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Qdrant
PublishedLast updated
Qdrant is a high-performance vector search engine built in Rust for production-grade semantic search and RAG applications.
Reviewed profile
Quick verdict
Qdrant is for engineering teams building semantic search, RAG, and recommendations with a Rust vector database. Caveat: license and download details need direct verification before procurement.
- Best for
- Production vector search
- RAG over scraped content
- Payload-filtered similarity search
- Multi-language SDK teams
- Not ideal for
- Traditional web crawling
- Hosted scraping APIs
- Simple keyword search
- Last verified
- Jun 26, 2026
Key facts
- Stars28,382 GitHub starsGitHub
- LanguageRust primary repositoryGitHub
- LicenseLicense not specifiedGitHub
- SDKsPython, JavaScript, Go, and Rust clientsProject docs
- DownloadsPackage download counts not availablenpm
Compiled from public repository, package, and project documentation data; verify current details with Qdrant before procurement.
Sources
github.com
Verified against the qdrant/qdrant repository and the PyPI package.
Qdrant is a high-performance, open-source vector search engine built in Rust for production AI workloads.
Key Features:
- Rust Performance - Built from the ground up in Rust for memory safety and blazing-fast search.
- Rich Filtering - Combine vector similarity with payload filtering for precise, context-aware retrieval.
- Distributed Mode - Scale horizontally across nodes for high availability and throughput.
- Quantization - Reduce memory usage with scalar and product quantization while preserving accuracy.
- REST and gRPC APIs - Integrate easily with any language or framework through well-documented APIs.
Whether you're building semantic search over scraped documents, powering recommendation engines, or deploying RAG systems, Qdrant provides the speed and reliability your production workloads demand.
Categories:
Features:
Vector SearchREST APIOpen SourceRich FilteringDistributed Mode
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