# Qdrant: what AI models say (September 2026)

Source: Memetik Index, https://www.memetik.ai/vendors/qdrant
Website: https://qdrant.tech

Qdrant was named in 49 of 50 AI answers across 1 category. Best position: Vector databases, 98% answer share, ranked #2, named first in 24%.

## What Qdrant is

Qdrant is a vector search engine for AI retrieval, built for developers. It is used to store and search data for applications that need to retrieve relevant information using vectors.

Qdrant stores metadata in JSON and supports advanced filters. It can combine keyword and vector search in a single query using dense or sparse vectors. New data can be indexed without rebuilding the whole index. Developers can use Qdrant through REST and gRPC APIs, as well as official clients.

(From Qdrant's own website, read 2026-09-02.)

## By category

| Category | Rank | Answer share | Named first | Models |
|---|---|---|---|---|
| Vector databases (https://www.memetik.ai/index/vector-databases) | #2 of 16 | 98% | 24% | GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity, Sonar Reasoning Pro |

## What the models said

> "Several comparative reviews explicitly state that for most RAG pipelines, Pinecone or Qdrant are the strongest defaults."
> — Sonar Reasoning Pro, Vector databases

> "Choose Qdrant if your app depends on metadata filtering and you want a strong open-source production option."
> — Perplexity, Vector databases

> "Choose a database like Qdrant, Weaviate, or Pinecone that supports dense + sparse hybrid search natively."
> — Gemini, Vector databases

> "Choose Qdrant for a strong balance of performance, filtering, cost control, and deployment flexibility."
> — GPT-5.6 Luna, Vector databases