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What AI says about Qdrant | Parse

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Qdrant

What AI says about Qdrant

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Data as of Oct 13, 2026Based on 39,420 AI responsesReviewed 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 page6%

Products

Cloud<1%

Mentioned in

6%

of AI answers about Qdrant and its rivals. Since Jul 5

The market map

Developer Backend & Vector Search Services

Qdrant in AI’s own words

Question: I need a managed vector database that scales well and has good metadata filtering capabilities.

ChatGPT SearchOct 12, 2026

Qdrant Cloud, Pinecone, and Weaviate Cloud. All three support filtered vector search

Ask ChatGPT yourself

How often AI mentions Qdrant

Developer Backend & Vector Search Services

60%40%20%0%

Use the left and right arrow keys to read each date.

Developer Backend & Vector Search Services50%

Aug 19SepOct 7

Developer Backend & Vector Search Services50%

Mentions by AI engine

Since Jul 5

ChatGPT Search5%
Google AI Mode

Track Qdrant: see where AI mentions it and what to do next.

Insights for Qdrant

Week of Sep 21–27

Gap between platforms: The platform gap for Qdrant in Vector Database Platforms narrowed to 13.5 points, ChatGPT Search ahead
Gap between platforms: The platform gap in Visual Search and Auto-Tagging APIs widened: Google AI Mode 26.7%, ChatGPT Search 16.7%
Slipping: Now ranks #10 in Visual Search and Auto-Tagging APIs, down from #7. Fell behind ViSenze in Visual Search and Auto-Tagging APIs

See where AI recommends Qdrant

Mentioned in

#BrandMentioned inAvg. position

1QdrantMentioned in 48%Avg. position 36.0
2PineconeMentioned in 40%Avg. position 25.3
3MilvusMentioned in 39%Avg. position 4.5
4WeaviateMentioned in 32%Avg. position 13.4
5SupabaseMentioned in 31%Avg. position 17.0
6FirebaseMentioned in 28%Avg. position 31.1
7pgvectorMentioned in 26%Avg. position 5.4
8AppwriteMentioned in 20%Avg. position 36.3
8PostgreSQLMentioned in 20%Avg. position 6.1
10AWSMentioned in 10%Avg. position 7.1
10ConvexMentioned in 10%Avg. position 5.2
14MongodbMentioned in 7%Avg. position 24.2
16PocketBaseMentioned in 6%Avg. position 205.5
16LaravelMentioned in 6%Avg. position 96.1

Where Qdrant ranks in AI

Developer Backend & Vector Search Services#1

Hybrid vector and keyword search#1
High-throughput vector search#4

Vector Database Platforms#1

Hybrid vector and keyword search#1
Managed vector databases for production RAG#4

Visual Search and Auto-Tagging APIs#5

View 3 more markets

(opens in a new tab)

Question: I need a managed vector database that scales well and has good metadata filtering capabilities.

Google AI ModeSep 29, 2026

Qdrant : Available as a managed cloud service built on a high-performance Rust core.

Ask Google AI Mode yourself (opens in a new tab)

Question: 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?

Google AI ModeOct 4, 2026

Qdrant : Written in Rust, Qdrant is famous for predictable low latency and hyper-fast payload filtering

Ask Google AI Mode yourself (opens in a new tab)

6%

Named first23%
2nd or 3rd30%
Later47%

How AI describes Qdrant

ease of useAI is mixed on ease of use: 55% of the answers comparing Qdrant rate it worse than a rival, 45% better. Includes “architectural complexity”, “complexity”, “ease of use / operational overhead”, “integration complexity”, “operational complexity”, “operational simplicity”, “ops complexity”, “setup complexity”, “simplicity”.

56% of analyzed statements about Qdrant are positive, and it is the pick in 98% of recommendations.

Common descriptions

high-performance · excellent · fast · high performance · open-source · written in rust

Compare Qdrant

Qdrant vs Weaviate
Qdrant vs Milvus
Qdrant vs Pinecone
Qdrant vs pgvector
Qdrant vs Chroma
Qdrant vs OpenSearch

Sources AI cites

qdrant.tech 30%Other sites 70%

qdrant.tech (opens in a new tab)Owned30%
medium.com (opens in a new tab)5%

youtube.com (opens in a new tab)5%

zenml.io (opens in a new tab)5%

firecrawl.dev (opens in a new tab)3%

Topics where AI mentions Qdrant

TopicMentioned inRank

Access-controlled semantic searchDeveloper Backend & Vector Search Services98%#1
Local development prototypingDeveloper Backend & Vector Search Services97%#1
QuestionMentioned in
I want to run a vector database on-premise for data privacy. What is the best open-source vector database that is easy to deploy and scale?Mentioned in 98%
Hybrid vector and keyword searchVector Database Platforms93%#1
In-memory vector database without SSDDeveloper Backend & Vector Search Services69%#1
Hybrid vector and keyword searchDeveloper Backend & Vector Search Services17%#1
Self-hosted open-source vector databaseDeveloper Backend & Vector Search Services98%#2
Time-filtered vector analysisDeveloper Backend & Vector Search Services96%#2
Vector database live re-indexingDeveloper Backend & Vector Search Services96%#2
Mentioned in
We need to re-index millions of vectors without downtime. What vector database offers the best live re-indexing and versioning capabilities?96%
Excerpts where Qdrant appeared in the AI's answer
Google AI Mode Google AI Mode
Qdrant and Milvus offer the best live re-indexing and versioning capabilities for millions of vectors without downtime
Google AI Mode Google AI Mode
Qdrant handles live re-indexing cleanly through its robust Collection Aliases and Named Vectors features.
Multi-tenant vector isolationDeveloper Backend & Vector Search Services61%#2
Image similarity search APIsVisual Search and Auto-Tagging APIs48%#2
Multimodal embedding storage and queryVector Database Platforms81%#3
Local development prototypingVector Database Platforms71%#3
Vector database for generative AIDeveloper Backend & Vector Search Services65%#3
Vector databases with metadata filteringDeveloper Backend & Vector Search Services57%#3
Hosted embeddings with tenant isolationDeveloper Backend & Vector Search Services54%#3
Managed vector databases for production RAGVector Database Platforms52%#4
QuestionMentioned in
Best vector database for document-heavy RAG?Mentioned in 100%
Which vector database supports hybrid search (keyword + semantic) and can be run efficiently on-premise?Mentioned in 95%
Document parsing for LLM pipelinesAI Document Processing and OCR Tools48%#4
High-throughput vector searchDeveloper Backend & Vector Search Services10%#4
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?100%
Excerpts where Qdrant appeared in the AI's answer
Google AI Mode Google AI Mode
Qdrant : Written in Rust, Qdrant is famous for predictable low latency and hyper-fast payload filtering
Google AI Mode Google AI Mode
Qdrant : Built in Rust with a heavy focus on low latency and blazing-fast payload filtering
Backend as a service with vector searchDeveloper Backend & Vector Search Services31%#6
Self-hosted open-source RAG frameworkRAG Platforms and Retrieval Tools63%#7
End-to-end RAG platform with ingestion and vectorizationRAG Platforms and Retrieval Tools10%#12
RAG retrieval visualization toolLLM Observability and Evaluation Platforms7%#12
Advanced RAG retrieval strategiesLLM Agent Frameworks and Tooling4%#12
Semantic product searchProduct Search and Discovery Platforms14%#14
Embedding model management and deploymentEnterprise MLOps Model Registry Platforms11%#14
Open-source embedding modelsEmbedding Model APIs and Services10%#14
Zero-retention RAG platform for trade secretsLLM Agent Frameworks and Tooling9%#14
Managed embedding APIsEmbedding Model APIs and Services11%#15
RAG document rerankingRAG Platforms and Retrieval Tools8%#17
Agent memory integrationLLM Agent Frameworks and Tooling3%#19
Self-hosted open-source LLM management platformLLM Fine-Tuning and Hosting Platforms6%#21
Web research agent frameworksLLM Agent Frameworks and Tooling3%#27
Developer onboarding assistanceAI Developer Productivity Tools2%#28
LLM knowledge graph databasesAI Knowledge Graph Platforms3%#33
Conversational AI chatbot developmentAI Agent Orchestration Platforms8%#34
Domain-adapted embeddingsLLM Fine-Tuning Platforms2%#35
Enterprise LLM fine-tuning and RAGLLM Infrastructure and Fine-Tuning Platforms6%#37
Multi-agent orchestrationLLM Agent Frameworks and Tooling1%#45
PII redaction for LLM pipelinesLLM Security and Governance Platforms1%#68

QuestionMentioned in

I need a vector database that supports hybrid search (keyword + semantic) and runs efficiently on-premise.Mentioned in 98%