Qdrant

Qdrant is an open-source vector database and similarity search engine, designed for storing and retrieving high-dimensional vectors at scale. Written in Rust and released under the Apache 2.0 licence, Qdrant is among the most widely deployed vector databases for retrieval-augmented generation, semantic search, recommendation systems, and other AI workloads that depend on embedding similarity. The project is developed by Qdrant Solutions GmbH, a Berlin-based company that also operates the managed Qdrant Cloud service on Amazon Web Services, Google Cloud Platform, and Microsoft Azure.

Architecture

Qdrant is built around a custom storage and indexing engine implemented in Rust, which gives it high throughput and predictable latency under load. The core indexing data structure is a Hierarchical Navigable Small World (HNSW) graph, an approximate nearest neighbour algorithm that provides good recall and query speed across millions to billions of vectors. Qdrant supports configurable distance metrics including cosine similarity, dot product, Euclidean (L2), and Manhattan (L1) distance.

The system organises data into named collections, each of which holds points consisting of an identifier, one or more vectors, and an arbitrary JSON payload. Collections can be sharded across nodes and replicated for fault tolerance, and Qdrant supports both single-node and distributed cluster deployments.

Key features

Qdrant supports dense vectors for semantic similarity, sparse vectors that approximate keyword behaviour, and multivector search for objects with several embeddings — useful for late-interaction models such as ColBERT. Hybrid search combines dense and sparse vectors in a single query and merges results using configurable fusion strategies such as Reciprocal Rank Fusion (RRF) and Distribution-Based Score Fusion (DBSF).

Filtered search is a particular strength of Qdrant. Each point carries a JSON payload, and queries can combine vector similarity with rich filter conditions — keyword matching, full-text search, numeric range, geo-bounding box, datetime range, and boolean must, should, and must_not clauses. Filters are integrated into the HNSW traversal rather than applied as a post-processing step, which preserves recall under aggressive filtering.

Built-in quantisation, including scalar, product, and binary quantisation, can reduce memory consumption by up to 97% with a configurable trade-off between speed and precision. Snapshots, point-in-time backups, and storage tiering enable production operations.

Multitenancy and enterprise features

In 2025, Qdrant added a Tiered Multitenancy capability that lets operators dedicate a shard of the underlying vector database to a specific tenant or workload, simplifying isolation in software-as-a-service deployments. Qdrant Cloud has added simplified cluster management, cloud role-based access controls, granular database API keys, advanced monitoring and observability dashboards, and cloud automation APIs aimed at enterprise users.

Ecosystem and integrations

Qdrant integrates broadly with the modern AI stack. Embedding providers including OpenAI, Cohere, Voyage AI, Mistral, and Hugging Face produce vectors that Qdrant stores. Application frameworks such as LangChain, LlamaIndex, Haystack, Semantic Kernel, and Spring AI provide native Qdrant connectors. Observability platforms such as Langfuse, Helicone, and Arize AI offer tracing for retrieval calls into Qdrant.

| Aspect | Qdrant | |--------|--------| | Language | Rust | | Indexing | HNSW | | Hybrid search | Native (dense + sparse) | | Filtering | Rich JSON payload filters | | Quantisation | Scalar, product, binary | | Deployment | Self-hosted, Qdrant Cloud |

Performance and benchmarks

Independent benchmarks published by ANN-Benchmarks and Qdrant's own benchmark suite have consistently ranked Qdrant among the fastest open-source vector databases on common workloads. The Rust implementation and asynchronous I/O architecture allow Qdrant to scale across CPU cores and handle high concurrency without the garbage-collection pauses common to Java-based alternatives.

See Also

References

References

1. Qdrant Solutions GmbH. (2024–2026). Qdrant Documentation. qdrant.tech.
2. Malkov, Y., and Yashunin, D. (2018). Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs. IEEE TPAMI.
3. Qdrant. (2025). Tiered Multitenancy and Enterprise Cloud Features. Qdrant Blog.
4. ANN-Benchmarks. (2024). Open-Source Vector Database Benchmark Results.
5. Bank Negara Malaysia. (2023). Risk Management in Technology (RMiT) Policy Document. BNM.