Blog
Couchbase vs Vespa Choosing the Right Vector Database for Your AI Apps

Couchbase vs Vespa Choosing the Right Vector Database for Your AI Apps

Nov 30, 20248 min read

What is a Vector Database?

Before we compare Couchbase and Vespa, 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.

Couchbase is a distributed multi-model NoSQL document-oriented database with vector search capabilities added on. Vespa is a purpose-built vector database. This post compares their vector search capabilities.

Couchbase: Overview and Core Technology

Couchbase is a distributed, open-source, NoSQL database that can be used to build applications for cloud, mobile, AI, and edge computing. It combines the strengths of relational databases with the versatility of JSON. Couchbase also provides the flexibility to implement vector search despite not having native support for vector indexes. Developers can store vector embeddings—numerical representations generated by machine learning models—within Couchbase documents as part of their JSON structure. These vectors can be used in similarity search use cases, such as recommendation systems or retrieval-augmented generation both based on semantic search, where finding data points close to each other in a high-dimensional space is important.

One approach to enabling vector search in Couchbase is by leveraging Full Text Search (FTS). While FTS is typically designed for text-based search, it can be adapted to handle vector searches by converting vector data into searchable fields. For instance, vectors can be tokenized into text-like data, allowing FTS to index and search based on those tokens. This can facilitate approximate vector search, providing a way to query documents with vectors that are close in similarity.

Alternatively, developers can store the raw vector embeddings in Couchbase and perform the vector similarity calculations at the application level. This involves retrieving documents and computing metrics such as cosine similarity or Euclidean distance between vectors to identify the closest matches. This method allows Couchbase to serve as a storage solution for vectors while the application handles the mathematical comparison logic.

For more advanced use cases, some developers integrate Couchbase with specialized libraries or algorithms (like FAISS or HNSW) that enable efficient vector search. These integrations allow Couchbase to manage the document store while the external libraries perform the actual vector comparisons. In this way, Couchbase can still be part of a solution that supports vector search.

By using these approaches, Couchbase can be adapted to handle vector search functionality, making it a flexible option for various AI and machine learning tasks that rely on similarity searches.

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.

Key Differences

When you need to implement vector search, both Couchbase and Vespa offer different approaches. Understanding their differences will help you make the right choice for your project.

Native Support vs. Adapted Solutions

Vespa provides built-in vector search capabilities. You can add vector fields directly to your documents, and Vespa handles the searching efficiently. It supports various vector types, including tensors, making it useful for complex document embeddings.

Couchbase takes a different approach. While it doesn't have native vector search support, you can implement vector search in several ways:

Using Full Text Search (FTS) by converting vectors into searchable fields
Storing raw vector embeddings and handling similarity calculations in your application
Integrating with external vector search libraries like FAISS or HNSW

Performance and Scalability

Vespa shines in performance optimization. It uses a specialized C++ engine for memory management and search operations, helping it maintain speed even with complex queries and large datasets. You can add more machines to your Vespa setup, and it automatically distributes the workload.

Couchbase's approach to vector search might require more manual optimization. Since vector search isn't built-in, you'll need to carefully consider how you implement it to maintain good performance. The choice between using FTS or application-level calculations will affect your scaling strategy.

Data Handling

Both systems handle JSON data well, but in different ways:

Vespa can process multiple search types simultaneously - vector search, text search, and structured data queries. This means you can combine different search types in a single query.

Couchbase brings together NoSQL flexibility with relational database features. While it handles JSON effectively, implementing vector search requires additional setup and potentially external tools.

Ease of Implementation

Setting up vector search in Vespa is straightforward since it's a core feature. You define vector fields in your schema, and Vespa handles the rest.

With Couchbase, you'll need to choose and implement your vector search strategy. This gives you flexibility but requires more development work. You'll need to decide between:

When to Choose Couchbase

Choose Couchbase when you need a NoSQL database that can be used for vector search, especially if you’re already using Couchbase elsewhere in your app. It’s good for projects where you want control over the vector search implementation whether through Full Text Search adaptation, application level calculations or integration with specialized libraries like FAISS. This works best when you have the development resources to implement and optimise your chosen vector search strategy.

When to Choose Vespa

Vespa is the better choice when you need built-in vector search without any implementation work. It’s good for scenarios where you need multiple search types (vector, text and structured data) and where automatic scaling is critical. Vespa’s C++ engine and automatic workload distribution makes it perfect for large scale applications that need to handle complex queries and high traffic without manual optimisation.

Conclusion

Couchbase gives you flexibility in vector search implementation through different approaches so it’s good for teams that want control over their vector search strategy. Vespa gives you built-in vector search with automatic scaling and optimisation so it’s good for immediate vector search deployment. Your choice should match your team’s technical expertise, existing infrastructure and specific requirements for vector search implementation. Consider development resources, scaling needs and whether you need immediate vector search or a custom approach.

Read this to get an overview of Couchbase and Vespa 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 Williams
Chloe Williams is a technical writer at Zilliz.

Content

Start Free, Scale Easily

Try the fully-managed vector database built for your GenAI applications.

Try Zilliz Cloud for Free

Share this article

The 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.

Get the Free Guide