Qdrant

The open-source vector database built in Rust: filtered semantic search, hybrid sparse+dense retrieval, and a managed cloud for RAG that has to stay fast as collections grow.

Product preview

Overview

Qdrant is the vector database teams pick when Pinecone’s managed ease is not enough ownership and a general-purpose DB’s vector plugin is not enough speed. Written in Rust, it treats filtered ANN as a first-class query — metadata predicates run with the search, not as a post-filter — which is what production RAG actually needs when “similar” is not “any similar,” it is “similar in this tenant, this language, this date range.”

Hybrid sparse+dense retrieval landed as a first-class mode, and Qdrant Cloud removes the ops tax if you do not want to run the cluster. Compared with Weaviate it is leaner and more search-shaped; compared with Chroma it is built for production scale rather than notebooks. The costs are another datastore to operate and a query model you must learn. For RAG that has to stay fast, it is a top-three choice.

Pros / Cons

Pros

Rust speed with first-class filtered ANN
Hybrid sparse+dense retrieval
Self-host or skip ops with Cloud
Open source, no query lock-in to a closed API

Cons

Another datastore to operate and back up
Query model is its own language
Ecosystem smaller than Pinecone’s tutorials

Who it's for

Production RAG with tenant and metadata filters

Hybrid keyword plus vector search

Self-hosted vector search in regulated stacks

Replacing a hosted vector DB for cost or residency

Bottom line

The Rust vector database for RAG that has to filter and stay fast — open source, Cloud optional.

Key Features

Rust engine for high-throughput ANN

Payload filters on vector search

Sparse + dense hybrid retrieval

Qdrant Cloud or self-host

Python, JS, Go, Rust clients

Compare Qdrant with alternatives

Qdrant vs Hugging Face Qdrant vs Replicate Qdrant vs Ollama 4 products · AI Platforms

Frequently asked questions

What is Qdrant?

Qdrant is a AI Platforms tool developed by Qdrant. The company was founded in 2021 and is headquartered in Berlin, Germany. The open-source vector database built in Rust: filtered semantic search, hybrid sparse+dense retrieval, and a managed cloud for RAG that has to stay fast as collections grow.