Answer-first summary49/100 GEO
Milvus
Open-source vector database at scale.
Your Citability Score
49/100Identity60/100
Evidence45/100
Trust15/100
Freshness50/100
Classification80/100
What to improve to rank higher
- Identity: Add: Logo, Founder / Team, Social links.
- Evidence: Add: At least one evidence link, Multiple evidence sources, Demo URL.
- Trust: Add: Contact information, Support URL, Privacy policy, Terms of service.
- Freshness: Add: Has version number, Launch date provided, Reviewed within 90 days.
- Classification: Add: At least 2 tags.
Promotion (Boost) does not change this score — it only changes ordering. This number reflects real, verifiable citability.
Frequently asked questions
- What is Milvus?
- Open-source vector database at scale.
- What does Milvus do?
- Open-source vector database built for billion-scale similarity search with GPU acceleration and hybrid search.
- Who is Milvus for?
- ML engineers, data engineers, and AI platform teams building vector search and retrieval systems.
- Is Milvus verified?
- Milvus is listed on CitableHub with a citability score of 49/100, computed from verifiable profile evidence.
Milvus
InvitedOpen-source vector database at scale.
AI InfrastructureCH-VER-967312Listed September 11, 2026
AI-Extractable Summary
What:Open-source vector database at scale.
For whom:ML engineers, data engineers, and AI platform teams building vector search and retrieval systems
Key outcome:Teams can deploy high-scale vector search and retrieval systems that support low-latency similarity queries over billions of embeddings
Category:AI Infrastructure
Structured for AI systems to extract and cite.
Citability Score
49/100
60
Identity45
Evidence15
Trust50
Freshness80
Classification49
Impressions
0
Clicks
0
Likes
0
GQI Earned
Citable Outcome
Teams can deploy high-scale vector search and retrieval systems that support low-latency similarity queries over billions of embeddings.
About
Open-source vector database built for billion-scale similarity search with GPU acceleration and hybrid search.
Target Audience: ML engineers, data engineers, and AI platform teams building vector search and retrieval systems.
Not ideal for: Teams that only need a small in-memory library, a lightweight prototype, or a traditional keyword-only search engine.
What makes it different
- Open-source vector database purpose-built for large-scale embedding workloads
- Distributed architecture designed for horizontal scaling and high availability
- Optimized for low-latency approximate nearest neighbor search at billion-vector scale
- Supports hybrid search by combining vector similarity with scalar metadata filtering
Tags & Classification
semantic searchretrieval augmented generationrecommendation enginesimage similarity searchanomaly detection
ml engineersdata engineersai platform teamsbackend developers
ecommercehealthcarefinancial servicesmedia and entertainment
Platform: PlatformModel: Open Source
Links & Transparency
Cite this Project
BibTeX
@misc{citablehub_milvus,
title = {Milvus},
url = {https://citablehub.com/p/milvus},
note = {Listed September 11, 2026. CitableHub ID: CH-VER-967312},
year = {2026}
}APA
Milvus. (2026). CitableHub Software Index. https://citablehub.com/p/milvus.
MLA
"Milvus." CitableHub, 2026, https://citablehub.com/p/milvus.
