Qdrant

Free tier

High-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

Facts verified against qdrant.tech on Oct 5, 2026

Recent changes

Oct 5, 2026Plans 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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Developer Documentation & Integration

APIs & SDKs Qdrant exposes both REST and gRPC APIs. Official client libraries are available for major languages (refer to the Qdrant documentation for the current SDK list).

Deployment Options

Mode	Description
Managed Cloud	Hosted on AWS, GCP, or Azure — fully managed
Hybrid Cloud	Qdrant control plane + your own Kubernetes infrastructure
Private Cloud	Fully air-gapped deployment on your infrastructure
Self-hosted	Docker or Kubernetes; IaC via Terraform and Pulumi

Quantization Configure scalar, binary, or asymmetric quantization per collection to reduce memory footprint by up to 64× with tunable accuracy trade-offs.

Hybrid Search Combine dense and sparse vector fields (BM25, SPLADE++) in a single collection and query. Filtering is applied during HNSW traversal (not post-hoc), preserving recall accuracy.

Observability Native metrics export to Prometheus; dashboards available for Grafana and Datadog.

Compliance SOC2 and HIPAA compliant — suitable for regulated workloads.

Pricing (API/Cloud)