What AI says about Qdrant
Data as of Oct 26, 2026Based on 32,853 AI responses
Reviewed by Dimitry Apollonsky
AI summary
Qdrant is an open-source vector search engine and database available for on-premise deployment and managed cloud use. It is among the brands most often named when developers need advanced metadata filtering, high-throughput vector search for production workloads, or open-source infrastructure without cloud subscriptions.
QdrantThis page8%
Products
- Cloud1%
Mentioned in
8%
of AI answers about Qdrant and its rivals · since Jul 5
The market map
Developer Backend & Vector Search ServicesMentioned in
- 1QdrantMentioned in 48%Avg. position 3.6
- 2PineconeMentioned in 41%Avg. position 4.3
- 2MilvusMentioned in 40%Avg. position 4.7
- 4WeaviateMentioned in 36%Avg. position 4.7
- 5SupabaseMentioned in 29%Avg. position 3.5
- 5pgvectorMentioned in 27%Avg. position 5.5
- 7FirebaseMentioned in 25%Avg. position 4.5
- 8PostgreSQLMentioned in 22%Avg. position 6.1
- 9AppwriteMentioned in 18%Avg. position 5.0
- 10AWSMentioned in 11%Avg. position 7.0
- 11ConvexMentioned in 9%Avg. position 5.1
- 11MongodbMentioned in 8%Avg. position 6.5
- 15ElasticsearchMentioned in 6%Avg. position 7.6
- 15Zilliz CloudMentioned in 6%Avg. position 5.6
Where Qdrant ranks in AI
MarketRank · Mentioned in
- Developer Backend & Vector Search Services#1 · 48%
- Vector Database Platforms#1 · 83%
- Visual Search and Auto-Tagging APIs#3 · 23%
- RAG Platforms and Retrieval Tools#7 · 19%
- Product Search and Discovery Platforms#8 · 13%
- Embedding Model APIs and Services#12 · 9%
- LLM Security and Governance Platforms#13 · 7%
- Enterprise MLOps Model Registry Platforms#26 · 2%
- AI Document Processing and OCR Tools#27 · 4%
- AI Knowledge Base Assistant Bots#32 · 1%
- LLM Fine-Tuning and Hosting Platforms#38 · 4%
- Enterprise Cloud Storage & Data Platforms#40 · 3%
Topics where AI mentions Qdrant
TopicMentioned in · on this topicRank
- High-throughput vector searchDeveloper Backend & Vector Search Services100%Rank #1
- Mentioned in
- The problem is, our vector search is slow under high load. What's the best vector database designed for high-throughput, low-latency production use?on this question100%
Excerpts where Qdrant appeared in the AI's answer
Google AI ModeOct 20, 2026, Google AI Mode Qdrant: Best for high-performance filtering combined with low latency.
Google AI ModeOct 16, 2026, Google AI Mode Qdrant : Built from scratch in Rust, Qdrant is exceptionally fast and memory-efficient.
- Access-controlled semantic searchDeveloper Backend & Vector Search Services98%Rank #1
- Self-hosted open-source vector databaseDeveloper Backend & Vector Search Services98%Rank #1
- Local development prototypingDeveloper Backend & Vector Search Services98%Rank #1
- Time-filtered vector analysisDeveloper Backend & Vector Search Services97%Rank #1
- Vector database live re-indexingDeveloper Backend & Vector Search Services97%Rank #1
- Mentioned in
- We need to re-index millions of vectors without downtime. What vector database offers the best live re-indexing and versioning capabilities?on this question96%
Excerpts where Qdrant appeared in the AI's answer
ChatGPT SearchOct 20, 2026, ChatGPT Search For re-indexing millions of vectors without downtime, my top recommendation is **Qdrant**
Google AI ModeOct 20, 2026, Google AI Mode Qdrant handles re-indexing exceptionally well through a combination of Named Vectors and Collection Aliases.
- Low-latency enterprise RAGDeveloper Backend & Vector Search Services90%Rank #1
- In-memory vector database without SSDDeveloper Backend & Vector Search Services68%Rank #1
- Hybrid vector and keyword search
- Multi-tenant vector isolation
- Vector databases with metadata filtering
- Hosted embeddings with tenant isolation
- Vector database for generative AI
- Managed embedding generation
- Backend as a service with vector search
- Self-hosted open source vector database
- Hybrid vector and keyword search
- Image similarity search APIs
- Local development prototyping
- Multimodal embedding storage and query
- Managed vector databases for production RAG
- Document parsing for LLM pipelines
- Self-hosted open-source RAG framework
- Advanced RAG retrieval strategies
- Embedding model management and deployment
- Open-source embedding models
- End-to-end RAG platform with ingestion and vectorization
- RAG retrieval visualization tool
- Coding assistant with private repository grounding
- Semantic product search
- Zero-retention RAG platform for trade secrets
- Managed embedding APIs
- RAG document reranking
- Self-hosted open-source LLM management platform
- Agent memory integration
- LLM knowledge graph databases
- Developer onboarding assistance
- LLM-based document analysis
- API-first AI customer support bot builder
- Enterprise LLM fine-tuning and RAG
- Web research agent frameworks
- LLM integration into existing applications
- PII redaction for LLM pipelines
- Multi-agent orchestration
How AI describes Qdrant
Common descriptions
high-performance · excellent · written in rust · fast · high performance · open-source
Tone of AI’s statements about Qdrant
- Positive55%
- Neutral43%
- Mixed2%
- Negative1%
Qdrant is the pick in 98% of AI’s recommendations.
Rated above a rival
- ease of useAI is mixed on ease of use: 53% of the answers comparing Qdrant rate it worse than a rival, 47% better. Includes “architectural complexity”, “complexity”, “ease of use / operational overhead”, “integration complexity”, “operational complexity”, “operational simplicity”, “ops complexity”, “setup complexity”, “simplicity”.
Question: I need a proxy that caches LLM responses (semantic caching) to reduce API costs.
ChatGPT SearchOct 25, 2026
Open-source gateway with semantic caching through Redis, Valkey, or Qdrant.
Google AI ModeSep 10, 2026
Requires a vector/cache database backend like Redis or Qdrant to compute and store prompt vectors.
ChatGPT SearchAsk ChatGPT yourself (opens in a new tab)Google AI ModeAsk Google AI Mode yourself (opens in a new tab)
Question: want to build a RAG system over my company's knowledge base. What managed vector database is best for low-latency similarity search?
ChatGPT SearchOct 23, 2026
I'd start by evaluating **Pinecone, Qdrant Cloud, and Zilliz Cloud (Milvus)**
ChatGPT SearchAsk ChatGPT yourself (opens in a new tab)Google AI ModeAsk Google AI Mode yourself (opens in a new tab)
How prominently AI mentions Qdrant
Position in answers
- Named first24%
- 2nd or 3rd30%
- Later46%
Each engine’s share of its own answers across Qdrant’s markets · since Jul 5
- ChatGPT Search7%
- Google AI Mode8%
Insights for Qdrant
Since Jul 5
Gap between platforms: The platform gap for Qdrant in Vector Database Platforms narrowed to 13.5 points, ChatGPT Search ahead
ChatGPT SearchChatGPT Search97%Google AI ModeGoogle AI Mode84%
Sources AI cites
qdrant.tech 32%Other sites 68%
- Owned32%
- Qdrant - Vector Search Engine · 6% (opens in a new tab)
- Documentation - Qdrant · 2% (opens in a new tab)
- Vector Search Benchmarks - Qdrant · 2% (opens in a new tab)
- Hybrid Search - Qdrant · 1% (opens in a new tab)
- Filtering - Qdrant · 1% (opens in a new tab)
- Hybrid Queries - Qdrant · 1% (opens in a new tab)
- Distributed Deployment - Qdrant · 1% (opens in a new tab)
- AI-Powered Vector Search for Smarter Shopping & Personalized E-Commerce - Qdrant · 1% (opens in a new tab)
- Collections - Qdrant · 1% (opens in a new tab)
- Qdrant Fundamentals · 1% (opens in a new tab)
- 5%
- Medium · 1% (opens in a new tab)
- Medium · <1% (opens in a new tab)
- Medium · <1% (opens in a new tab)
- Top Vector Databases — 2024 | Production-Grade | by Mudassir Aqeel Ahmed · <1% (opens in a new tab)
- Medium · <1% (opens in a new tab)
- Medium · <1% (opens in a new tab)
- Vector Databases for RAG - by Gur Raunaq Singh - Medium · <1% (opens in a new tab)
- Which Vector Database Should You Use? Choosing the Best One for Your Needs | by Plaban Nayak | The AI Forum | Medium · <1% (opens in a new tab)
- Vector Databases Explained: The Missing Piece in Your AI Stack · <1% (opens in a new tab)
- Azure AI Search, Qdrant, Cosmos DB & pgvector - Medium · <1% (opens in a new tab)
- 5%
- 6 Vector Databases Compared: Which One Should You Use? · <1% (opens in a new tab)
- 15. Pinecone vs. Weaviate vs. Milvus vs. Qdrant · <1% (opens in a new tab)
- Vector Databases: From First Principles to Production (2026 ... · <1% (opens in a new tab)
- Top 15 Vector Databases for AI Builders in 2026 | Pinecone, Milvus, Qdrant, Chroma & More · <1% (opens in a new tab)
- 15. Pinecone vs. Weaviate vs. Milvus vs. Qdrant · <1% (opens in a new tab)
- Is Your Vector Database Really Fast? · <1% (opens in a new tab)
- Vector Databases Explained: The Complete Guide for 2026 · <1% (opens in a new tab)
- Vector Database Comparison: Qdrant vs Milvus vs Weaviate + PostgreSQL with pgai · <1% (opens in a new tab)
- Qdrant Essentials | Implementing Hybrid Search in Qdrant: Merging Dense & Sparse Vectors · <1% (opens in a new tab)
- Vector Databases Explained: The Complete Guide for 2026 · <1% (opens in a new tab)
- 2%
- Best open source vector database solutions: Top 8 in 2026 · 1% (opens in a new tab)
- What Is a Vector Database? Top 10 Open Source Options · 1% (opens in a new tab)
- Best open source vector database software: Top 10 in 2026 · 1% (opens in a new tab)
- pgvector Hybrid Search: Benefits, Use Cases & Quick Tutorial · <1% (opens in a new tab)
- 2%
- GitHub - qdrant/qdrant: Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/ · GitHub · 1% (opens in a new tab)
- Best way for indexing a stream of data. · qdrant · Discussion #67 · GitHub · 1% (opens in a new tab)
- Adding TTL support · Issue #8236 · qdrant/qdrant - GitHub · <1% (opens in a new tab)
- GitHub - VersionAI/qdrant-vectorial-db: Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/ · GitHub · <1% (opens in a new tab)
- awesome-copilot/skills/qdrant-model-migration/SKILL.md at main · github/awesome-copilot · GitHub · <1% (opens in a new tab)
- GitHub - mcpuishor/qdrant-laravel: A fluent package to interact with ... · <1% (opens in a new tab)
- openai-knowledge-retrieval/README.md at main · openai/openai-knowledge-retrieval · GitHub · <1% (opens in a new tab)
- What storage does qdrant use? FAISS or something else? What persistence storage does qdrant? · qdrant · Discussion #2540 · GitHub · <1% (opens in a new tab)
- Date and time data type time for filtering. · qdrant · Discussion #43 · GitHub · <1% (opens in a new tab)
- JWT RBAC payload filters - documentation needed · qdrant · Discussion #7987 · GitHub · <1% (opens in a new tab)
- 1%
- Vector Databases in Practice: Building a Realistic Hybrid Search RAG System with Qdrant · 1% (opens in a new tab)
- Medium · <1% (opens in a new tab)
- Medium · <1% (opens in a new tab)
- Medium · <1% (opens in a new tab)
- 10 Underground AI/ML Tools That Actually 100x Developer Productivity | by Rohan Mistry | Towards AI · <1% (opens in a new tab)
- 14 Vector Database Optimization Tips for Faster AI Search | by EzInsights AI | Towards AI · <1% (opens in a new tab)
- Building a Modern RAG Agent in 2026: Qwen3 Embeddings and Vector Database in Qdrant. · <1% (opens in a new tab)
- Medium · <1% (opens in a new tab)
- Setting Up Your Qdrant Vector Database | by Harpreet Sahota · <1% (opens in a new tab)
- The 10 Best Vector Databases for RAG · <1% (opens in a new tab)
The page AI cites most for Qdrant
