Milvus vs Qdrant
Milvus and Qdrant are both production-ready open-source vector databases, but they serve different operational priorities. Milvus is built for teams that need to scale vector search to tens of billions of vectors with a flexible deployment ladder from a pip-installable library to fully distributed enterprise clusters. Qdrant is built for teams that need maximum search precision and developer ergonomics, with native hybrid retrieval, one-stage filtering, advanced quantization, and a Rust-powered engine that prioritizes speed and memory efficiency. The choice depends on whether your primary constraint is scale and deployment flexibility or search quality and real-time performance.
Milvus3.9Qdrant4.2
Vector Databases
Quick Comparison
| Feature | Milvus | Qdrant |
|---|---|---|
| Core Language | Go/C++ with Python SDK | Rust with Python, JavaScript, and gRPC clients |
| Deployment Model | Lite, Standalone, Distributed, Zilliz Cloud (managed) | Cloud, Hybrid Cloud, Private Cloud, Edge (Beta) |
| Hybrid Search | Metadata filtering and hybrid search supported | Native dense + sparse vector blending with BM25 and SPLADE++ |
| Quantization | Supports vector compression for large-scale deployments | Asymmetric, scalar, and binary quantization (up to 64x memory reduction) |
| Pricing Model | Contact for pricing | Free Tier free, $1 (no specific tier mentioned) |
| GitHub Stars | Not publicly listed on context data | 30,400+ stars on GitHub |
| Best For | Massive-scale GenAI apps needing tens of billions of vectors | Production AI search with real-time indexing and advanced filtering |
Milvus
- Core Language:
- Go/C++ with Python SDK
- Deployment Model:
- Lite, Standalone, Distributed, Zilliz Cloud (managed)
- Hybrid Search:
- Metadata filtering and hybrid search supported
- Quantization:
- Supports vector compression for large-scale deployments
- Pricing Model:
- Contact for pricing
- GitHub Stars:
- Not publicly listed on context data
- Best For:
- Massive-scale GenAI apps needing tens of billions of vectors
Qdrant
- Core Language:
- Rust with Python, JavaScript, and gRPC clients
- Deployment Model:
- Cloud, Hybrid Cloud, Private Cloud, Edge (Beta)
- Hybrid Search:
- Native dense + sparse vector blending with BM25 and SPLADE++
- Quantization:
- Asymmetric, scalar, and binary quantization (up to 64x memory reduction)
- Pricing Model:
- Free Tier free, $1 (no specific tier mentioned)
- GitHub Stars:
- 30,400+ stars on GitHub
- Best For:
- Production AI search with real-time indexing and advanced filtering
Community & Adoption Signals
| Metric | Milvus | Qdrant |
|---|---|---|
| GitHub stars | — | 31.0k |
| PyPI weekly downloads | 1.3M | 6.1M |
| Docker Hub pulls | 75.6M | 28.7M |
| Search interest | 3 | 5 |
As of 2026-05-04 — updated weekly.
Interface Preview
Milvus
Qdrant
Feature Comparison
| Feature | Milvus | Qdrant |
|---|---|---|
| Search Capabilities | ||
| Vector Similarity Search | Global Index for high-speed search across tens of billions of vectors with minimal performance loss | HNSW-based search built in Rust with SIMD optimizations and custom Gridstore engine |
| Hybrid Search | Metadata filtering and hybrid search capabilities with multi-vector support | Native dense + sparse vector blending in one query with BM25, SPLADE++, and miniCOIL support |
| Filtering | Metadata filtering on vector attributes during search | One-stage filtering applied during HNSW traversal with no pre- or post-filtering overhead |
| Performance & Storage | ||
| Quantization | Vector compression supported for large-scale deployments | Asymmetric, scalar, and binary quantization reducing memory by up to 64x while maintaining search quality |
| Real-Time Indexing | Batch and streaming insertion supported with indexing optimized for large datasets | Real-time indexing with vectors searchable the instant they are added |
| Memory Efficiency | Cloud-native stateless components designed for elastic scaling across large vector sets | Memory-efficient storage architecture supporting billions of vectors with minimal memory footprint |
| Deployment & Scalability | ||
| Deployment Options | Milvus Lite (pip install), Standalone (single machine), Distributed (enterprise), Zilliz Cloud (managed) | Qdrant Cloud (managed), Hybrid Cloud (BYOK), Private Cloud (air-gapped), Edge (Beta) |
| Scaling Architecture | Distributed architecture with separated storage and computation; horizontal scaling to billions of vectors | Auto-sharding and high availability on AWS, GCP, or Azure with decoupled control and data planes |
| Enterprise Security | Role-based access control with enterprise-grade security in distributed deployments | SOC 2 and HIPAA compliant with SSO, RBAC, private networking, and vector-scoped API keys |
| Developer Experience | ||
| API & SDKs | Python SDK with pip install; write once and deploy with one line of code | REST, gRPC, and official clients for Python and JavaScript with advanced HNSW control |
| Built-in Tooling | Integration with popular AI dev tools; guided notebooks for RAG, image search, and multimodal search | Built-in Web UI for exploring collections, testing queries, and inspecting results; native cloud inference |
| Reranking & Relevance | Multi-vector support for improved relevance across different embedding types | Full-spectrum reranking with score boosting, ColBERT late interaction, and MMR diversification |
| Use Cases | ||
| Primary Use Cases | RAG, image search, multimodal search, hybrid search, and Graph RAG applications | RAG, AI agents, semantic search, recommendation systems, and anomaly detection |
| Multivector Support | Multi-vector capabilities for cross-modal similarity matching | Built-in multivector for expressive, flexible, and multimodal retrieval per object |
| Community & Ecosystem | Large supportive community with extensive resources and regular Unstructured Data Meetups | 30,400+ GitHub stars, 60,000+ community members, Apache-2.0 licensed, active development |
Search Capabilities
Vector Similarity Search
MilvusGlobal Index for high-speed search across tens of billions of vectors with minimal performance loss
QdrantHNSW-based search built in Rust with SIMD optimizations and custom Gridstore engine
Hybrid Search
MilvusMetadata filtering and hybrid search capabilities with multi-vector support
QdrantNative dense + sparse vector blending in one query with BM25, SPLADE++, and miniCOIL support
Filtering
MilvusMetadata filtering on vector attributes during search
QdrantOne-stage filtering applied during HNSW traversal with no pre- or post-filtering overhead
Performance & Storage
Quantization
MilvusVector compression supported for large-scale deployments
QdrantAsymmetric, scalar, and binary quantization reducing memory by up to 64x while maintaining search quality
Real-Time Indexing
MilvusBatch and streaming insertion supported with indexing optimized for large datasets
QdrantReal-time indexing with vectors searchable the instant they are added
Memory Efficiency
MilvusCloud-native stateless components designed for elastic scaling across large vector sets
QdrantMemory-efficient storage architecture supporting billions of vectors with minimal memory footprint
Deployment & Scalability
Deployment Options
MilvusMilvus Lite (pip install), Standalone (single machine), Distributed (enterprise), Zilliz Cloud (managed)
QdrantQdrant Cloud (managed), Hybrid Cloud (BYOK), Private Cloud (air-gapped), Edge (Beta)
Scaling Architecture
MilvusDistributed architecture with separated storage and computation; horizontal scaling to billions of vectors
QdrantAuto-sharding and high availability on AWS, GCP, or Azure with decoupled control and data planes
Enterprise Security
MilvusRole-based access control with enterprise-grade security in distributed deployments
QdrantSOC 2 and HIPAA compliant with SSO, RBAC, private networking, and vector-scoped API keys
Developer Experience
API & SDKs
MilvusPython SDK with pip install; write once and deploy with one line of code
QdrantREST, gRPC, and official clients for Python and JavaScript with advanced HNSW control
Built-in Tooling
MilvusIntegration with popular AI dev tools; guided notebooks for RAG, image search, and multimodal search
QdrantBuilt-in Web UI for exploring collections, testing queries, and inspecting results; native cloud inference
Reranking & Relevance
MilvusMulti-vector support for improved relevance across different embedding types
QdrantFull-spectrum reranking with score boosting, ColBERT late interaction, and MMR diversification
Use Cases
Primary Use Cases
MilvusRAG, image search, multimodal search, hybrid search, and Graph RAG applications
QdrantRAG, AI agents, semantic search, recommendation systems, and anomaly detection
Multivector Support
MilvusMulti-vector capabilities for cross-modal similarity matching
QdrantBuilt-in multivector for expressive, flexible, and multimodal retrieval per object
Community & Ecosystem
MilvusLarge supportive community with extensive resources and regular Unstructured Data Meetups
Qdrant30,400+ GitHub stars, 60,000+ community members, Apache-2.0 licensed, active development
Our Verdict
Milvus and Qdrant are both production-ready open-source vector databases, but they serve different operational priorities. Milvus is built for teams that need to scale vector search to tens of billions of vectors with a flexible deployment ladder from a pip-installable library to fully distributed enterprise clusters. Qdrant is built for teams that need maximum search precision and developer ergonomics, with native hybrid retrieval, one-stage filtering, advanced quantization, and a Rust-powered engine that prioritizes speed and memory efficiency. The choice depends on whether your primary constraint is scale and deployment flexibility or search quality and real-time performance.
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Frequently Asked Questions
What is the main difference between Milvus and Qdrant?
Milvus is a Go/C++ vector database focused on massive-scale deployments that can handle tens of billions of vectors through its distributed architecture with separated storage and computation. Qdrant is a Rust-based vector search engine optimized for production-grade AI search with native hybrid retrieval, one-stage filtering during HNSW traversal, and advanced quantization that reduces memory usage by up to 64x. Milvus emphasizes flexible deployment tiers from lightweight prototyping to enterprise clusters, while Qdrant emphasizes search precision with features like full-spectrum reranking and built-in multivector support.
Which vector database is better for RAG applications?
Both platforms support RAG workloads effectively, but they approach the problem differently. Milvus offers guided notebooks specifically for RAG, hybrid search, and Graph RAG patterns, making it straightforward to prototype and deploy retrieval pipelines. Qdrant provides native hybrid search that blends dense and sparse vectors in a single query with BM25 and SPLADE++ support, plus full-spectrum reranking with ColBERT late interaction models. For teams that need token-level precision in retrieval, Qdrant's native hybrid capabilities give it an edge. For teams scaling RAG across massive vector collections, Milvus's distributed architecture is a strong fit.
How do Milvus and Qdrant compare on pricing?
Milvus is open-source and free to self-host, with Zilliz Cloud offering a fully managed option in both serverless and dedicated cluster configurations. Qdrant is also open-source under the Apache-2.0 license, with Qdrant Cloud providing a free tier for getting started and usage-based pricing that scales with production workloads. Qdrant's free-forever 1GB cluster on Qdrant Cloud gives teams a risk-free starting point. Both vendors offer enterprise plans through direct sales for organizations with advanced security and compliance requirements.
Can Milvus and Qdrant handle real-time data ingestion?
Qdrant explicitly supports real-time indexing where vectors become searchable the moment they are added, with no need to rebuild the entire index. Milvus supports both batch and streaming insertion optimized for large-scale datasets, with its cloud-native stateless architecture designed for continuous data ingestion. For use cases where sub-second search availability after insertion is critical, such as real-time recommendation engines or live anomaly detection, Qdrant's real-time indexing architecture is purpose-built for that pattern.
Which vector database has better community support?
Both platforms have strong open-source communities. Qdrant has over 30,400 GitHub stars and more than 60,000 community members, with active development under the Apache-2.0 license and a latest release of v1.17.1 in March 2026. Milvus has a large developer community with regular Unstructured Data Meetups and extensive documentation resources. Both projects maintain active GitHub repositories, comprehensive documentation, and responsive community channels. Qdrant's GitHub metrics are publicly stronger, while Milvus leverages its Zilliz-backed ecosystem for enterprise support.