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qdrant

Free

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Search ToolsFreeFree tier
Type
Open Source
Company
Qdrant
Visit Website GitHub (31.0k stars)

About qdrant

Qdrant is a high-performance, open-source vector database and search engine designed for AI retrieval at scale. Written entirely in Rust with SIMD optimizations and a custom storage engine (Gridstore), it delivers fast and scalable vector search with real-time indexing, memory-efficient storage, and advanced filtering. Qdrant supports native hybrid search combining dense and sparse vectors (BM25, SPLADE++, miniCOIL), multivector per object, one-stage filtering during HNSW traversal, and full-spectrum reranking including ColBERT and MMR. It offers flexible deployment options: fully managed cloud (AWS, GCP, Azure), hybrid cloud (bring your own Kubernetes), private cloud (air-gapped), and edge (beta). Enterprise features include SSO, RBAC, SOC 2 and HIPAA compliance, private networking, and zero-downtime upgrades. Qdrant is used by companies for AI trip planning, real-time personalized responses, multi-agent platforms, and more.

Key Features

Real-time indexing: new vectors searchable immediately after addition
Memory-efficient storage with custom Gridstore engine, storing billions of vectors
Expansive metadata filters supporting nested, text, geo, has_vector, and more
Native hybrid search (dense + sparse) with BM25, SPLADE++, miniCOIL
Built-in multivector support for more expressive and multimodal retrieval
Efficient one-stage filtering during HNSW traversal – high recall with low latency
Full-spectrum reranking: score boosting, ColBERT, MMR
Deploy anywhere: cloud, hybrid cloud, private cloud, and edge
Enterprise-ready: SOC 2, HIPAA, SSO, RBAC, private networking, zero-downtime upgrades
Rust-powered with SIMD optimizations for maximum performance

Pros & Cons

Pros
  • High performance due to Rust, SIMD, and custom storage engine
  • Real-time indexing without rebuilding the entire index
  • Advanced filtering capabilities including nested, text, geo filters
  • Native support for hybrid search (dense + sparse vectors)
  • Flexible deployment options: cloud, hybrid, private, edge
  • Strong open-source community (30k+ GitHub Stars, 60k+ Community Members)
  • Enterprise-grade security and compliance (SOC 2, HIPAA, SSO, RBAC)
  • Built-in reranking with ColBERT, MMR, and score boosting
Cons
  • Free tier limited to 1GB RAM and 4GB disk, may require upgrade for production
  • Self-hosting requires infrastructure expertise for optimal performance

Best For

AI trip planning across billions of reviews and images (2-3x revenue increase)Real-time personalized responses with deep contextual awareness (Breeze AI)Multi-agent platforms with real-time context across 2M+ conversationsScalable vector search for AI agent platforms with 5,000+ data sources (Dust)Reducing latency by 90% and increasing throughput by 150% for AI agents (Lyzr)Production-grade AI retrieval at any scale and deployment model

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FAQ

What happens if I exceed free tier limits?
If your data size growth exceeds the Free Tier limits (1GB RAM and 4GB disk), you can easily upgrade to a Standard Tier and scale up your cluster.
How does Qdrant Cloud pricing work?
Qdrant Cloud pricing is based on resource usage; larger clusters cost more. The free tier includes 1GB RAM and 4GB disk storage. Upgrading provides a dedicated cluster.
Can I migrate from OSS to Qdrant Cloud?
Yes, you can easily migrate your existing Qdrant OSS deployment to Qdrant Cloud. Qdrant provides a migration tool and documentation to help transition smoothly.
How do I choose between Free Tier and Standard Tier?
The Free Tier is ideal for testing and prototypes, limited to 1GB RAM and 4GB disk without high availability. Standard Tier is for production workloads with scalable resources and higher uptime SLAs.