Qdrant: Vector Databases

Founded 2021|Berlin, Germany|11-50 people|Open Source

What is Qdrant?

Qdrant is a high-performance open-source vector database written in Rust, optimized for speed and reliability. It supports advanced filtering with payload indexes, quantization for memory efficiency, and distributed deployments for horizontal scaling.

Qdrant offers both a self-hosted open-source version and a managed Qdrant Cloud service with free, hybrid cloud, and enterprise tiers. The Rust-based architecture provides memory safety without garbage collection overhead, leading to consistently low latency and high throughput.

Founded in 2021 and headquartered in Berlin, Germany, Qdrant has raised $37.8M in total funding including a $28M Series A led by Spark Capital in January 2024. The company is popular with teams that need production-grade vector search with fine-grained control over indexing and query parameters.

Key features

Core capabilities this platform advertises.

High-performance open-source vector search
Written in Rust for speed
Advanced filtering with payload indexes
Distributed deployment support
On-disk storage for cost efficiency

Strengths and tradeoffs

What this tool does well, and the limitations to keep in mind.

Pros

Written in Rust for exceptional performance and memory safety
Advanced filtering with rich payload indexes during search
Built-in quantization reduces RAM usage by up to 97%
Flexible deployment: self-hosted, cloud, or hybrid
ACID-compliant operations with point-in-time consistency

Cons

Horizontal scaling features still evolving compared to mature systems
Higher learning curve than simpler alternatives like pgvector
Data ingestion hiccups possible with very large initial datasets
Smaller community and ecosystem compared to Pinecone

Plans & pricing

What's included in each plan, and how the tiers compare.

Free Cloud

Free

1 GB storage
Community support
Single-node deployment

Cloud (Pay-as-you-go)

From $0.024/hr

Hourly

Scalable clusters
Multiple regions
Automatic backups

Hybrid Cloud

Custom

Contact sales for a quote

Deploy in your VPC
Managed control plane
Data sovereignty

Enterprise

Custom

Contact sales for a quote

Dedicated support
Custom SLAs
Advanced security
On-premise option

View official pricing page

Common use cases

Engineering teams who need a fast, self-hosted vector database with strong filtering

High-throughput vector search
Production RAG with complex filtering
Self-hosted vector infrastructure
Real-time similarity matching
Cost-efficient large-scale deployments

Using Qdrant with Respan

Respan integrates with Qdrant to provide end-to-end observability for vector search pipelines. Teams can monitor retrieval accuracy, track query performance, and evaluate how vector search results impact downstream LLM outputs.

Monitor Qdrant query latency and throughput in production
Evaluate retrieval quality and relevance scoring
Track vector search costs across deployment configurations
Optimize embedding and indexing strategies with performance data

Run Qdrant in production with Respan

10KFree traces/mo

1,000+Models

5 minSetup

Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 1,000+ models through one gateway.

Try Respan free

Best Qdrant alternatives & competitors

Top companies in Vector Databases you can use instead of Qdrant.

Milvus

Billion-scale vector search