Vector Databases · qdrant

qdrant

Qdrant is an open-source vector database and search engine written in Rust, designed to store and query embeddings for AI applications. It supports dense, sparse, and hybrid search with rich filtering, quantization, and distributed scaling capabilities.

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Source: GitHub — github.com/qdrant/qdrant

33k

GitHub stars

2.5k

Forks

Rust

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	qdrant/qdrant
Owner	qdrant
Primary language	Rust
License	Apache-2.0 — OSI-approved
Stars	33k
Forks	2.5k
Open issues	619
Latest release	v1.18.2 (2026-06-04)
Last updated	2026-07-08
Source	https://github.com/qdrant/qdrant

What qdrant is

Rust-based vector database exposing REST and gRPC APIs for similarity search across multiple vector types. Features HNSW indexing, payload-based filtering, vector quantization (up to 97% RAM reduction), horizontal sharding/replication, and hybrid search with configurable fusion strategies (RRF, DBSF).

Quickstart

Get the qdrant source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/qdrant/qdrant.git2cd qdrant3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Semantic Search and Recommendation Systems

Ideal for applications requiring neural search, semantic text matching, and content-based recommendations. Supports dense vector search with flexible filtering to constrain results by metadata.

Large-Scale ML Pipeline Integration

Well-suited for production ML workflows where embeddings need persistent storage, fast retrieval, and zero-downtime scaling. Native support for multiple vector types enables complex neural network architectures.

Faceted and Hybrid Search Applications

Excels when combining full-text (sparse vectors) and semantic (dense vectors) search in a single query. Built-in faceting and filtering make it useful for e-commerce, discovery platforms, and knowledge bases.

Implementation considerations

Vectors must be pre-computed by an embedding model (e.g., OpenAI, Hugging Face); Qdrant handles storage and search only.
Security: default Docker deployment is unauthenticated and open. Review the documented security setup before production use.
Quantization trade-off: enabling quantization reduces RAM but may impact precision; tune based on your latency and accuracy requirements.
Payload filtering is powerful but complex queries may impact search performance; test filtering strategies at scale.
Multi-vector search (dense + sparse) requires explicit configuration of fusion strategies (RRF, DBSF); choose based on domain relevance.

When to avoid it — and what to weigh

Simple SQL/Relational Workloads — If your primary need is traditional ACID transactions and SQL queries, a relational database is more appropriate. Qdrant is specialized for vector similarity, not general-purpose data management.
Real-Time Transactional Guarantees Required — While Qdrant provides persistence and replication, its guarantees are optimized for search consistency, not financial or banking transaction ACID semantics.
Extremely Resource-Constrained Environments — Although Qdrant Edge exists for lighter deployments, the main server is optimized for scale rather than minimal footprint. Edge version may still not suit severely constrained IoT scenarios.
No Embedding Generation — Qdrant stores and searches vectors but does not generate embeddings. You must provide a separate embedding model or service to encode data before ingestion.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing use in commercial and proprietary software with minimal restrictions.

Apache-2.0 is a permissive open-source license that permits commercial use, modification, and distribution. No license restrictions on commercial deployment of self-hosted Qdrant. However, the Qdrant Cloud offering is a separate commercial service with its own terms; review cloud pricing and SLAs independently. For significant commercial reliance, evaluate vendor support contracts.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Default Docker deployment runs unauthenticated and open to all interfaces; official documentation explicitly flags this. Self-hosted security depends on proper network isolation, TLS, and authentication configuration. No claim of penetration testing or third-party security audit data provided in the repository. Review security documentation before production; consider vendor support for security-critical deployments.

Alternatives to consider

Pinecone

Managed vector database with built-in security and scaling; trade-off is vendor lock-in and higher cost compared to self-hosted Qdrant.

Weaviate

Open-source vector database with GraphQL API and stronger built-in semantic features; comparable scale and flexibility but different query model.

Milvus

Distributed, open-source vector database with cloud-native architecture; strong for large-scale deployments but steeper operational complexity.

Software development agency

Build on qdrant with DEV.co software developers

Qdrant enables semantic search and AI-powered recommendations at scale. Start with our free tier or managed cloud, or run self-hosted. Let's discuss the right approach for your AI application.

Talk to DEV.co

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qdrant FAQ

Does Qdrant generate embeddings?

No. Qdrant stores and searches pre-computed vectors. You must use external models (OpenAI, Hugging Face, etc.) to encode text, images, or other data into embeddings.

Is the default Docker setup secure for production?

No. The documentation explicitly notes the default container is insecure and unauthenticated. Before production, configure TLS, authentication, and proper network isolation as per the security guide.

Can I use Qdrant for e-commerce product search?

Yes. Hybrid search (dense semantic + sparse full-text), faceting, and rich payload filtering make it well-suited for product discovery and categorization applications.

What is Qdrant Edge?

A lightweight, in-process variant designed for edge devices and offline scenarios. Data syncs with a Qdrant server. Useful for low-latency applications requiring local vector search.

Software development & web development with DEV.co

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If qdrant is part of your vector databases roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Deploy Vector Search?

Qdrant enables semantic search and AI-powered recommendations at scale. Start with our free tier or managed cloud, or run self-hosted. Let's discuss the right approach for your AI application.

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