LanceDB vs Milvus: Vector Databases Comparison
Compare LanceDB and Milvus side by side. Both are tools in the Vector Databases category.
Updated February 28, 2026
The short answer
Choose LanceDB if open-source and fully featured free tier (LanceDB OSS).
Choose Milvus if extreme scalability handling billions of vectors in distributed environments.
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Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | — | Open Source |
| Best For | — | Organizations that need vector search at billion-scale with high throughput |
| Website | lancedb.com | milvus.io |
| Key Features | — |
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| Use Cases | — |
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Pros & Cons: LanceDB vs Milvus
- +Open-source and fully featured free tier (LanceDB OSS)
- +Native multimodal support: text, images, videos, audio, point clouds
- +Up to 100x cost savings with disk-based compute-storage separation
- +Zero-copy automatic versioning and time-travel capabilities
- +Seamless transition from local (OSS) to cloud with same SDK
- −Relatively new platform with smaller ecosystem
- −Documentation still evolving compared to mature alternatives
- −Cloud tier pricing may add up with heavy usage
- −Smaller community support compared to established databases
- +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
- −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
Pricing: LanceDB vs Milvus
- · 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
When to Choose LanceDB vs Milvus
- Billion-vector similarity search
- Large-scale recommendation systems
- Image and video retrieval
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About LanceDB
LanceDB is an open-source, AI-native multimodal lakehouse designed for billion-scale vector search. Founded in 2022 by Chang She and Lei Xu as part of Y Combinator's Winter 2022 batch, LanceDB is built on the Lance columnar format and combines embedded simplicity with cloud-scale performance. The platform enables users to store, query, and filter vectors, metadata, and multi-modal data (text, images, videos, point clouds, and more) with support for vector similarity search, full-text search, and SQL. LanceDB offers blazing fast hybrid search, filter, and rerank over billions of vectors with compute-storage separation for up to 100x cost savings. The platform includes zero-copy automatic versioning, allowing users to manage versions of data without needing extra infrastructure. LanceDB's disk-based architecture with compute-storage separation enables up to 100x cost savings compared to memory-based solutions while supporting multimodal data. Based in San Francisco with approximately 30 employees, LanceDB hit $2.3M in revenue with a 15-person team in 2024.
About 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.
What is Vector Databases?
Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.
Browse all Vector Databasestools →Frequently Asked Questions
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