Vespa vs MyScale Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Vespa and MyScale, 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 is a purpose-built vector database. MyScale is a database built on ClickHouse that combines vector search and SQL analytics with vector search capabilities as an add-on. 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 MyScale? Overview and Core Technology
MyScale is a cloud based database built on top of the open source ClickHouse database, designed for AI and machine learning workloads. It can handle structured and vector data and real time analytics and machine learning. MyScale is focused on time series, vector search and full text search so it’s good for real time processing and AI driven insights. By using ClickHouse architecture, MyScale is high performance and scalable for AI.
One of the key features of MyScale is native SQL support which simplifies AI driven queries by integrating vector search, full text search and traditional SQL queries in one system. This reduces the need for multiple tools and makes it scalable for AI. MyScale supports and manages analytical processing of both structured and vectorized data on one platform using OLAP database architecture to operate on vectorized data. Developers can interact with MyScale using SQL so it’s accessible to all programmers familiar with relational databases.
MyScale has multiple vector index types and similarity metrics to support different use cases. It supports common distance metrics like Euclidean distance (L2), inner product (IP) and cosine similarity. The database has multiple indexing algorithms: MSTG (Multi-Scale Tree Graph), ScaNN, IVFFLAT, IVFPQ, IVFSQ and HNSW, each with its own set of parameters to tune. MyScale’s proprietary MSTG vector engine uses NVMe SSDs to increase data density so it outperforms specialized vector databases in both performance and cost.
By combining the functionality of an SQL database, vector database and full text search engine into one system MyScale reduces infrastructure and maintenance costs. This unification allows for joint data queries and analytics and a single data foundation for AI applications. MyScale also has MyScale Telemetry for full observability of LLM systems so you can monitor and debug efficiently. As data gets more complex MyScale is a future proof solution that can handle newer data modalities and database sizes while keeping computing performance and integration between different data types.
Key Differences
Choosing the right vector search solution is key to your project’s success. This comparison looks at Vespa and MyScale, two of the big players in the vector search space, to help you make an informed decision based on your needs.
Search
Vespa has a unified search approach, combining vector search, text search and structured data search in one place. Vector search has support for multiple vector fields per document and specialized tensor handling for complex document embeddings. The search engine uses a custom C++ engine for memory management and query processing.
MyScale takes a different approach by building on top of ClickHouse. It integrates vector search directly with SQL, with multiple index types: MSTG, ScaNN, IVFFLAT, IVFPQ, IVFSQ, HNSW. It supports common distance metrics: Euclidean distance (L2), inner product (IP), cosine similarity. MyScale’s MSTG vector engine uses NVMe SSDs to increase data density.
Data
Vespa can handle various data types, from simple numbers to complex structures. It’s great at handling multiple search types at the same time, so it’s good for applications that need to combine different search approaches.
MyScale is focused on structured and vector data, with a strong emphasis on time series and real-time analytics. It uses OLAP database architecture for vectorized data processing, so developers can work with both structured and vector data using familiar SQL queries.
Performance and Scalability
Vespa can auto-scale across multiple machines. The system distributes workloads automatically and adapts to changes in data volume and traffic patterns. Auto-scaling helps optimize resource usage and costs.
MyScale uses ClickHouse’s high-performance architecture for scalability. Their proprietary MSTG vector engine claims better performance and cost efficiency compared to specialized vector databases, especially when using NVMe SSDs.
Integration and Developer Experience
Vespa is a complete search solution that can handle multiple search types without additional tools. But no information on integration with other systems.
MyScale stands out with its SQL-first approach. Developers familiar with SQL can start working with vector search without learning new query languages. MyScale Telemetry for monitoring and debugging LLM systems makes it easier to maintain and optimize applications.
Infrastructure
Vespa auto-scaling and workload distribution can help optimize resource usage and reduce ops costs. The system adapts to actual usage patterns.
MyScale combines SQL database, vector database and full-text search in one system, which can reduce infrastructure and maintenance costs. Their MSTG vector engine uses NVMe SSDs efficiently which can also help with cost performance.
When to Choose Each Technology
Vespa shines in applications that need multiple search types working together seamlessly, such as e-commerce platforms combining product similarity search, text search, and structured filters. Its automatic scaling and custom C++ engine make it ideal for large-scale applications where you need to handle complex queries across different data types while maintaining high performance.
MyScale works best for teams already using SQL databases who want to add vector search capabilities without learning new query languages. It's particularly strong for applications involving time series data, real-time analytics, and AI-driven insights, especially when you need detailed monitoring of your LLM systems and want to keep your tech stack simple.
Conclusion
Both Vespa and MyScale offer compelling features for vector search, but they take different paths to get there. Vespa's strength lies in its unified search approach, automatic scaling, and ability to handle complex document structures with multiple vector fields. MyScale stands out with its SQL-first approach, specialized vector indexing options, and built-in monitoring tools for LLM systems. Your choice should align with your team's expertise, existing infrastructure, and specific requirements for data handling, performance, and scalability. Consider running benchmarks with your actual data and usage patterns before making a final decision.
Read this to get an overview of Vespa and MyScale 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 MyScale? Overview and Core Technology
- Key Differences
- Search
- Data
- Performance and Scalability
- Integration and Developer Experience
- Infrastructure
- When to Choose Each Technology
- Conclusion
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
Content
Start Free, Scale Easily
Try the fully-managed vector database built for your GenAI applications.
Try Zilliz Cloud for FreeThe Definitive Guide to Choosing a Vector Database
Overwhelmed by all the options? Learn key features to look for & how to evaluate with your own data. Choose with confidence.