Answer-first summary52/100 GEO
Qdrant
High-performance vector search engine.
Your Citability Score
52/100Identity75/100
Evidence45/100
Trust15/100
Freshness50/100
Classification80/100
What to improve to rank higher
- Identity: Add: 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 Qdrant?
- High-performance vector search engine.
- What does Qdrant do?
- Open-source vector similarity search engine with extended filtering, payloads, and production-ready deployment.
- Who is Qdrant for?
- Developers, ML engineers, and platform teams building vector search, semantic retrieval, and RAG systems.
- Is Qdrant verified?
- Qdrant is listed on CitableHub with a citability score of 52/100, computed from verifiable profile evidence.
Qdrant
InvitedHigh-performance vector search engine.
AI InfrastructureCH-VER-967285Listed September 10, 2026
AI-Extractable Summary
What:High-performance vector search engine.
For whom:Developers, ML engineers, and platform teams building vector search, semantic retrieval, and RAG systems
Key outcome:Users can retrieve semantically similar results in milliseconds at scale, improving search relevance, recommendations, and RAG quality for AI applications
Category:AI Infrastructure
Structured for AI systems to extract and cite.
Citability Score
52/100
75
Identity45
Evidence15
Trust50
Freshness80
Classification58
Impressions
0
Clicks
0
Likes
0
GQI Earned
Citable Outcome
Users can retrieve semantically similar results in milliseconds at scale, improving search relevance, recommendations, and RAG quality for AI applications.
About
Open-source vector similarity search engine with extended filtering, payloads, and production-ready deployment.
Target Audience: Developers, ML engineers, and platform teams building vector search, semantic retrieval, and RAG systems.
Not ideal for: Teams that only need basic keyword search or a simple relational database without vector similarity capabilities.
What makes it different
- High-performance vector search with low-latency similarity retrieval at production scale
- Rich metadata payload filtering for precise hybrid search and retrieval workflows
- Open-source core with flexible deployment options across self-hosted, cloud, and enterprise environments
- Built for AI workloads with support for embeddings, multimodal data, and scalable indexing
Tags & Classification
semantic searchrag retrievalrecommendation systemssimilarity searchimage and text matching
machine learning engineersbackend developersplatform engineersdata engineers
saasecommercemediahealthcare
Platform: PlatformModel: Open Source
Links & Transparency
Cite this Project
BibTeX
@misc{citablehub_qdrant,
title = {Qdrant},
url = {https://citablehub.com/p/qdrant},
note = {Listed September 10, 2026. CitableHub ID: CH-VER-967285},
year = {2026}
}APA
Qdrant. (2026). CitableHub Software Index. https://citablehub.com/p/qdrant.
MLA
"Qdrant." CitableHub, 2026, https://citablehub.com/p/qdrant.