SingleStore vs Weaviate Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare SingleStore and Weaviate, 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.
SingleStore is a distributed, relational, SQL database management system with vector search as an add-on and Qdrant is a vector database. This post compares their vector search capabilities.
SingleStore: Overview and Core Technology
SingleStore has made vector search possible by putting it in the database itself, so you don’t need separate vector databases in your tech stack. Vectors can be stored in regular database tables and searched with standard SQL queries. For example, you can search similar product images while filtering by price range or explore document embeddings while limiting results to specific departments. The system supports both semantic search using FLAT, IVF_FLAT, IVF_PQ, IVF_PQFS, HNSW_FLAT, and HNSW_PQ for vector index and dot product and Euclidean distance for similarity matching. This is super useful for applications like recommendation systems, image recognition and AI chatbots where similarity matching is fast.
At its core SingleStore is built for performance and scale. The database distributes the data across multiple nodes so you can handle large scale vector data operations. As your data grows you can just add more nodes and you’re good to go. The query processor can combine vector search with SQL operations so you don’t need to make multiple separate queries. Unlike vector only databases SingleStore gives you these capabilities as part of a full database so you can build AI features without managing multiple systems or dealing with complex data transfers.
For vector indexing SingleStore has two options. The first is exact k-nearest neighbors (kNN) search which finds the exact set of k nearest neighbors for a query vector. But for very large datasets or high concurrency SingleStore also supports Approximate Nearest Neighbor (ANN) search using vector indexing. ANN search can find k near neighbors much faster than exact kNN search sometimes by orders of magnitude. There’s a trade off between speed and accuracy - ANN is faster but may not return the exact set of k nearest neighbors. For applications with billions of vectors that need interactive response times and don’t need absolute precision ANN search is the way to go.
The technical implementation of vector indices in SingleStore has specific requirements. These indices can only be created on columnstore tables and must be created on a single column that stores the vector data. The system currently supports Vector Type(dimensions[, F32]) format, F32 is the only supported element type. This structured approach makes SingleStore great for applications like semantic search using vectors from large language models, retrieval-augmented generation (RAG) for focused text generation and image matching based on vector embeddings. By combining these with traditional database features SingleStore allows developers to build complex AI applications using SQL syntax while maintaining performance and scale.
Weaviate: Overview and Core Technology
Weaviate is an open-source vector database designed to simplify AI application development. It offers built-in vector and hybrid search capabilities, easy integration with machine learning models, and a focus on data privacy. These features aim to help developers of various skill levels create, iterate, and scale AI applications more efficiently.
One of Weaviate's strengths is its fast and accurate similarity search. It uses HNSW (Hierarchical Navigable Small World) indexing to enable vector search on large datasets. Weaviate also supports combining vector searches with traditional filters, allowing for powerful hybrid queries that leverage both semantic similarity and specific data attributes.
Key features of Weaviate include:
- PQ compression for efficient storage and retrieval
- Hybrid search with an alpha parameter for tuning between BM25 and vector search
- Built-in plugins for embeddings and reranking, which ease development
Weaviate is an entry point for developers to try out vector search. It offers a developer-friendly approach with a simple setup and well-documented APIs. Deep integration with the GenAI ecosystem makes it suitable for small projects or proof-of-concept work. The target audience for Weaviate are software engineers building AI applications, data engineers working with large datasets and data scientists deploying machine learning models. Weaviate simplifies semantic search, recommendation systems, content classification and other AI features.
Weaviate is designed to scale horizontally so it can handle large datasets and high query loads by distributing data across multiple nodes in a cluster. It supports multi-modal data, works with various data types (text, images, audio, video) depending on the vectorization modules used. Weaviate provides both RESTful and GraphQL APIs for flexibility in how developers interact with the database.
However, for large-scale production environments, there are several considerations to keep in mind:
- Limited enterprise-grade security features
- Potential scalability challenges with multi-billion vector datasets
- Manual management required for newly released tiered storage options
- Horizontal scale-up requires assistance from Weaviate engineers and cannot be done automatically
This last point is particularly noteworthy, as it means organizations need to plan ahead and allocate time for scaling operations, ensuring they don't approach their system limits without proper preparation.
Key Differences
Search Methodology
SingleStore has vector search built into the database so you can search for similar items alongside your SQL queries. It supports exact k-nearest neighbors (kNN) and approximate nearest neighbor (ANN) search with options like FLAT, IVF_FLAT, IVF_PQ, and HNSW algorithms. Exact kNN is precise but resource intensive, ANN is fast but for large datasets that need fast interactive queries.
Weaviate uses Hierarchical Navigable Small World (HNSW) indexing for efficient vector search on large datasets. It also has hybrid search capabilities, so you can mix vector based and traditional keyword search. This flexibility makes Weaviate a great choice for applications that need both semantic understanding and structured filtering.
Data
SingleStore is great for structured and semi-structured data. Vector search is part of the relational database. Vectors are stored in columnstore tables and accessed via SQL so it’s easy to integrate with AI applications.
Weaviate is broader, supports multi-modal data like text, images, audio, video. It integrates with various vectorization modules so it’s versatile for unstructured data. But data handling may require additional setup for structured data use cases.
Scalability
SingleStore distributes data across multiple nodes so it’s seamless for large datasets. Adding nodes is easy and performance is consistent as data grows. It combines vector search with SQL operations so query complexity is reduced.
Weaviate scales horizontally, data is distributed across clusters. It supports large datasets but scaling requires manual intervention and close collaboration with Weaviate engineers. This can be a delay for companies that scale fast.
Flexibility and Customization
SingleStore’s SQL based approach gives flexibility in data modeling and queries. Developers can combine vector search with traditional database operations so complex AI applications with minimal system integration effort.
Weaviate has flexibility through its RESTful and GraphQL APIs, so it caters to developers with different preferences. Its hybrid search capabilities and integration with various embedding models gives customization options for specific use cases like semantic search or content classification.
Integration and Ecosystem
SingleStore integrates with existing data pipelines and analytics workflows. It can store and query vectors alongside traditional data so it’s a unified platform for AI applications.
Weaviate excels in ecosystem support, has pre-built modules for popular embeddings and reranking techniques. But enterprise integrations are limited compared to SingleStore, may require additional development for full deployment.
Ease of Use
SingleStore’s SQL first approach and great documentation makes it easy for developers familiar with relational databases. Setting up and maintaining is easy, especially for teams with existing SQL expertise.
Weaviate has developer friendly setup with clear APIs and documentation. It’s great for smaller teams or projects exploring vector search but may require more effort for scaling and operational stability in large scale environments.
Cost
SingleStore combines vector and relational database so it can reduce cost by eliminating the need for separate systems. Operational cost depends on scale and node configuration.
Weaviate’s open-source model reduces upfront cost but enterprise deployments will incur additional cost for support and scaling. Newly released features like tiered storage require manual management, so additional operational overhead.
Security Features
SingleStore has enterprise grade security, encryption, authentication, access control. These are important for companies that prioritize data security.
Weaviate’s security is limited. It supports basic access controls but advanced features like end-to-end encryption and granular authentication mechanisms are not as mature so it’s a concern for enterprise use cases.
When to Choose Each
SingleStore is for high performance and scalability especially when combining vector search with structured data queries. Robust SQL and enterprise grade security makes it a great choice for large distributed data environments like recommendation systems, financial analysis and AI business intelligence.
Weaviate is for projects that need hybrid or multi modal search capabilities, especially when dealing with unstructured data like text, images or videos. It’s a great choice for developers working on proof of concept AI applications, content classification or semantic search where ease of setup and experimentation is key.
Summary
SingleStore is great for vector search with structured data, robust scalability, enterprise grade security and SQL based operations. Weaviate is great for multi modal, developer friendly setup and hybrid search. Ultimately it’s up to your use case, data types and performance needs. Assess your project requirements to see which one fits best.
Read this to get an overview of SingleStore and Weaviate 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?
- SingleStore: Overview and Core Technology
- Weaviate: Overview and Core Technology
- Key Differences
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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