Zilliz

Zilliz Cloud is an enterprise-grade fully managed service built by the original team behind the open-source vector database Milvus (43,000+ GitHub stars). In May 2026 it evolved into a Vector Lakebase platform, supporting 100 billion+ vector entities and designed for production-grade AI applications.

Core Capabilities

• Extreme performance: The proprietary Cardinal engine delivers sub-10ms latency, query throughput 10x faster than open-source alternatives, and QPS up to 10,000+
• Full-spectrum search: Unified vector search, full-text search (BM25 is 4x faster than Elasticsearch), JSON filtering, geospatial queries, and hybrid retrieval
• Lakehouse architecture: Zero-copy access to your S3 data (supports Iceberg, Lance, Parquet), no ETL required, with separated storage and compute
• Cost optimization: A tiered storage architecture cuts storage costs by 87% ($0.04/GB/month), and on-demand compute avoids idle resources
• Enterprise compliance: SOC 2 Type II, ISO 27001, and HIPAA certified, with CMEK, VPC peering, and private endpoints
• Open-source DNA: Based on Apache 2.0-licensed Milvus, supports BYOC deployment with no vendor lock-in

Use Cases AI engineers building production-grade RAG systems, recommendation engines, image/video retrieval, and multimodal search; enterprises processing hundreds of millions of vectors or more; regulated industries (healthcare, finance) needing enterprise compliance; and teams migrating from other vector databases that hit cost or scale bottlenecks.

Unique Advantages The proprietary Cardinal engine is the core differentiating technology, outperforming comparable products. AWS Advanced Technology Partner certification and a G2 double crown (highest performance + easiest to use). JSON Shredding technology delivers 10x filtering performance — a key reason Reddit chose Milvus over Qdrant. Prices were sharply reduced in 2026, with a 10TB dataset's monthly cost dropping from $3,000 to $400.

Editor's Review Zilliz represents the enterprise benchmark for vector databases. For teams that need to process large-scale vectors (hundreds of millions and up) and demand extreme performance and enterprise compliance, this is the first-choice option. Users report search times dropping from 8 seconds to under 1 second, with a G2 rating of 4.7/5 (92% 5-star). But scenarios with fewer than 50 million vectors may find it overkill, and the learning curve is relatively steep. If your AI application is moving from prototype to production, this is an infrastructure choice worth deep evaluation.