Free cloud cluster
$0USD / free forever
0.5 vCPU, 1 GB RAM and 4 GB disk.
AI Search & Research Tools
- Introduction
- High-performance vector search engine for AI retrieval, RAG, semantic search, recommendations, and AI agents, deployable on cloud, hybrid, or edge.
- Added on
- 11/14/2026
- Pricing
- Freemium
- Official links
- Website, company & support ↗
PRODUCT OVERVIEW
What is Qdrant?
Qdrant is a high-performance vector search engine built in Rust, designed for production-grade AI retrieval. It supports dense and sparse vectors, native hybrid search with BM25, SPLADE++, and miniCOIL, multivector representations, and efficient one-stage metadata filtering during HNSW traversal. Developers can use REST, gRPC, or official clients (Python, JavaScript, etc.) and a built-in Web UI to explore collections and test queries. Qdrant powers RAG, semantic search, recommendation systems, AI agents, and anomaly detection. It is available as open-source software, Qdrant Cloud (managed on AWS, GCP, Azure), Hybrid Cloud, Private Cloud, and Edge (Beta). A free tier and paid plans are offered.
How to use Qdrant
- 1
Start with Qdrant Cloud
Sign up for a free Qdrant Cloud account to spin up a cluster in seconds.
- 2
Use the API
Interact with Qdrant via REST, gRPC, or official clients such as Python and JavaScript.
- 3
Explore with Web UI
Use the built-in Web UI to inspect collections, test vector and metadata queries, and apply filters.
- 4
Deploy anywhere
Choose Qdrant Cloud, Hybrid Cloud, Private Cloud, or Edge (Beta) based on your infrastructure needs.
Qdrant Features
Native hybrid search (dense + sparse) with BM25, SPLADE++, and miniCOIL
Expansive metadata filters with nested, text, geo, and has_vector conditions
Built-in multivector support for multimodal retrieval
Efficient one-stage filtering during HNSW traversal
Full-spectrum reranking with score boosting, late interaction models, and MMR
Real-time indexing without rebuilding the entire index
Memory-efficient storage with asymmetric, scalar, and binary quantization
Developer-friendly APIs: REST, gRPC, and official clients
Built-in Web UI for visual exploration and query testing
Native cloud inference for text and image embeddings
Qdrant Use Cases
RAG and GenAI context retrieval
AI agents with persistent memory
Semantic search beyond keywords
Recommendation systems with real-time similarity matching
Data analysis and anomaly detection
Qdrant FAQs
Qdrant is a high-performance vector search engine for AI retrieval, supporting dense and sparse vectors, hybrid search, and advanced filtering.
Qdrant can be deployed as open-source software, Qdrant Cloud (managed on AWS, GCP, Azure), Hybrid Cloud, Private Cloud, and Edge (Beta).
Yes, Qdrant supports native hybrid search blending dense and sparse vectors, with BM25, SPLADE++, and miniCOIL.
Qdrant provides REST, gRPC, and official clients for Python, JavaScript, and other languages.
Yes, Qdrant Cloud offers a free tier; paid plans are also available.
Yes, Qdrant is designed for RAG and GenAI, delivering context-rich answers with hybrid retrieval and metadata filters.
The built-in Web UI allows you to explore collections, test vector and metadata queries, apply filters, and inspect results.
Yes, Qdrant supports asymmetric, scalar, and binary quantization to reduce memory usage.
Qdrant Official Information
- Company
- Qdrant
- Website
- https://qdrant.tech/
- Documentation
- https://qdrant.tech/documentation/
- Contact
- https://qdrant.tech/contact-us/
Qdrant Pricing
Official pricing and capacity calculator checked 2026-11-09. Paid pricing depends on provider, region, memory, storage, vector dimensions and replication. The unconfigured calculator gives no quote; prior generic $25/month and $0.12/hour are not verified.
Standard cloud
Price to be verified
COMMUNITY REVIEWS
Qdrant User Reviews
No reviews yet.
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