Qdrant vs Milvus

by Gralio Mar 11, 2025

Qdrant and Milvus are both vector databases designed for AI and ML applications. Qdrant focuses on cloud-native features and ease of use through its API and SDKs, while Milvus emphasizes open-source flexibility and community support. Both offer efficient vector similarity search and cater to various AI applications, but their deployment and integration approaches differ.

At Gralio.ai we help to simplify your decision-making process by offering detailed, side-by-side software comparisons like this one, to help you confidently choose the tool that aligns with your business goals.

This comparison was created by analysing 20 reviews and 60 websites, saving 36 minutes of reading.

Qdrant

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Milvus

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About

Qdrant is an open-source vector database designed for AI and machine learning applications. It allows your applications to store, search, and analyze large amounts of complex data, like images and text, based on their meaning and relationships to each other. This makes it ideal for building advanced search engines, recommendation systems, and AI-powered features. Qdrant prioritizes speed, scalability, and efficient use of resources.

Milvus is a free, open-source database specifically designed for managing and searching large collections of AI data. If your company uses AI that analyzes images, video, or text, Milvus can help you quickly find similar items within those collections. It is commonly used in applications like image recognition, recommendation systems, and fraud detection. Milvus is known for its speed, ability to handle massive datasets, and ease of use.

Summary

Main difference

Qdrant is a good choice if you need a cloud-native vector database with easy API integration and efficient data management. Milvus is better suited for those who prefer an open-source solution with broad platform support and community-driven development.

Relative strengths of Qdrant (compared to Milvus)

Easier API integration and deployment with various SDKs and a Cloud API.
Cloud-native architecture with built-in scalability and high-availability features.
Efficient data storage and compression using quantization and delta encoding.

Relative weaknesses of Qdrant (compared to Milvus)

Limited community support compared to the larger open-source community of Milvus.
Less emphasis on cross-platform support outside of its Cloud API and provided SDKs.
Fewer readily available integrations with third-party tools compared to Milvus’s flexible architecture.

Who should use Qdrant VS. Milvus

Qdrant is an open-source vector database for AI and ML applications. It excels at storing, searching, and analyzing complex data like images and text, making it ideal for advanced search engines, recommendation systems, and other AI-powered features. Qdrant prioritizes speed, scalability, and efficient resource use, especially for growing businesses.

Milvus is an open-source vector database for storing and searching unstructured data, beneficial for AI applications. It's praised for speed and scalability, catering to businesses of all sizes building AI-powered applications needing to manage extensive unstructured data. Consider Milvus if your AI analyzes images, video, or text and requires efficient similarity searches within large datasets.

Best for medium to large businesses (101+ employees) seeking AI/ML solutions.
Particularly well-suited for E-commerce, IT/Software, and Marketing/Advertising.

Ideal for small to large businesses seeking efficient AI data management.
Suitable for any industry leveraging AI-powered applications.

Qdrant and Milvus features

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High-Performance Vector Search

Supported

Qdrant offers efficient similarity search for high-dimensional vectors, suitable for large-scale AI applications.

Supported

Milvus supports efficient similarity search for high-dimensional vectors, ideal for large-scale AI applications.
Cloud-Native Scalability & High-Availability

Supported

Qdrant Cloud supports horizontal and vertical scaling with zero-downtime upgrades.

Supported

Milvus supports cloud-native scalability and high availability via its distributed architecture and managed cloud services.
Easy API Integration & Deployment

Supported

Qdrant offers a RESTful API and SDKs in multiple languages for easy integration. Deployment options include a Cloud API, but Docker support is not explicitly mentioned.

Supported

Milvus offers Docker deployment and a query language API for integration.
Efficient Data Storage & Compression

Supported

Qdrant supports efficient storage and compression with quantization and delta encoding, reducing storage costs.

Partially supported

Milvus supports efficient storage via compaction and integration with Parquet files, which offer compression.
Vector similarity search

Supported

Qdrant is a vector database designed for similarity search using various methods like ANN.

Supported

Milvus supports searching for similar vectors using various metrics.
SQL support

Not supported

Qdrant uses a RESTful API for querying and does not support SQL.

Partially supported

Milvus does not directly support SQL but can integrate with tools that do.

Qualities

Value and Pricing Transparency

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Reliability and Performance

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Scalability

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Qdrant and Milvus Pricing

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Qdrant and Milvus review insights

Users love

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Qdrant and Milvus Ratings

G2

5.0/5

(12)
Glassdoor

1.0/5

(1)

G2

4.7/5

(8)

Company health

Employee growth

65% increase in the last year

26% increase in the last year

Web traffic

14% increase in the last quarter

30% increase in the last quarter

Financing

October 2023 - $38M

May 2022 - $113M

How does Qdrant's cloud offering compare to Milvus's self-hosting focus?

Qdrant offers a cloud-based solution alongside its open-source offering, providing a managed service for users who prefer not to manage their own infrastructure. Milvus, while also open-source, primarily focuses on self-hosting, requiring users to set up and maintain their own deployments. This means Qdrant offers a more convenient option for those seeking a cloud-based vector database, while Milvus caters to users who desire more control over their infrastructure and deployment.

Which product offers better integration with existing Python workflows?

Milvus offers better integration with existing Python workflows due to its explicit mention of cross-platform automation scripts available through its Python SDK. While Qdrant mentions SDKs in multiple languages, it doesn't specifically highlight Python scripting for automation, suggesting Milvus may be a more seamless fit for Python-centric environments.

What are the advantages of Qdrant?

Qdrant offers cloud-native scalability and high-availability features, potentially simplifying infrastructure management and ensuring application reliability. It emphasizes efficient data storage and compression techniques, which can lead to cost savings. Qdrant also provides a RESTful API and multiple SDKs, facilitating easier integration with existing systems.

What are the disadvantages of Qdrant?

Qdrant's limited documentation and community support can be a significant disadvantage, especially for users new to vector databases. Its reliance on a cloud API for scaling and high availability might present vendor lock-in concerns and potential cost implications compared to self-hosting options. While Qdrant offers a RESTful API, the lack of explicit Docker support might complicate certain deployment scenarios. Finally, the single negative Glassdoor review, while not statistically significant, raises potential concerns about user experience that would need further investigation.

Alternatives to Qdrant and Milvus

Weaviate

Weaviate is a specialized database designed for AI applications. It allows businesses to build features like semantic search by making sense of relationships between data, not just keywords. It's open-source, meaning it's free to use and adaptable. Weaviate works well with existing AI tools and offers flexible setup options for different needs.

Pinecone

Pinecone is a cloud-based database designed specifically for AI applications. Unlike traditional databases, it focuses on storing and quickly searching through "vectors," which are mathematical representations of data like text, images, or audio. This allows companies to build AI features that can, for example, instantly find the most relevant information from millions of documents, power accurate recommendation systems, or analyze complex patterns. Pinecone manages all the technical aspects, so developers can focus on building their applications without worrying about infrastructure.

Zilliz

Zilliz Cloud is a cloud-based platform designed to manage and search massive volumes of data, perfect for tasks like product recommendations and identifying patterns. What sets it apart is its ability to find data similarities, not just exact matches, which is crucial for understanding complex data like images, text, and audio. Built on the open-source platform Milvus, Zilliz Cloud offers a user-friendly way to leverage this powerful technology without the headache of managing infrastructure. This makes it ideal for companies looking to enhance search accuracy, improve recommendations, or implement AI-driven insights.

ArangoDB

ArangoDB is a versatile database designed for modern applications. It combines graph, document, and other data models, allowing businesses to manage complex, interconnected data effectively. This flexibility simplifies development and boosts performance compared to traditional databases. ArangoDB is trusted by leading companies across industries for various applications like fraud detection, network management, and supply chain optimization.

Elastic Enterprise Search

Elastic Enterprise Search lets you create modern search experiences for your company's website, online store, internal documents, and customer support resources. Built on the popular Elasticsearch platform, it offers AI-powered features like relevant search suggestions and detailed analytics to understand user behavior. It's designed to handle large amounts of data and can be customized to fit your business needs, whether you need a simple website search or a complex e-commerce system.

MongoDB

MongoDB is a popular database that stores information in flexible documents instead of rigid tables. This makes it easy for developers to work with and allows for changes as your needs evolve. It's designed for modern applications and scales easily to handle large amounts of data. MongoDB is used by companies of all sizes and industries, particularly for managing customer data, online content, and real-time analytics.

Page co-authored by

Michal Kaczor

CEO at Gralio

Michal has worked at startups for many years and writes about topics relating to software selection and IT management. As a former consultant for Bain, a business advisory company, he also knows how to understand needs of any business and find solutions to its problems.

Tymon Terlikiewicz

CTO at Gralio

Tymon is a seasoned CTO who loves finding the perfect tools for any task. He recently headed up the tech department at Batmaid, a well-known Swiss company, where he managed about 60 software purchases, including CX, HR, Payroll, Marketing automation and various developer tools.

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