Do AI agents pick Qdrant?
How often agents choose Qdrant when a developer needs vector database — measured across Claude, GPT, Gemini.
95% CI 0%–4%
When developers ask an AI agent for vector db, Qdrant is picked 1% of the time — ranking #4 of 5 measured across Claude, GPT, Gemini.
0 docs in FineWeb · 341 URLs in Common Crawl
Level 1 · Basic Web Presence · 3/16 agent standards
2.5M installs / month ▲ 14% MoM
Get the full report
The per-model and per-surface breakdown for Qdrant — where it wins, where it loses, and to whom — plus an alert when the Pick Rate moves.
About Qdrant
A vector search engine for storing, searching, and managing high-dimensional vector embeddings.
Qdrant is a vector database and search engine designed for applications that need to find similar items based on vector representations — such as semantic search, recommendation systems, and similarity matching. It stores collections of vectors alongside optional payload data and exposes APIs for querying nearest neighbors. It can be self-hosted via Docker or used as a managed service.
Where to start
Run Qdrant locally with Docker, then use the JavaScript REST client or another supported client to create a collection and start upserting and querying vectors against the REST API.
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