WorkflowsApr 8, 2026·3 min read

Milvus — Scalable Vector Database for AI at Scale

Cloud-native vector database built for billion-scale AI search. Milvus offers GPU-accelerated indexing, hybrid search, multi-tenancy, and Kubernetes-native deployment.

AI
AI Open Source · Community
Quick Use

Use it first, then decide how deep to go

This block should tell both the user and the agent what to copy, install, and apply first.

# Docker Compose (standalone)
curl -sfL https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh | bash
from pymilvus import MilvusClient

client = MilvusClient("http://localhost:19530")

# Create collection
client.create_collection(
    collection_name="docs",
    dimension=1536,
)

# Insert vectors
client.insert(
    collection_name="docs",
    data=[
        {"id": 1, "vector": [0.1, 0.2, ...], "text": "AI is transforming software"},
        {"id": 2, "vector": [0.3, 0.4, ...], "text": "Python is popular for ML"},
    ],
)

# Search
results = client.search(
    collection_name="docs",
    data=[[0.15, 0.25, ...]],
    limit=5,
    output_fields=["text"],
)

What is Milvus?

Milvus is a cloud-native vector database designed for billion-scale similarity search. Built in Go and C++, it provides GPU-accelerated indexing, hybrid dense+sparse search, multi-tenancy, and Kubernetes-native deployment. Milvus is the backbone of production AI search at scale — used by companies processing billions of vectors with sub-second latency.

Answer-Ready: Milvus is a cloud-native vector database for billion-scale AI search. GPU-accelerated indexing, hybrid search (dense+sparse+full-text), multi-tenancy, and K8s deployment. Used by 10,000+ organizations. Zilliz Cloud for managed hosting. 32k+ GitHub stars.

Best for: Enterprise teams needing vector search at massive scale. Works with: OpenAI, Cohere, HuggingFace embeddings, LangChain, LlamaIndex. Setup time: Under 5 minutes.

Core Features

1. Multiple Index Types

Index Best For Speed
IVF_FLAT Small-medium datasets Good
IVF_SQ8 Memory-efficient Good
HNSW Low latency Fastest
GPU_IVF_FLAT GPU-accelerated Very fast
SCANN Balanced Very good

2. Hybrid Search

# Dense + Sparse + Full-text in one query
results = client.hybrid_search(
    collection_name="docs",
    reqs=[
        AnnSearchRequest(data=[[0.1, ...]], anns_field="dense_vector", limit=10),
        AnnSearchRequest(data=sparse_vector, anns_field="sparse_vector", limit=10),
    ],
    ranker=RRFRanker(),  # Reciprocal Rank Fusion
    limit=10,
)

3. Filtering

results = client.search(
    collection_name="docs",
    data=[[0.1, ...]],
    filter='category == "ai" and year >= 2024',
    limit=10,
)

4. Multi-Tenancy

# Partition key for tenant isolation
client.create_collection(
    collection_name="multi_tenant",
    dimension=1536,
    partition_key_field="tenant_id",
)

5. Deployment Options

Mode Scale Use Case
Lite (in-process) Dev/test Prototyping
Standalone Single node Small production
Distributed Multi-node K8s Billion-scale
Zilliz Cloud Managed Zero-ops production

Milvus vs Alternatives

Feature Milvus Qdrant Pinecone Weaviate
Scale Billions Millions Billions Millions
GPU indexing Yes No No No
Hybrid search Yes Yes No Yes
Multi-tenancy Native Namespace Namespace Class
Self-hosted Yes Yes No Yes
Managed cloud Zilliz Qdrant Cloud Yes WCS

FAQ

Q: How big can it scale? A: Billions of vectors across distributed nodes. Zilliz has customers with 10B+ vectors.

Q: Is there a managed version? A: Yes, Zilliz Cloud offers fully managed Milvus with free tier.

Q: Does it support GPU? A: Yes, GPU-accelerated indexing (IVF_FLAT, IVF_PQ) for 10x faster index building.

🙏

Source & Thanks

Created by Zilliz. Licensed under Apache 2.0.

milvus-io/milvus — 32k+ stars

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