Qdrant
Free tierHigh-performance vector search engine built for production-grade AI retrieval at any scale
Free tier available·Technical·API available·Open source
Key strengths
Built entirely in Rust with SIMD for high performanceNative hybrid search (dense + sparse vectors, BM25, SPLADE++)Efficient one-stage filtering during HNSW traversalAdvanced quantization (scalar, binary, asymmetric) reducing memory up to 64xMultiple deployment models: cloud, hybrid cloud, private cloud, edgeReal-time indexing without full index rebuildsSOC2 & HIPAA compliant
Free tier + paid plans
San Francisco, USA
Founded 2020
Self-hostable
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Recent changes
- Plans updated: Free tier: 0.5 vCPU / 1GB RAM / 4GB Disk, free forever. Standard tier: usage-based pricing (charged for vCPU, memory GB, storage GB). Premium tier: minimum spend required. Hybrid Cloud and Private Cloud available for enterprise.
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Qdrant — High-Performance Vector Search at Scale
Qdrant is an open-source vector database and search engine written entirely in Rust, leveraging SIMD instructions for maximum throughput. It supports native hybrid search combining dense vectors, sparse vectors, BM25, and SPLADE++ in a single query pass, with efficient one-stage filtering during HNSW graph traversal — eliminating the post-filtering accuracy loss common in other engines.
Key architectural highlights:
- Advanced quantization (scalar, binary, asymmetric) delivering up to 64× memory reduction
- Real-time indexing with no full index rebuilds required
- Multiple deployment targets: managed cloud (AWS, GCP, Azure), Hybrid Cloud (your infrastructure + Qdrant control plane), Private Cloud, and edge
- REST and gRPC APIs with client libraries available
- SOC2 & HIPAA compliant
- 34,931 GitHub stars — actively maintained open-source project (founded 2020)
- Self-hostable via Docker or Kubernetes, with infrastructure-as-code support via Terraform and Pulumi
- Observability integrations: Prometheus, Grafana, Datadog
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