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

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

Dec 09, 20248 min read

What is a Vector Database?

Before we compare Vespa and Vald, 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 Vald 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.

Vald: Overview and Core Technology

Vald is a powerful tool for searching through huge amounts of vector data really fast. It's built to handle billions of vectors and can easily grow as your needs get bigger. The cool thing about Vald is that it uses a super quick algorithm called NGT to find similar vectors.

One of Vald's best features is how it handles indexing. Usually, when you're building an index, everything has to stop. But Vald is smart - it spreads the index across different machines, so searches can keep happening even while the index is being updated. Plus, Vald automatically backs up your index data, so you don't have to worry about losing everything if something goes wrong.

Vald is great at fitting into different setups. You can customize how data goes in and out, making it work well with gRPC. It's also built to run smoothly in the cloud, so you can easily add more computing power or memory when you need it. Vald spreads your data across multiple machines, which helps it handle huge amounts of information.

Another neat trick Vald has is index replication. It stores copies of each index on different machines. This means if one machine has a problem, your searches can still work fine. Vald automatically balances these copies, so you don't have to worry about it. All of this makes Vald a solid choice for developers who need to search through tons of vector data quickly and reliably.

Key Differences

Both Vespa and Vald have great features for vector search but they serve slightly different needs based on architecture, performance and flexibility. Below we’ll go through the main differences to help you decide.

Search Method

Vespa supports hybrid search, combining vector search with text and structured data search. This makes it very versatile for applications that need multi-modal search like e-commerce or document repositories. Vespa handles vector data using tensors and is optimized for large scale concurrent queries.

Vald is vector only search, using the NGT (Neighborhood Graph and Tree) algorithm for high speed approximate nearest neighbor (ANN) search. This makes Vald perfect for workloads where vector similarity is the main concern.

Data

Vespa can handle different data formats—structured, semi-structured, unstructured. It has advanced query features like filtering and ranking across these data types so developers have flexibility to design complex applications.

Vald is built for vector data. While it integrates with structured data systems, its core is designed to manage and search dense vector datasets.

Scalability and Performance

Vespa is scalable through its distributed architecture. Its C++ engine is built to manage memory and processing so it can handle complex queries across large datasets without performance drop.
Vald scales by distributing vector indexes across machines. Features like dynamic indexing and index replication ensure it performs well under high traffic or frequent updates.

Flexibility and Customization

Vespa has extensive query, ranking and data modeling customization. Developers can tune the system to handle complex search scenarios so it’s suitable for applications that need fine grained search relevance adjustments.

Vald has API level flexibility, especially with gRPC integration. It’s easy to customize data ingestion and retrieval workflows so it’s developer friendly for specific vector search use cases.

Integration and Ecosystem

Vespa has a rich ecosystem and supports integration with many programming languages and frameworks. Its multi-modal data handling makes it compatible with many applications.

Vald is designed to fit into modern cloud native environments. It runs well on Kubernetes so it’s a good fit for developers using containerized deployments.

Usability

Vespa has a steeper learning curve because of its many features and complex configuration options. But it has good documentation and a community for developers willing to put in the time.

Vald is easier to set up and maintain, especially for vector specific use cases. Its focus on usability makes it good for developers who want quick deployment.

Cost

Vespa requires more resources upfront because of its hybrid search and many features. But its ability to combine multiple search modes might justify the cost for complex applications.

Vald is more cost effective for vector only workloads. Its indexing and replication mechanisms helps to reduce operational cost in cloud.

Security

Vespa has a full security model, including encryption, authentication and fine grained access control, suitable for enterprise applications that requires strong security.

Vald has basic security features like TLS encryption and token based authentication. Not as extensive as Vespa’s but good enough for most use cases.

When to use Vespa

Vespa is for when you need a search engine that can handle vector, text and structured data queries all in one. It’s great for large scale distributed systems where you have different types of data to index and query, like e-commerce platforms, document repositories or recommendation engines. Vespa’s ability to combine complex search modes with advanced ranking and filtering makes it perfect for situations where you need precision, scalability and flexibility in your search operations.

When to use Vald

Vald is for when vector similarity search is the main requirement, like building image recognition systems, AI-driven recommendation engines or semantic search applications. Its focus on high-speed vector search and cloud-native architecture makes it great for developers deploying in Kubernetes or handling large datasets of embeddings. Vald’s simplicity, dynamic indexing and auto-scaling means you get performance without the overhead of managing extra search modes or complex config.

Summary

Both Vespa and Vald are vector search tools, each good at different things. Vespa is great at hybrid search workloads and different types of data, so good for complex large scale applications. Vald is good at high-speed vector search and cloud-native, so good for developers who want simplicity and efficiency in embedding heavy use cases. Ultimately it’s up to your use case, data diversity and performance requirements – choose the one that fits your needs.

Read this to get an overview of Vespa and Vald 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