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DirectoryAI & ML PlatformsVector Databases & RAGQdrant

Qdrant

About Qdrant

Qdrant offers a production-grade vector database designed to power AI-driven search and retrieval applications at enterprise scale. Built entirely in Rust, it delivers real-time indexing, memory-efficient storage, and advanced filtering capabilities optimized for modern AI workloads. The platform supports hybrid search combining dense and sparse vectors, enabling more expressive and relevant search results across diverse data types.

Targeted at enterprises requiring scalable, secure, and flexible AI search infrastructure, Qdrant supports deployment across cloud, hybrid, on-premises, and edge environments. Its enterprise-ready features include SOC 2 compliance, granular access controls, zero-downtime upgrades, and multi-tenancy, ensuring robust security and operational continuity. Qdrant is ideal for industries such as e-commerce, healthcare, legal tech, and hospitality that demand high accuracy and low-latency vector search integrated with business logic.

How to evaluate Vector Databases & RAG

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Qdrant. It comes from our Vector Database & AI Search buyer guide.

25%
Retrieval Quality & Index Options
ANN algorithms offered (HNSW, IVF/IVF-PQ, DiskANN, GPU/CAGRA), achievable recall@k at your latency budget, quantization for memory savings, and whether the index supports real-time inserts or needs rebuilds
20%
Hybrid Search & Filtering at Scale
Native vector+keyword (BM25 / sparse) fusion, reranking, and — critically — in-graph metadata pre-filtering that holds recall under selective filters rather than degrading to brute force or empty result sets
20%
Scale, Performance & Freshness
Vector ceiling per node and horizontally, p95/p99 latency and QPS under your real filters, multi-tenancy and namespaces, multi-region, and how fast new or updated embeddings become queryable (insert-to-searchable lag)
15%
Operating Model & Lock-In
Managed SaaS vs. self-hosted vs. bring-your-own-cloud, license terms (Apache 2.0 / BSD vs. proprietary), data-export and migration path, and how much sync/ETL you take on if vectors live apart from the source data
10%
Security, Governance & Residency
RBAC and API-key scoping, encryption in transit and at rest, SOC 2 / ISO 27001 / GDPR posture, data residency and VPC/air-gapped options, and inherited governance (e.g. Unity Catalog, platform IAM) where applicable
10%
Developer Experience & Ecosystem
Client SDKs and API ergonomics, LangChain / LlamaIndex / framework integrations, built-in or hosted embedding and reranking, docs quality, and operational maturity (backups, observability, upgrades)

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Quick Facts

qdrant.tech
CategoryAI & ML Platforms
SubcategoryVector Databases & RAG

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