M4A

Milvus

Open-source vector database built for scalable AI applications

freemiumproductionopen-sourceenterprisedistributedgpu-acceleratedkubernetes
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Memory Types

semantic, contextual

Integrations

langchain, llamaindex, haystack, openai, towhee


Overview


Milvus is an open-source vector database designed for massive-scale AI applications. Created by Zilliz and having raised $113M in funding, Milvus has become one of the most popular vector databases in the enterprise space. It's built for scenarios requiring billion-scale vector search with GPU acceleration and cloud-native architecture.


The platform offers advanced features like GPU indexing, hybrid search, and time travel capabilities. Milvus uses a disaggregated architecture separating storage and compute, enabling independent scaling and cost optimization. It's particularly popular in large enterprises and research institutions handling massive datasets.


Key Features


  • **GPU Acceleration**: Dramatically faster indexing with GPU support
  • **Cloud-Native Architecture**: Kubernetes-native with disaggregated storage/compute
  • **Hybrid Search**: Combines vector, scalar filtering, and full-text search
  • **Time Travel**: Query historical data with MVCC
  • **Multiple Indexes**: HNSW, IVF, DiskANN, and more
  • **Distributed System**: Built for horizontal scaling
  • **Data Persistence**: Strong consistency guarantees
  • **Multi-Tenancy**: Resource isolation and quota management

    • When to Use Milvus


    Milvus is ideal for:

  • Enterprise applications with billion-scale vector datasets
  • Organizations requiring GPU acceleration for performance
  • Cloud-native deployments on Kubernetes
  • Applications needing strong consistency and ACID properties
  • Research institutions with massive similarity search needs
  • Multi-tenant platforms requiring resource isolation

    • Pros


  • Handles massive scale (billions of vectors)
  • GPU acceleration for faster indexing
  • Mature open-source project with large community
  • Enterprise-grade features and reliability
  • Strong backing from Zilliz with $113M funding
  • Excellent documentation and examples
  • Multiple deployment options (standalone, cluster, cloud)
  • Active development with frequent releases

    • Cons


  • Complex setup and operation compared to simpler alternatives
  • Resource-intensive (high memory and compute requirements)
  • Steeper learning curve
  • Overkill for small to medium datasets
  • GPU acceleration requires specialized hardware
  • Can be costly to run at scale

    • Pricing


  • **Open Source**: Free, self-hosted
  • **Zilliz Cloud (Managed)**: Pay-as-you-go starting at $0.15/hour
  • **Dedicated Cloud**: Starting at $300/month
  • **Enterprise**: Custom pricing with support and SLAs

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