Milvus — Vector Databases Platform
Vector DatabasesLayer 2Open SourceWhat is Milvus?
Milvus is an open-source vector database built for scalable similarity search, capable of handling billions of vectors in distributed environments. Created by Zilliz, a company founded in 2017 by Charles Xie (former founding engineer of Oracle 12c cloud database), Milvus has become one of the most widely deployed vector databases with over 30,000 GitHub stars.
The database supports multiple index types including IVF, HNSW, and DiskANN, with GPU-accelerated search and hybrid search combining dense and sparse vectors in a single query. Milvus runs on Kubernetes for production deployments and is governed under the LF AI & Data Foundation. Zilliz Cloud offers a fully managed version with automatic scaling, starting with a free tier and usage-based pricing from $4 per million vector compute units.
Zilliz has raised approximately $113-132 million in funding, with a $60 million Series B extension in August 2022 led by Prosperity7 Ventures (Aramco). The company is headquartered in San Francisco with roughly 140 employees. Zilliz was named "Highest Performer" and "Easiest to Use" in G2's Summer 2025 Vector Database Grid Report.
Key Features
- ✓Billion-scale vector search
- ✓GPU-accelerated indexing
- ✓Distributed architecture
- ✓Multiple index types
- ✓Cloud and self-hosted options
Pros & Cons
Pros
- +Extreme scalability handling billions of vectors in distributed environments
- +Fully open-source under Apache 2.0 with no vendor lock-in
- +Hybrid search combining dense and sparse vectors in single queries
- +Rich index flexibility with IVF, HNSW, DiskANN and GPU acceleration
- +Managed cloud option reduces DevOps burden with auto-scaling
Cons
- -Complex distributed architecture requiring Kubernetes for production
- -Steep learning curve for index tuning and deployment configuration
- -High memory and storage requirements for large datasets
- -No in-place vector updates requiring delete and re-insert workarounds
Milvus Pricing
Free trial available
- ✓Apache 2.0 license
- ✓Self-hosted on Kubernetes
- ✓Full feature access
- ✓Community support
- ✓5GB storage
- ✓2.5M vCUs/month
- ✓Up to 5 collections
- ✓Up to 32 query CUs
- ✓Manual scaling
- ✓Email/ticket support
- ✓99.95% uptime SLA
- ✓SSO and RBAC
- ✓24/7 enterprise support
- ✓VPC peering
Common Use Cases
Organizations that need vector search at billion-scale with high throughput
- •Billion-vector similarity search
- •Large-scale recommendation systems
- •Image and video retrieval
- •Genomics and scientific computing
- •Enterprise-scale RAG systems
Using Milvus with Respan
Respan complements Milvus by providing observability for the LLM layer in RAG (Retrieval-Augmented Generation) pipelines. Teams using Milvus for vector search can monitor the end-to-end AI pipeline including embedding generation, retrieval quality, and LLM response generation through Respan.
- ✓Monitor RAG pipeline performance from embedding to generation
- ✓Track LLM costs in Milvus-powered retrieval workflows
- ✓Evaluate retrieval quality and LLM response accuracy
- ✓Optimize embedding model selection for vector search quality
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Last verified: February 28, 2026