Milvus vs Qdrant
No clear leader: Milvus (57.2) and Qdrant (55.8) are within the 5-point margin; treat as a tie. The attribute-by-attribute breakdown below, with a source and date on every value, is the honest way to compare them.
Capabilities
Feature-by-feature on the axes that matter for vector databases. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
Milvus
A high-performance, distributed vector database that enables fast similarity search across billions of high-dimensional vectors, supporting real-time updates, flexible metadata filtering, and hybrid search capabilities for AI and machine learning applications.
Qdrant
A database system designed for storing and searching vectors with comprehensive filtering capabilities. It provides production-ready APIs and tools for semantic similarity matching and embedding-based applications.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
Milvus
- OpenAI
- Sentence Transformers
- BGE-M3
- SPLADE
- Jina AI
- BM25
- PyMilvus
- Jina
Qdrant
- Slack
- Adobe
- Hubspot
- Arize
- Google DeepMind
- Qualcomm
- Cohere
- AWS
- GCP
- Azure
- LangChain
- FastEmbed
- Google Cloud Storage
- Azure Blob Storage
Milvus or Qdrant: which one depends on you
A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.
Free to run, no account needed to start. How the evaluation works
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.