Qdrant vs Vearch Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Qdrant and Vearch, let's first explore the concept of vector databases.
A vector database is specifically designed to store and query high-dimensional vectors, which are numerical representations of unstructured data. These vectors encode complex information, such as the semantic meaning of text, the visual features of images, or product attributes. By enabling efficient similarity searches, vector databases play a pivotal role in AI applications, allowing for more advanced data analysis and retrieval.
Common use cases for vector databases include e-commerce product recommendations, content discovery platforms, anomaly detection in cybersecurity, medical image analysis, and natural language processing (NLP) tasks. They also play a crucial role in Retrieval Augmented Generation (RAG), a technique that enhances the performance of large language models (LLMs) by providing external knowledge to reduce issues like AI hallucinations.
There are many types of vector databases available in the market, including:
- Purpose-built vector databases such as Milvus, Zilliz Cloud (fully managed Milvus)
- Vector search libraries such as Faiss and Annoy.
- Lightweight vector databases such as Chroma and Milvus Lite.
- Traditional databases with vector search add-ons capable of performing small-scale vector searches.
Qdrant and Vearch are purpose-built vector databases. This post compares their vector search capabilities.
Qdrant: Overview and Core Technology
Qdrant is a vector database for similarity search and machine learning. Built from the ground up for vector data, it’s the go to choice for AI developers. Qdrant optimizes performance and can handle high dimensional vector data which is key for many modern ML models.
One of the key strengths of Qdrant is its flexible data modeling. You can store and index not just vectors but also payload data associated with each vector. This means you can run complex queries that combine vector similarity with filtering on metadata, so you can have more powerful and nuanced search. Qdrant ensures data consistency with ACID compliant transactions even during concurrent operations.
Qdrant’s vector search is at the heart of the platform. It uses a custom version of the HNSW (Hierarchical Navigable Small World) algorithm for indexing which is efficient in high dimensional spaces. The Distance Matrix API allows to calculate efficiently pairwise distances between vectors, so it’s great for tasks like clustering and dimensionality reduction - even with thousands of vectors. For scenarios where precision matters more than speed, Qdrant also supports exact search and provides visual tools to explore vector relationships through the Graph UI.
What’s special about Qdrant is its query and optimization features. Its query language works seamlessly with vector search and supports complex operations including a powerful Facet API to aggregate and count unique values in the data. Memory optimization features like on-disk text and geo indexing allow to handle large scale deployments while keeping performance through intelligent caching. Qdrant has automatic sharding and replication for scalability and supports various data types and query conditions from string matching to numerical ranges and geo-locations. The scalar, product and binary quantization features can reduce memory usage and speed up search, especially for high dimensional vectors.
You can configure the trade off between search precision and performance with both approximate and exact matching depending on your use case. The architecture is designed for real world scenarios where vector search needs to be combined with filtering and aggregation, so it’s great for building practical AI applications.
What is Vearch? Overview and Core Technology
Vearch is a tool for developers building AI applications that need fast and efficient similarity searches. It’s like a supercharged database, but instead of storing regular data, it’s built to handle those tricky vector embeddings that power a lot of modern AI tech.
One of the coolest things about Vearch is its hybrid search. You can search by vectors (think finding similar images or text) and also filter by regular data like numbers or text. So you can do complex searches like “find products like this one, but only in the electronics category and under $500”. It’s fast too - we’re talking searching on a corpus of millions of vectors in milliseconds.
Vearch is designed to grow with your needs. It uses a cluster setup, like a team of computers working together. You have different types of nodes (master, router and partition server) that handle different jobs, from managing metadata to storing and computing data. This allows Vearch to scale out and be reliable as your data grows. You can add more machines to handle more data or traffic without breaking a sweat.
For developers, Vearch has some nice features that make life easier. You can add data to your index in real-time so your search results are always up-to-date. It supports multiple vector fields in a single document which is handy for complex data. There’s also a Python SDK for quick development and testing. Vearch is flexible with indexing methods (IVFPQ and HNSW) and supports both CPU and GPU versions so you can optimise for your specific hardware and use case. Whether you’re building a recommendation system, similar image search or any AI app that needs fast similarity matching, Vearch gives you the tools to make it happen efficiently.
Key Differences
Search Methodology and Performance
Qdrant uses a custom HNSW (Hierarchical Navigable Small World) algorithm for vector indexing. It has a Distance Matrix API for clustering and dimensionality reduction. You can choose exact search when precision is key or approximate search when speed is more important.
Vearch supports multiple indexing methods (IVFPQ and HNSW) and works on CPU and GPU. You can tune your setup according to your performance needs and hardware.
Data and Flexibility
Qdrant is great with complex data. It combines vector similarity search with metadata filtering and has a Facet API for aggregating and counting unique values. It supports various data types: string matching, numerical ranges and geo-locations. It ensures data consistency with ACID compliant transactions which is important when you have concurrent operations.
Vearch supports hybrid search, you can combine vector search with standard data filtering. For example, you can search for similar products and apply price or category filters. It also supports multiple vector fields in a single document, so it's suitable for complex data structures.
Scalability
Qdrant scales with automatic sharding and replication. It has memory optimization features like on-disk text and geo indexing and intelligent caching to keep performance. Scalar, product and binary quantization features help to reduce memory usage when working with high dimensional vectors.
Vearch has a distributed cluster architecture with specialized nodes (master, router and partition server) for different parts of the operation. It's easy to scale by adding more machines as your data or traffic grows.
Integration and Development Experience
Vearch has a Python SDK for development and testing, so you can prototype quickly. It supports real-time indexing, so your search results are up to date with the data changes.
Qdrant is focused on practical AI use cases where vector search needs to work with filtering and aggregating. Its query language is integrated with vector search and has visual tools through Graph UI to explore vector relationships.
When to Choose Qdrant
Choose Qdrant when your application needs complex data operations combining vector similarity with metadata filtering. ACID compliance, query language and Facet API make it perfect for applications where data consistency and rich query capabilities are key, such as content recommendation systems, semantic search or any scenario where you need to have full control over search behavior with multiple filtering conditions.
When to Choose Vearch
Choose Vearch when you need a highly scalable vector search with real-time indexing and hardware flexibility. Distributed architecture, support for both CPU and GPU and ability to have multiple vector fields per document make it perfect for large scale AI applications like image similarity search, product recommendations or any use case where you need to scale horizontally and have fast search performance.
Summary
Both Qdrant and Vearch have robust vector search but they are good at different things. Qdrant is good at data consistency, complex querying and metadata handling with features like ACID compliance and multiple filtering options. Vearch is good at scalability, hardware flexibility and real-time indexing. Your choice should depend on your requirements - choose Qdrant if you need strong data consistency and complex query capabilities or Vearch if scalability and real-time indexing are your top priority. Consider your data volume, query complexity, hardware resources and if you need real-time updates to make the right choice for your use case.
Read this to get an overview of Qdrant and Vearch but to evaluate these you need to evaluate based on your use case. One tool that can help with that is VectorDBBench, an open-source benchmarking tool for vector database comparison. In the end, thorough benchmarking with your own datasets and query patterns will be key to making a decision between these two powerful but different approaches to vector search in distributed database systems.
Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
VectorDBBench is an open-source benchmarking tool for users who need high-performance data storage and retrieval systems, especially vector databases. This tool allows users to test and compare different vector database systems like Milvus and Zilliz Cloud (the managed Milvus) using their own datasets and find the one that fits their use cases. With VectorDBBench, users can make decisions based on actual vector database performance rather than marketing claims or hearsay.
VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.
Download VectorDBBench from its GitHub repository to reproduce our benchmark results or obtain performance results on your own datasets.
Take a quick look at the performance of mainstream vector databases on the VectorDBBench Leaderboard.
Read the following blogs to learn more about vector database evaluation.
Further Resources about VectorDB, GenAI, and ML
Chloe WilliamsChloe Williams is a technical writer at Zilliz.
- What is a Vector Database?
- Qdrant: Overview and Core Technology
- What is Vearch**? Overview and Core Technology**
- Key Differences
- When to Choose Qdrant
- When to Choose Vearch
- Summary
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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