Which is better for vector search, Milvus or Qdrant?

When deciding between Milvus and Qdrant for vector search, a glance at their GitHub repositories reveals essential characteristics that may influence your choice. Notably, Milvus boasts over 45,552 stars, making it a favorite in the vector database community, while Qdrant, a robust contender, has garnered 33,828 stars. This popular attention is not just a metric of community appreciation; it reflects the underlying performance and reliability that users expect from these solutions.

Overview of Candidates

• Milvus

  • Stars: 45,552
  • Last Updated: Just now; last push on August 7, 2026
  • License: Apache-2.0
  • Maintenance Status: ✅ Actively maintained
  • Open Issues: 1,246
  • Contributors: 365
  • Description: A high-performance vector database tailored for scalable vector approximate nearest neighbor (ANN) search with support for real-time data streaming and a cloud-native architecture.

• Qdrant

  • Stars: 33,828
  • Last Updated: Just now; last push on August 7, 2026
  • License: Apache-2.0
  • Maintenance Status: ✅ Actively maintained
  • Open Issues: 668
  • Contributors: 191
  • Description: A high-performance vector search engine and database that emphasizes a production-ready service capable of handling a vast array of neural network applications with features for extended filtering and rich capabilities.

Maintenance and Activity Comparison

Both projects display strong signs of ongoing maintenance, with latest code pushes happening today, which is a great indicator of an actively developing repository. Milvus, however, has significantly more contributors and open issues, suggesting a broader user base and perhaps more complex community engagement.

To evaluate their performance and capabilities further, we see:

• Milvus positions itself as a leader in high-volume, real-time vector searches—perfect for modern AI applications that demand immediate access to large vector datasets and allow for hybrid search capabilities (vector search combined with structured queries).
• Qdrant, meanwhile, emphasizes usability for developers with a rich set of features for managing vector data and is also available in a managed cloud version, which might be attractive for those looking to minimize infrastructure management burdens.

Use Cases and Best Fit

If your project involves high concurrency and requires handling hundreds of thousands of complex vector queries while ensuring real-time updates, Milvus could be the optimal choice. For cases where flexible filtering capabilities and effective integration with AI systems are crucial, Qdrant might serve you better.

Verdict:

• Choose Milvus if you need a well-supported, feature-rich environment for managing vector data at scale.
• Choose Qdrant for a straightforward, powerful vector search engine that emphasizes ease of use and efficient integration with existing AI workflows.

In conclusion, both Milvus and Qdrant have their merits and flaws. Your specific use case—such as scaling needs, developer proficiency, and infrastructure preferences—should ultimately dictate your choice.