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Qdrant
Qdrant is an open-source vector search engine written in Rust, with hybrid search, metadata filtering and a managed cloud for AI retrieval.
Qdrant is a vector database and search engine for developers building retrieval for RAG, agents, recommendations and semantic search. It stores vectors with JSON metadata and applies filters during the index traversal itself, which keeps recall high and latency low under complex conditions. Hybrid search blends dense and sparse vectors in one query, with support for BM25, SPLADE++ and miniCOIL, and multivector storage and late-interaction models such as ColBERT allow reranking inside the engine. The core is written in Rust and released under the Apache 2.0 licence, so it can be self-hosted for free. Qdrant Cloud is a managed service on AWS, Google Cloud and Azure with a free 1 GB cluster, and Hybrid Cloud, Private Cloud and an Edge beta cover Kubernetes, air-gapped and on-device deployments.
Qdrant at a glance
- Best for: Software engineer
- Runs on: API, self-hosted, open source
- Pricing: Open-source version free to self-host; Qdrant Cloud free tier with a 1 GB cluster; Standard usage-based; Premium with minimum spend
- Released: May 2020
- Category: AI coding tool
- Website: qdrant.tech
Key features
- Dense, sparse and hybrid vector search in one query
- Metadata filters applied during HNSW traversal
- Multiple vectors per object and late-interaction reranking
- Score boosting and MMR to diversify results
- Managed cloud on AWS, Google Cloud and Azure
- Hybrid Cloud, Private Cloud and Edge deployment options
- Apache 2.0 open-source core written in Rust
Use it for
- Store embeddings for a RAG chatbot over company documents
- Give an AI agent long-term memory with semantic recall
- Run product recommendations on filtered similarity search
- Index a codebase for semantic code search
Companion to
Tools whose articles name Qdrant as a companion, each with its job for them.
Alternatives to Qdrant
- HelixDB — Combines graph, vector, and full-text search in one engine.
- TurboVec — Fits a 10 million document corpus in 4 GB of RAM and searches faster than FAISS.
- seekdb — Unifies relational, vector, and text data for production-grade AI agents.
- SeekStorm — It delivers sub-millisecond lexical and vector search performance in Rust.
- zvec — It is a pure local, serverless vector database providing fast hybrid search.
- Weaviate — Stores data objects and vectors for semantic and hybrid search capabilities.
- Quivr — It provides a pre-built RAG infrastructure for rapid application integration.
- USearch — It uses SIMD instructions and optimized concurrency to outperform FAISS.
- Chroma — Object storage architecture with automatic query-aware data tiering and caching.
- Qoder — It features deep codebase awareness, autonomous coding agents, and cross-platform support.
- Grit — Automates large-scale code refactoring and migration tasks efficiently.
- RuVector — Provides persistent vector retrieval, graph relationships, and local decision making in Rust.
All Qdrant alternatives