Launched January 3, 2023
Qdrant

Qdrant

Vector Database for the next generation of AI applications.

Free3,614 impressions#154 of its week

Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization.

Qdrant is an open-source, Rust-based vector database offering high-performance similarity search with advanced metadata filtering.

for
Developers building AI-powered search, recommendation, RAG, or agent systems.
pricing
freemium
license
Apache-2.0
qdrant/qdrant 35k 2.7kRustupdated 14 days ago
works withPythonJavaScriptRESTgRPCAWSAzureGCPPrometheusGrafanaDatadog

Key features

8 features of Qdrant
  • Expansive Metadata Filters — Store JSON metadata and apply nested, text, geo, and vector filters during search.
  • Native Hybrid Search — Combine dense vector and sparse keyword queries (BM25, SPLADE++, miniCOIL) in a single request.
  • Built-in Multivector Support — Attach multiple vectors per object for richer, multimodal retrieval.
  • Real-Time Indexing — New vectors become searchable instantly without rebuilding the index.
  • Memory-Efficient Storage — Store billions of vectors with optimized storage and up to 64× quantization compression.
  • Developer-Friendly APIs — Access via REST, gRPC, and official Python/JavaScript clients with full control over HNSW and reranking.
  • Enterprise Tooling — Includes RBAC, SSO, backups, point-in-time restore, and monitoring integrations (Prometheus, Grafana, Datadog).
  • Multi-Cloud Deployment Options — Run on-prem, hybrid, edge, or fully managed Qdrant Cloud on AWS, Azure, or GCP.

Use cases

  • Semantic search for large document corpora with real-time updates.
  • Recommendation engines that match users to items via vector similarity.
  • RAG pipelines that retrieve context-rich passages using hybrid dense-sparse search.
  • AI agents requiring persistent memory and fast similarity lookup.
  • Anomaly detection by finding outlier patterns in high-dimensional data.

Qdrant FAQ

What happens if I exceed the Free Tier limits?+

You can upgrade to a Standard Tier, which provides larger resources and dedicated clusters.

How does Qdrant Cloud pricing work?+

Pricing is usage-based; you pay for the compute, memory, storage, backups, and paid inference tokens your cluster consumes.

Can I migrate from OSS to Qdrant Cloud?+

Yes, Qdrant provides a migration tool and documentation to move an existing OSS deployment to the cloud.

How do I choose between Free and Standard tiers?+

Free Tier is for testing with limited resources; Standard Tier is for production workloads needing dedicated clusters and higher availability.

What’s the difference between Managed, Hybrid, and Private Cloud?+

Managed Cloud is fully run by Qdrant; Hybrid lets you use your own infrastructure with Qdrant’s control plane; Private Cloud offers isolated on-prem deployments.

How is billing calculated month-to-month?+

Billing is hourly based on actual resource usage (vCPU, memory, storage, backups, and inference tokens) and summarized monthly.

Summarized by DevHunt from qdrant.tech · Oct 2, 2026. Details may change; check the official site.