Qdrant

Qdrant is a high-performance vector database written in Rust, delivering exceptional speed and memory efficiency for similarity search. It supports filtering, payloads, and distributed deployment out of the box. Best for performance-critical applications where query latency and throughput matter, with both self-hosted and managed cloud options.

AI ToolFree Tier: Free self-host; cloud tierCompany: Qdrant SolutionsCategory: InfrastructureOpen Source: YesQuick Start: Run with Docker: 'docker run -p 6333:6333 qdrant/qdrant' → Install the Python client with 'pip install qdrant-client' → Create a collection, upsert vectors, and search

Qdrant

Rust-based high-performance vector search

Visit Qdrant →

Qdrant is a high-performance vector database written in Rust, delivering exceptional speed and memory efficiency for similarity search. It supports filtering, payloads, and distributed deployment out of the box. Best for performance-critical applications where query latency and throughput matter, with both self-hosted and managed cloud options.

FREE TIER
Free self-host; cloud tier
COMPANY
Qdrant Solutions
CATEGORY
Infrastructure › Vector Databases
OPEN SOURCE
Yes
PRIVACY
Rust; self-hosted
TAGS
vector-dbrust

QUICK START

Run with Docker: 'docker run -p 6333:6333 qdrant/qdrant' → Install the Python client with 'pip install qdrant-client' → Create a collection, upsert vectors, and search

BEST FOR

  • ▸Developers building high-throughput AI apps needing sub-millisecond vector search.
  • ▸Teams requiring a scalable, distributed vector database with advanced filtering.
  • ▸Engineers prioritizing Rust-level performance & memory efficiency for vector ops.

NOT FOR

  • ✕Beginners needing a simple, low-code vector search solution for small projects.
  • ✕Projects where vector search is a minor feature and performance isn't critical.
Finding similar tools…
← Back to all tools