Pinecone
Managed vector database for AI applications
Qdrant
Fast, scalable vector search engine for AI-driven applications
Qdrant delivers high-performance vector similarity search with payload filtering, hybrid sparse-dense queries, and horizontal scaling, enabling developers to build semantic search, recommendation, and classification systems quickly.
Overview
Overview
Qdrant is a Rust‑based vector similarity engine that stores vectors together with arbitrary JSON payloads. It lets developers perform nearest‑neighbor searches while filtering on metadata, making it suitable for semantic search, recommendation, and classification workloads.
Capabilities & Deployment
The service supports dense vectors, sparse token‑level vectors for hybrid search, and built‑in vector quantization to cut RAM usage. Horizontal scaling is achieved through sharding and replication, with zero‑downtime rolling updates. Clients are available for Python, Go, Rust, JavaScript/TypeScript, .NET, and Java, and the API can be accessed via REST or gRPC. Qdrant can be run locally with Docker, embedded via the in‑memory Python client, or consumed as a fully managed Qdrant Cloud offering that includes a free tier.
Integrations
Qdrant integrates with popular AI stacks such as LangChain, LlamaIndex, Haystack, and OpenAI’s retrieval plugins, allowing seamless use as a persistent vector store for large language model applications.
Highlights
Advanced payload filtering with JSON support and complex boolean logic
Hybrid search combining dense vectors and sparse token-level vectors
Built-in vector quantization and on-disk storage to reduce RAM usage
Horizontal scaling via sharding, replication, and zero-downtime rolling updates
Pros
- Rust implementation provides high throughput and low latency
- Rich filtering enables fine-grained business logic
- Multiple client libraries cover major programming languages
- Managed Qdrant Cloud offers a free tier for experimentation
Considerations
- Requires understanding of vector embeddings to get best results
- On-disk storage may need SSD for optimal performance
- Advanced features (e.g., quantization) add configuration complexity
- Community support may be less extensive than larger commercial solutions
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When teams consider Qdrant, these hosted platforms usually appear on the same shortlist.
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Fit guide
Great for
- Teams building semantic search or recommendation engines
- Applications needing fast similarity lookup with metadata filters
- Projects that require horizontal scaling across multiple nodes
- Developers who prefer self-hosted or cloud-managed vector databases
Not ideal when
- Simple keyword-only search without vector embeddings
- Environments lacking Rust or Docker runtime support
- Use cases demanding sub-millisecond latency on massive datasets without hardware acceleration
- Organizations that need a fully managed SaaS with no self-hosting responsibilities
How teams use it
Semantic Text Search
Find relevant documents based on meaning rather than keywords, improving retrieval accuracy for chatbots and knowledge bases.
Similar Image Search for Food Discovery
Enable users to locate dishes by visual similarity, boosting engagement in culinary apps.
Extreme Classification in E-commerce
Classify millions of products into fine-grained categories using vector similarity, enhancing catalog organization and recommendation relevance.
Personalized Recommendations
Generate real-time item suggestions by matching user embeddings with product vectors, driving higher conversion rates.
Tech snapshot
Rust88%
Python11%
Shell1%
C1%
Nix1%
Dockerfile1%
Frequently asked questions
How can I run Qdrant locally for development?
Use the official Docker image (`docker run -p 6333:6333 qdrant/qdrant`) or the in-memory Python client (`QdrantClient(":memory:")`).
Which programming languages have official client libraries?
Official clients are available for Python, Go, Rust, JavaScript/TypeScript, .NET/C#, and Java.
Does Qdrant support persistent on-disk storage?
Yes, it offers on-disk storage with vector quantization and async I/O, allowing data to survive restarts.
Is there a managed cloud offering?
Qdrant Cloud provides a fully managed service with a free tier, handling scaling and maintenance.
How does payload filtering work?
Payloads are arbitrary JSON attached to vectors; queries can filter on any field using keyword, numeric range, geo-location, or full-text conditions combined with boolean clauses.
Project at a glance
Active- Stars
- 28,308
- Watchers
- 28,308
- Forks
- 2,005
LicenseApache-2.0
Repo age5 years old
Last commit4 hours ago
Self-hostingSupported
Primary languageRust
Last synced 4 hours ago