Qdrant
activeby Qdrant Solutions GmbH · since 2021
Rust vector search engine with rich payload filtering, quantization and a managed cloud.
Overview
Qdrant is an open-source vector database written in Rust, known for fast filtered search: payload indexes are integrated into its HNSW graph so restrictive filters do not wreck recall. It supports dense, sparse and multi-vector points, scalar/binary/product quantization, a universal Query API for hybrid fusion and re-ranking, and distributed sharding with replication. It runs comfortably from a single Docker container to large clusters, and Qdrant Cloud adds managed, hybrid (your Kubernetes) and private deployments. A strong default for self-hosted vector search.
Strengths & trade-offs
Strengths
- +Filter-aware HNSW keeps recall high under restrictive payload filters
- +Dense, sparse and multi-vector (ColBERT) support with server-side fusion
- +Scalar, binary and product quantization with on-disk storage
- +Small, efficient Rust binary that is easy to self-host
- +Managed, hybrid and private cloud options
Trade-offs
- −Full-text support is a filter index, not a BM25 search engine
- −No joins or relational queries
- −Distributed mode requires planning shard counts up front
- −Enterprise features such as SSO are cloud-tier only
Great for
- +RAG retrieval with metadata filtering
- +Recommendation and similarity search
- +Image and multimodal search
- +Multi-tenant semantic search
Think twice for
- −Keyword-relevance-heavy search (use Elasticsearch/Vespa)
- −Transactional workloads
- −Analytics
Capabilities
Native or official first-party support · 8 of 25 tracked
Scale & consistency
- Comfortable data size
- Large · ≤ 1 PB
- Horizontal scaling
- Native
- Multi-region
- No
- Transactions
- None
- Consistency
- Tunable, Eventual
- Schema
- Schemaless
- Architecture
- Distributed, Client server
- Query languages
- Qdrant Query API (JSON filters)
Collections shard across nodes with configurable replication factor; on-disk vectors and quantization support billion-scale collections.
Deployment
available on · AWS · Google Cloud · Azure
Ecosystem
Alternatives to Qdrant
Open-source AI-native vector database with hybrid search, modules for embeddings and multi-tenancy.
qdrant vs weaviate →Cloud-native, distributed open-source vector database built for billions of vectors.
qdrant vs milvus →Fully managed serverless vector database with built-in embedding, reranking and assistants.
qdrant vs pinecone →Open-source retrieval database for AI apps, from embedded Python to serverless Chroma Cloud.
qdrant vs chroma →The standard Postgres extension for vector similarity search with HNSW and IVFFlat indexes.
qdrant vs pgvector →Embedded, serverless retrieval database on the Lance columnar format for multimodal AI data.
qdrant vs lancedb →Qdrant FAQ
What is Qdrant?
Qdrant is a vector database by Qdrant Solutions GmbH, first released in 2021. Qdrant is an open-source vector database written in Rust, known for fast filtered search: payload indexes are integrated into its HNSW graph so restrictive filters do not wreck recall. It supports dense, sparse and multi-vector points, scalar/binary/product quantization, a universal Query API for hybrid fusion and re-ranking, and distributed sharding with replication. It runs comfortably from a single Docker container to large clusters, and Qdrant Cloud adds managed, hybrid (your Kubernetes) and private deployments. A strong default for self-hosted vector search.
Is Qdrant open source?
Yes. Qdrant is open source. License: Apache License 2.0.
Does Qdrant support ACID transactions?
No. Qdrant does not provide ACID transactions; its consistency model is tunable, eventual.
Does Qdrant scale horizontally?
Qdrant scales horizontally out of the box, with built-in sharding or partitioning. It is comfortable at large scale (≤ 1 PB).
How can I run Qdrant?
You can self-host it and use the vendor's managed cloud. Managed offerings include Qdrant Cloud. It is available on AWS, Google Cloud and Azure.
What is Qdrant best for?
Qdrant is a strong fit for RAG retrieval with metadata filtering; recommendation and similarity search; and image and multimodal search. Think twice for keyword-relevance-heavy search (use Elasticsearch/Vespa).
What are the best alternatives to Qdrant?
Common alternatives to Qdrant include Weaviate, Milvus, Pinecone, Chroma and pgvector. The right choice depends on your workload, scale and hosting; WhichDB's smart match ranks them against your requirements.
How popular is Qdrant?
Qdrant ranks #55 of 844 databases on the WhichDB Score (35.9/100), and #5 among vector databases. The score combines public adoption signals such as package downloads, GitHub activity, job posts and developer discussion.