast, structured filtering alongside vector search in a single system—critical for RAG pipelines, recommendations, and multi-tenant applications.
Hybrid BM25-optimized Full-Text and Vector Search
Zilliz Cloud now delivers even more enhanced full-text search, offering up to 7× faster performance than Elasticsearch on selected datasets and significantly smaller indexes. This unifies keyword and semantic retrieval while simplifying production architectures.
Expanded Data Types and Multilingual Retrieval for More AI Use Cases
Milvus 2.6.x on Zilliz Cloud adds native support for spatial data, timezone-aware timestamps, INT8 vectors, and nested structures, alongside improved multilingual tokenization and phrase search—enabling complex, global AI applications without external systems.
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