Open-source, Rust-built vector similarity search engine with payload filtering, hybrid search, quantization, and a fully managed Qdrant Cloud — popular for RAG, recommendation, and agent memory.
Open-source, Rust-built vector similarity search engine with payload filtering, hybrid search, quantization, and a fully managed Qdrant Cloud — popular for RAG, recommendation, and agent memory.
Qdrant is a high-performance vector search engine written in Rust, distributed as open source under Apache 2.0 and offered as a managed service via Qdrant Cloud. Its technical reputation comes from a focused, fast HNSW implementation, rich payload filtering (filter on metadata at query time without slowing search), strong hybrid search via sparse vectors and full-text indexes, and aggressive quantization (scalar, product, and binary) that lets large indexes fit in less RAM with minimal recall loss. Operationally, Qdrant supports collections with shards and replicas, snapshots and backups, RBAC and JWT-based access control, and a clean REST + gRPC API with idiomatic Python, JS/TS, Go, Rust, and Java clients. Qdrant Cloud offers a free community/managed plan, a paid scale tier with usage-based pricing on cluster size, and an Enterprise plan with private cloud, BYOC, SSO, and SOC 2.
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Qdrant delivers a strong balance of performance, filtering capabilities, and operational flexibility among open-source vector databases. The Rust-based engine is built for serious retrieval workloads, though teams still need retrieval engineering skill to get the best results.
Qdrant supports dense and sparse vectors together, including BM25, SPLADE++, and miniCOIL. This lets teams combine semantic relevance with keyword precision in one retrieval layer, which is particularly useful for RAG, legal search, support search, and product discovery.
Qdrant stores metadata in JSON and supports filters such as nested, text, geo, and has_vector. The website emphasizes one-stage filtering during HNSW traversal, which helps preserve recall and latency under complex filtering conditions.
Qdrant supports multiple vectors per object, allowing richer retrieval models for multimodal, multi-field, or late-interaction search. This is useful when one record needs separate embeddings for title, body, image, user behavior, or token-level representations.
Qdrant can be deployed through managed Cloud, Hybrid Cloud, Private Cloud, on-prem, Kubernetes, and Qdrant Edge beta. The enterprise feature set includes SSO, multitenancy, RBAC, private networking, backups, point-in-time restore, zero-downtime upgrades, and observability integrations.
The engine is built in Rust with SIMD and a custom storage engine called Gridstore, according to the website. Qdrant also supports asymmetric, scalar, and binary quantization, with the website claiming memory reduction up to 64x while maintaining search quality.
$0
Usage-based per cluster size and resources
Custom
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Hybrid dense and sparse vector search now generally available with BM25 support.
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The current website footer is marked 2026 and lists Qdrant Cloud Inference and Qdrant Edge (Beta) among current products. The scraped content does not provide dated 2025-2026 release notes, but it does describe Cloud Inference for generating text and image embeddings inside Qdrant Cloud and Edge beta for low-latency vector search close to where data is generated.
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