Milvus vs Qdrant
Side-by-side — pricing, strengths, tradeoffs, and fit.
Milvus
Open-source vector database built for enterprise-scale AI
Qdrant
High-performance open-source vector search engine
At a glance
Open SourceFree Tier
● Established·From Free / Open Source
Open SourceFree Tier
● Established·From Freemium
Overview
One-liner
One-liner
Open-source vector database built for enterprise-scale AI
One-liner
High-performance open-source vector search engine
Maturity
Maturity
EstablishedMaturity
EstablishedOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableFree tier
✓ AvailableStarts at
Starts at
Free / Open Source
Starts at
Freemium
Model
Model
Flat rate
Model
Hybrid
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Enterprise architects and large teams who need to handle billions of vectors with enterprise-grade reliability, RBAC, and Kubernetes-native deployment
Organizations with compliance requirements who need a self-hosted vector database with full data control
Teams building large-scale recommendation systems, image search, or multimodal AI applications
✓ Best for
Developers and startup founders who want an open-source vector database they can self-host for full data control and cost savings
Enterprise architects who need a compliant self-hosted vector store for sensitive data
Teams building complex RAG pipelines that need rich payload filtering and hybrid search
Not ideal for
✕ Not for
Solo developers or small teams prototyping who need quick setup since Milvus has significant operational complexity
Startups who need a managed zero-ops solution without Kubernetes expertise
✕ Not for
Teams that want a zero-ops fully managed cloud vector database without any infrastructure concern
Non-technical users who need a no-code interface
Strengths
↑ Strengths
Billion-scale vectors
Multiple index types (HNSW, IVF, DiskANN)
RBAC
Multi-tenancy
↑ Strengths
Open-source Rust engine
Self-hosted and cloud
Dense and sparse vectors
Rich payload filtering
Weaknesses
↓ Weaknesses
Limited language support beyond Python and Go/C++
Complex setup for non-Kubernetes environments
↓ Weaknesses
Limited language support beyond Python and Rust
Complex setup for non-Rust environments
Performance degradation under high load without proper tuning
Smaller community compared to more established vector search engines