Term
Qdrant
Qdrant is an open-source vector database written in Rust — it stores embeddings for semantic search, RAG and recommendation systems and runs locally, on your own cluster or as a managed cloud.
Qdrant — explained in more detail
Qdrant launched as an open-source project in 2021 and is today among the most widely used self-hostable vector DBs. The engine is written in Rust and uses HNSW as its index structure — fast even at millions of vectors. Records (points) consist of a vector and arbitrary JSON payload; payload filters are a core feature and are evaluated directly inside the index rather than as a post-step. Deployment modes: single-node container, distributed cluster, embedded mode inside an application, or Qdrant Cloud (managed).
Example / Practical context
A RAG pipeline with Qdrant: use the Python client to write embeddings via client.upsert(collection_name="docs", points=[...]), then client.search(collection_name="docs", query_vector=q, query_filter=...) to query. Filters like {"must": [{"key": "tenant", "match": {"value": "acme"}}]} compose naturally — important for multi-tenant scenarios. Snapshot and backup APIs simplify migrations.
How it differs from related terms
Pinecone offers comparable functionality but is cloud-only. Weaviate leans more on schema modelling with classes and properties; Milvus targets very large datasets with explicit GPU index support. Qdrant’s sweet spot: solid self-hosting with strong filter performance and manageable operational overhead. pgvector remains the right choice when vectors should live next to relational data in a Postgres database.
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GlossarChroma
Chroma (ChromaDB) is an open-source vector database for storing and searching embeddings. It is widely used in RAG systems to retrieve relevant text passages via similarity search and feed them to language models.
LexikonVector Databases Compared
What vector databases do, when you need one, and how Chroma, Weaviate, Milvus, Qdrant and pgvector stack up against each other.