Qdrant vs Milvus: Which Vector Database Should You Choose?

Which vector database should you choose for your AI-powered application, Qdrant or Milvus?

As the need for high-dimensional data storage grows in modern AI use cases like semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG), vector databases have become essential. 

In this article, we compare Qdrant vs Milvus, two of the most popular vector databases, based on architecture, performance, and ideal use cases. You’ll get a practical breakdown of insertion speed, query latency, indexing, and throughput, backed by real benchmark tests using the SQuAD dataset. 

Whether you’re building lightweight prototypes or enterprise-scale systems, this guide will help you choose the right tool based on your specific needs. Read on to find out which vector DB aligns best with your tech stack and scalability goals.

What is a Vector Database?

Vector databases are designed to store, index, and then query high-dimensional vector embeddings in a storage system. In contrast to traditional databases that store structured data, vector databases are adept at handling unstructured data (like text, images, audio) and their numerical representations.

The basic problem that vector databases solve is "similarity search". Given a query vector, retrieve the most similar vectors in the dataset. This is solved using a number of approximate nearest neighbor (ANN) algorithms that sacrifice perfect accuracy for speed and scalability.

Vector DB use case:

• RAG systems: storing document embeddings for context retrieval with LLMs
• Semantic search: identifying content that is conceptually similar within text corpus
• Recommendation systems: matching users preferences and items via embeddings
• Image/video search: matching visual similarity within multimedia systems
• Anomaly detection: identifying outliers in high-dimensional feature spaces

Now we are going to look at the top vector databases that power modern AI applications. We'll compare their key features, performance, and ideal use cases. This comparison will help you choose the most suitable vector DB for your specific project requirements.

What is Qdrant?

Qdrant is a vector similarity search engine. It offers a service that is production ready, with an easy-to-use API that allows you to store, search, and manage points (i.e., vectors), It is optimized for high-performance similarity search, so it is mostly used for AI/ML purposes like semantic search, recommendation systems, and Retrieval-Augumented Generation (RAG).

Qdrant Architecture Overview:

The diagram below represents a high-level overview of some of the main components of Qdrant. Here are the terminologies you should get familiar with.

Collections: A collection is a named set of points which all consist of a vector and optional metadata (payload). All vectors in a collection have the same dimensionality and a same distance metric is used for comparisons. Qdrant also allows you to use named vectors, which each point may store multiple vectors that may all have different dimensions and metrics while using the same ID. 

Distance Metrics: These define how vector similarity is measured on a collection. The metric(s) has to be determined when the collection is created. The choice is made based on how the vector was generated. It will be usually determined by the embedding model or neural network used.

Points: Points are the core data structure inside Qdrant. Each point consists of:

→ ID: A unique identifier for the point.

→ Vector: A high-dimensional representation of the data (e.g., text, image,audio).

→ Payload: An optional JSON object that stores additional metadata, useful for filtering and enriching search results.

Storage Options:

→ In-memory storage: Keeps vectors in RAM for faster access; disk is only used for persistence.

→ Memmap storage: It will create a virtual address space that is connected with the file on disk.

Clients: Qdrant provides client libraries in several programming languages so developers can easily integrate vector search into their webpages, applications, etc.

What is Milvus?

Milvus, originally developed by Zilliz and now part of the LF AI & Data Foundation, takes a different approach with its cloud-native architecture. From the ground up, Milvus was designed for the specific purpose of maximizing large-scale implementations, which means it is focused on scalability, performance, and enterprise-grade tools for management.

Milvus Architecture Overview

Milvus’s cloud-native and highly decoupled system architecture ensures that the system can continuously expand as data grows:

Milvus employs a disaggregated architecture where compute, storage, and metadata are all decoupled and can be scaled independently based on the load. It is implemented as a microservices architecture with separate components for coordination, data nodes, query nodes and index nodes.

The architecture allows for both standalone deployments and distributed deployments. In distributed mode, Milvus employs cloud-native technologies, like Kubernetes, for orchestration and employs message queues for reliable messaging between components.

Milvus takes a "cloud-native" approach, built entirely with distributed and massively scalable computing in mind. It can operate in two modes:

1. Standalone Mode: Single instance for development and small deployments
2. Cluster Mode: Distributed architecture for production workloads

Core Components of Milvus

Milvus uses microservices across many components:

→ Coordinator Services: Manage metadata and coordinate operations

→ Worker Nodes: Handle data processing and queries 

→ Storage: Separate compute from storage for better scalability 

→ Message Queue: Ensure reliable data processing

Comparison Between Qdrant and Milvus

The architectural differences between these two databases are very large. Qdrant is built in Rust, which gives it both memory-safety and speed. Its concern with developer experience opens the door for teams of all sizes. Qdrant's rich filtering on payload makes it extremely strong as a search engine for applications that need to search based on complex metadata.

Milvus has chosen a different direction with implemented language, and many indexing types. The many index types available on Milvus create greater flexibility for developers to optimize for specific use cases, and the mature ecosystem on Milvus has many available tools and integrations.

Benchmark Setup: How Qdrant and Milvus Were Tested

In order to provide valuable comparative performance reviews, we undertook comprehensive benchmarks with realistic data and query patterns that represented practical use cases.    

The test datasets used here are SQuAD (Stanford Question Answering Dataset), which comprises over 1,00,000 question-answer pairs derived from Wikipedia articles. For that dataset, the embeddings were generated using sentence-transformers with 384-dimensional vectors. This is a common scenario for many semantic search and RAG applications using contemporary embedding models.

For search evaluation, we executed Top-3 nearest neighbour queries, as this is the common pattern for recommendation systems and semantic search applications.

The benchmarks tested the four most important metrics in production: 

1. Insertion Time for inserting bulk data
2. Indexing Time for making the data searchable
3. Query Latency for a great user experience
4. Throughput for concurrent requests

Benchmarking Qdrant and Milvus: Insertion, Latency & QPS

Qdrant Performance

Testing performance results in the following characteristics for Qdrant:

1. Insertion Time: 41.27 seconds for the entire dataset
2. Query Latency: 94.52 ms average response time
3. Throughput (QPS): 4.70 queries per second

Qdrant showed consistent performance with predictable resource usage patterns. The insertion process showed little variation with virtually no spikes in memory or CPU usage, making it a viable option for environments with limited resources.

Milvus Performance

Milvus showed quite different performance characteristics. While Milvus showed excellent performance, it was predominantly optimized for a different set of use cases:

1. Insertion Time: 12.02 seconds for data ingestion
2. Indexing Time: 0.60 seconds for indexing
3. Query Latency: 40.00 ms ms average response
4. Throughput (QPS): 46.33 queries per second

Milvus appears to have superior performance for bulk operation and concurrent query handling. Milvus is also able to separate the indexing step from the initial data loading, and so search performance can be optimized after the data has been ingested.

Performance Analysis of Milvus and Qdrant

Insertion Performance

Milvus unquestionably leads in the insertion space, with data ingestion in 12.02 seconds compared to Qdrant's 41.27 seconds. There's a 3.4x performance advantage, which is very important for use cases that constantly update data or as part of a real-time ingestion pipeline. 

This performance difference results from Milvus's optimized bulk-insertion methods, of which both Qdrant and Milvus use, and Milvus's distributed architecture, which parallelizes data processing across nodes. 

If applications need to continuously update their vector databases due to new content, the performance led by Milvus equates to better user experiences and lower operational costs.

Indexing Time

Milvus provides a number of primary custom indexing strategies that can affect query performance quite a bit. The 0.60-second indexing time in the test shows just how fast the system can re-optimize the data for search operations. The separation of insertion with indexing could also allow for different designs of data pipelines. 

Qdrant integrates indexing into the insertion process, which simplifies operations but may impact insertion speed. Conversely, Qdrant ensures that data is available for search operations without indexes needing to run on a secondary logic.

Query Latency:

Qdrant achieves a much better performance for individual query latency with response times at 94.52ms, compared to Milvus at 250.01ms. This difference of almost 2.6x resulting response time provides much better systems for real-time applications, where customers are accustomed to interacting with the system in less than a second.

This reduced latency is a consequence of the optimized query processing pipeline, and the more efficient memory consumption in comparison to Milvus. In instances of interactive interfaces, such as chatbots, recommendation systems, or search ever-changing search interfaces, the latency is directly related to end-user experience. 

Throughput (QPS)

Milvus wins in throughput scenarios with 46.33 queries per second performance compared to Qdrant's performance of 4.70 QPS. This gives Milvus almost a 10x advantage and makes Milvus a far better candidate for environments with high concurrency in which many users are concurrently querying the system. 

The high throughput of Milvus is attributed to its optimized ANN (i.e., Approximate Nearest Neighbor) engine and its batched search capabilities. Both are utilized which allows Milvus to serve many queries in a single batch smartly and tolerates traffic spikes with minimal performance degradation.

Choosing Between Qdrant and Milvus

Choosing between Qdrant and Milvus depends largely on your specific use case, infrastructure, and team expertise.

Qdrant is ideal for low-latency applications that require rich metadata filtering. Its lightweight, developer-friendly setup makes it a great choice for teams looking to get started quickly without the complexity of managing distributed systems.

Milvus, on the other hand, excels in high-throughput environments with demanding indexing needs. Its ability to handle 10x more queries per second makes it a solid option for large-scale, production-grade systems.

Evaluate your operational capabilities and long-term goals. If simplicity and speed-to-deploy matter most, go with Qdrant. If performance and scalability are your priorities, Milvus is the better fit.

Conclusion

Both Qdrant and Milvus are powerful vector databases, each excelling in different areas.

Qdrant stands out in latency-sensitive and metadata-rich use cases, offering a smooth and developer-friendly experience. On the other hand, Milvus is built for high throughput, bulk ingestion, and scalable deployments, making it ideal for production environments with heavy data workloads.

Thanks to its strong concurrency capabilities, Milvus can process large volumes of data efficiently during peak loads. Ultimately, the right choice depends on your specific use case, growth plans, and operational capacity. Consider not just your current needs, but also how your infrastructure and team can scale with the database you choose.

Frequently Asked Questions

1. What are the key differences between Qdrant and Milvus?

Qdrant is optimized for low-latency search and rich metadata filtering, making it ideal for real-time, interactive applications. Milvus, in contrast, offers higher throughput and supports more indexing options, which are better suited for large-scale deployments with heavy concurrency and bulk data ingestion.

2. Which vector database is better for Retrieval-Augmented Generation (RAG) applications?

Qdrant is often preferred for RAG due to its fast query latency and advanced metadata filtering, which helps refine context retrieval more accurately. However, Milvus can also work well if your application needs to handle a very large volume of queries concurrently.

3. Can I use Qdrant or Milvus for semantic search on text data?

Yes, both Qdrant and Milvus support semantic search. Qdrant is especially effective for lightweight semantic search systems where metadata filtering and low-latency queries are important. Milvus offers more indexing flexibility and is better for large-scale implementations.

4. How does the performance of Qdrant and Milvus compare in benchmarks? In benchmark tests using the SQuAD dataset:

• Milvus showed better insertion speed and higher throughput (46.33 QPS).
• Qdrant had lower query latency (94.52 ms), making it ideal for responsive search tasks. Milvus is better for handling bulk operations, while Qdrant is optimized for real-time querying.

5. Is Qdrant or Milvus better for beginners starting with vector databases?

Qdrant is generally better suited for beginners due to its simple setup, lightweight architecture, and developer-friendly APIs. It can be deployed with a single binary and supports easy integration through REST and gRPC. Milvus, while powerful and scalable, has a more complex, microservices-based architecture that's better suited for experienced teams working on large-scale projects.