Qdrant vs Weaviate

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

功能对比

Qdrant 与 Weaviate 功能对比
维度	Qdrant	Weaviate
Best For	Self-hosted deployments that need payload filtering during search	Corpora that need keyword and vector search in one query
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

灰底行表示两者相同；有差异的行才是下方推荐结论的依据。

该选哪个？

由上述差异按固定规则生成，不是模型生成的。

Choose Qdrant if…

Best For is "Self-hosted deployments that need payload filtering during search" rather than "Corpora that need keyword and vector search in one query"
Written in is "Rust" rather than "Go"

Choose Weaviate if…

Best For is "Corpora that need keyword and vector search in one query" rather than "Self-hosted deployments that need payload filtering during search"
Written in is "Go" 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.

Weaviate

database · Vector Databases

Weaviate is an open-source vector database that combines vector similarity with keyword search in a single query, a technique usually called hybrid search. It solves the problem that pure vector search can miss exact matches: identifiers, error codes and rare terms are poorly represented in embedding space but trivially matched by keywords. It exists because most real corpora need both. It can be self-hosted or consumed as a managed service.