Zilliz Cloud vs Vearch Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Zilliz Cloud 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.
Zilliz Cloud and Vearch are purpose-built vector databases. This post compares their vector search capabilities.
Zilliz Cloud: Overview and Core Technology
Zilliz Cloud is a fully managed vector database service built on top of the open-source Milvus engine. It helps developers and organizations to handle large scale AI applications by storing, managing and searching vector embeddings efficiently. It takes care of infrastructure for you, so you can focus on building AI features instead of managing databases.
One of the key advantages of Zilliz Cloud is the automatic performance optimization. The system has AutoIndex technology which will choose the best indexing method for your data and use case. So you don’t have to spend time tuning parameters or comparing different index types. The platform also uses IVF (Inverted File) and graph-based techniques to speed up similarity search across large datasets.
The platform has enterprise features. You can deploy your vector databases across AWS, Azure or Google Cloud, with options to use Zilliz’s fully managed service or bring your own cloud account (BYOC). For organizations that handle sensitive data, Zilliz Cloud has security controls like encryption, access management and compliance tools. The system also supports different consistency levels so you can balance between fast updates and strong data consistency based on your needs.
Cost management is another important aspect of Zilliz Cloud. The platform uses tiered storage to automatically move less accessed data to cheaper storage options, so you can reduce cost without affecting performance. You can also choose compute resources that match your workload - for example, use more powerful instances for heavy processing tasks and lighter ones for simple queries. This flexibility helps you to optimize your spending while maintaining good performance.
For AI applications that need to search different types of data together, Zilliz Cloud supports hybrid search. You can search across text embeddings, image vectors and other data types in a single query. The platform also supports various similarity metrics like Cosine, Euclidean and Inner Product so it’s suitable for different machine learning models and use cases. As your data grows, the system can scale horizontally by adding more resources automatically so you can maintain good performance even under heavy workload.
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
Zilliz Cloud and Vearch take different approaches to vector search implementation. Zilliz Cloud uses AutoIndex technology to automatically select optimal indexing methods based on your data and use case. It combines IVF and graph-based techniques for fast similarity searches.
Vearch supports multiple indexing methods, specifically IVFPQ and HNSW, and lets you choose between CPU and GPU implementations. While this offers more control, it requires more technical knowledge to optimize performance for your specific use case.
Data Handling
Zilliz Cloud excels in hybrid search capabilities, allowing you to search across text embeddings, image vectors, and other data types in a single query. It supports various similarity metrics (Cosine, Euclidean, Inner Product) to accommodate different machine learning models.
Vearch also offers hybrid search functionality, combining vector searches with traditional filtering options. You can search vectors while applying filters like numerical ranges or text categories. It supports multiple vector fields in a single document, making it suitable for complex data structures.
Scalability and Performance
Zilliz Cloud handles scalability through automatic horizontal scaling. When your data or workload grows, the system adds resources automatically to maintain performance. It also implements tiered storage, moving less-accessed data to cheaper storage options without compromising performance.
Vearch uses a distributed architecture with specialized nodes (master, router, and partition server) for different tasks. This design allows you to scale by adding more machines to handle increased data volume or traffic. The system can search millions of vectors in milliseconds, making it suitable for large-scale applications.
Flexibility and Customization
Zilliz Cloud provides flexibility in deployment options. You can use it across AWS, Azure, or Google Cloud, either as a fully managed service or by bringing your own cloud account (BYOC). The platform also offers adjustable consistency levels to balance between update speed and data consistency.
Vearch gives developers more direct control over the system's configuration. You can fine-tune indexing methods and choose between CPU/GPU implementations. It supports real-time data updates, ensuring search results stay current as your data changes.
Integration and Ecosystem
Zilliz Cloud, built on the open-source Milvus engine, benefits from that ecosystem's integrations and community support. The platform focuses on providing a managed service that handles infrastructure management for you.
Vearch offers a Python SDK for development and testing, making it accessible for Python developers. However, the documentation focuses less on managed services and more on direct implementation details.
Ease of Use
Zilliz Cloud prioritizes ease of use through automation. Its AutoIndex technology removes the need to manually tune parameters or compare index types. The managed service aspect means you don't need to handle infrastructure management.
Vearch requires more hands-on management but offers more direct control over system behavior. While it provides helpful tools like the Python SDK, you'll need more technical expertise to optimize its performance.
Cost Considerations
Zilliz Cloud implements cost optimization through tiered storage and flexible compute resource allocation. You can match resources to workload requirements, using more powerful instances for heavy processing and lighter ones for simple queries.
Vearch, being a self-managed solution, requires you to handle infrastructure costs directly. While this might be more cost-effective for teams with existing infrastructure expertise, it requires more active management of resources.
Security Features
Zilliz Cloud includes enterprise-grade security features: encryption, access management, and compliance tools. The managed service aspect means security updates and patches are handled automatically.
Vearch's security features aren't explicitly detailed in the provided information, suggesting security implementation might need to be handled at the infrastructure level.
When to Choose Each Technology
Zilliz Cloud
Zilliz Cloud shines in enterprise environments where teams need to handle large-scale AI applications without managing complex infrastructure. It's the right choice for organizations building recommendation systems, content similarity engines, or image search applications that need enterprise-grade security and automatic scaling. The platform works especially well for teams that want to focus on AI feature development rather than database management, or when you need to deploy across multiple cloud providers with consistent performance.
Vearch
Vearch fits best in scenarios where teams need granular control over their vector search implementation and have the technical expertise to optimize it. It's ideal for applications that need real-time updates with specific performance requirements, such as e-commerce platforms with frequent inventory changes or media platforms that need to index new content immediately. Vearch also works well for teams that already have established infrastructure and want to integrate vector search capabilities without moving to a managed service.
Conclusion
Zilliz Cloud stands out for its managed service approach, automatic optimization features, and enterprise-ready security controls. It removes infrastructure complexity while providing the tools needed for sophisticated AI applications. Vearch, on the other hand, offers more direct control over system behavior and real-time update capabilities, making it suitable for teams that want to fine-tune their vector search implementation. Your choice should align with your team's technical expertise, scaling needs, and whether you prefer a managed service or hands-on control. Consider your data volume, update frequency, security requirements, and whether you need features like automatic optimization or prefer manual configuration.
Read this to get an overview of Zilliz Cloud 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?
- Zilliz Cloud: Overview and Core Technology
- What is Vearch**? Overview and Core Technology**
- Key Differences
- Ease of Use
- Zilliz Cloud
- Vearch
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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