Qdrant vs Milvus

Open-source vector database written in Rust, with payload filtering.

Feature comparison

Feature comparison of Qdrant and Milvus
Feature	Qdrant	Milvus
Best For	Self-hosted deployments that need payload filtering during search	Collections that outgrow a single node
Cloud	Yes	Yes
Filtering	Yes	Yes
Hybrid Search	Yes	Yes
Open Source	Yes	Yes
Self-hosted	Yes	Yes
Type	Vector database	Vector database
Written in	Rust	Go and C++

Shaded rows are identical for both; the differing rows are the ones that drive the recommendation below.

Which should you choose?

Generated from the differences above by structured rules, not by a model.

Choose Qdrant if…

Best For is "Self-hosted deployments that need payload filtering during search" rather than "Collections that outgrow a single node"
Written in is "Rust" rather than "Go and C++"

Choose Milvus if…

Best For is "Collections that outgrow a single node" rather than "Self-hosted deployments that need payload filtering during search"
Written in is "Go and C++" rather than "Rust"

Qdrant

database · Vector Databases

Qdrant is an open-source vector similarity search engine written in Rust, storing vectors alongside JSON payloads and allowing results to be filtered by those payloads during the search rather than after it. It solves the problem that retrieval usually needs a condition as well as a similarity, such as restricting to one tenant or one document type. It exists because filtering after a vector search silently loses recall: the top matches may all be filtered away. Qdrant can be self-hosted or used as a managed service.

Milvus

database · Vector Databases

Milvus is an open-source vector database built as a distributed system, separating storage and compute so a collection can grow beyond one machine. It solves the problem that a single-node vector index eventually stops fitting in memory or stops keeping up with query load. It exists for the upper end of the scale range, where the operational complexity of a distributed deployment is repaid by horizontal growth. It supports several index types so recall and latency can be traded against each other.