Vespa vs Vearch Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Vespa 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.
Vespa and Vearch are purpose-built vector databases. This post compares their vector search capabilities.
Vespa: Overview and Core Technology
Vespa is a powerful search engine and vector database that can handle multiple types of searches all at once. It's great at vector search, text search, and searching through structured data. This means you can use it to find similar items (like images or products), search for specific words in text, and filter results based on things like dates or numbers - all in one go. Vespa is flexible and can work with different types of data, from simple numbers to complex structures.
One of Vespa's standout features is its ability to do vector search. You can add any number of vector fields to your documents, and Vespa will search through them quickly. It can even handle special types of vectors called tensors, which are useful for representing things like multi-part document embeddings. Vespa is smart about how it stores and searches these vectors, so it can handle really large amounts of data without slowing down.
Vespa is built to be super fast and efficient. It uses its own special engine written in C++ to manage memory and do searches, which helps it perform well even when dealing with complex queries and lots of data. It's designed to keep working smoothly even when you're adding new data or handling a lot of searches at the same time. This makes it great for big, real-world applications that need to handle a lot of traffic and data.
Another cool thing about Vespa is that it can automatically scale up to handle more data or traffic. You can add more computers to your Vespa setup, and it will automatically spread the work across them. This means your search system can grow as your needs grow, without you having to do a lot of complicated setup. Vespa can even adjust itself automatically to handle changes in how much data or traffic you have, which can help save on costs. This makes it a great choice for businesses that need a search system that can grow with them over time.
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 Capabilities and Performance
Both Vespa and Vearch do vector search but in different ways. Vespa combines vector, text and structured data search in one query system. This means you can search across different data types in one go, which is very useful for complex applications that need to mix traditional search with vector similarity.
Vearch is vector search with a focus on similarity based queries. It supports both IVFPQ and HNSW indexing methods so you have flexibility in how you approach vector indexing. One of the advantages of Vearch is GPU acceleration support, which can give a big boost to search performance when you have the hardware.
Data Management
Vespa's data management is based on a structured document model. You can combine vectors with traditional fields and the system handles real-time indexing and updates smoothly. It supports tensor operations so you can do complex vector manipulations which can be very useful in advanced machine learning applications.
Vearch is very simple in data management. It also supports multiple vector fields and real-time updates but since it's focused on vector data it has less complexity in schema definition. The system has built-in data validation and flexible schema definitions that can adapt to different use cases.
Scalability Architecture
Vespa's distributed architecture handles scalability through auto sharding and replication. The system allows independent scaling of content and serving nodes and has built-in load balancing that manages request distribution. One of the advantages is that Vespa doesn't require external coordination service, so it simplifies the operational overhead.
Vearch has a 3 node architecture design. Master nodes manage the cluster, router nodes handle requests and partition servers store and compute data. This separation of concerns allows you to scale specific parts of your system. The architecture supports horizontal scaling through partition addition but requires more manual management compared to Vespa.
Integration and Ecosystem
Vespa has a full integration ecosystem. Its REST APIs cover all operations and has native Java APIs for deeper integration. It also has Kubernetes operators and built-in machine learning model serving, so it's very suitable for modern cloud native architecture.
Vearch has limited integration points with HTTP APIs and language support through its Python SDK and Go client library. While the integration options are less compared to Vespa, they cover the most common use cases and has straightforward Docker deployment.
Cost and Operation Considerations
The cost structure of these systems are very different. Vespa has self-hosted and cloud options through Vespa Cloud with linear resource scaling and built-in optimization features. This can help you manage cost as you grow but the enterprise features comes with extra cost.
Vearch being self-hosted only, the cost is tied to your infrastructure choices. The hardware requirements depends on the index type and if you plan to use acceleration features you need to consider the GPU cost. The simpler architecture can be lower operational cost for smaller deployment.
Security Implementation
Vespa has comprehensive TLS encryption, role based access control and multi-tenancy support. It uses certificate based authentication, which is enterprise grade security suitable for sensitive applications.
Vearch has basic authentication mechanisms and network isolation options. While its security features are less compared to Vespa, it covers the essential requirements for most applications. It also has custom security plugins for extra flexibility.
When to Choose Each
Vespa: Best Use Cases
Vespa is the best choice for large scale enterprise applications that need to combine multiple types of search. It shines in scenarios where you need to search vectors, text and structured data in real-time, such as e-commerce platforms with personalized product recommendations, content platforms with semantic search with strict metadata filtering or recommendation systems that need to balance similarity matching with business rules. Its strong consistency guarantees and auto-scaling capabilities makes it perfect for mission critical applications that handle sensitive data or high availability.
Vearch: Best Use Cases
Vearch is best for specialized vector search applications where GPU acceleration can bring significant performance gains. It's perfect for focused use cases like image similarity search, document embedding comparison or AI powered recommendation systems that mainly rely on vector operations. Organizations with existing GPU infrastructure or building AI applications that need fast similarity matching without complex filtering will find Vearch's simple deployment and optimization for vector operations an advantage. Its lighter architecture and specialized focus makes it a great choice for projects where vector search is the main requirement.
Conclusion
The choice between Vespa and Vearch ultimately comes down to your technical requirements, operational capabilities and business needs. Vespa is a full solution for complex large scale applications that need multiple types of search and enterprise features while Vearch is specialized vector search with GPU acceleration and simpler deployment. Consider your application’s scale, search requirements, infrastructure and team expertise when making the decision. Remember the best choice isn’t always the most feature rich but the one that fits your use case the most and can scale with you.
Read this to get an overview of Vespa 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?
- Vespa: Overview and Core Technology
- What is Vearch**? Overview and Core Technology**
- Key Differences
- Vespa: Best Use Cases
- Vearch: Best Use Cases
- Conclusion
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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