Zilliz Cloud vs Vald Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Zilliz Cloud 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.
Zilliz Cloud and Vald are purpose-built vector databases. This post compares their vector search capabilities.
Zilliz Cloud: Overview and Core Technology
Zilliz Cloud is a fully managed vector database service built on top of the open-source Milvus engine. It helps developers and organizations to handle large scale AI applications by storing, managing and searching vector embeddings efficiently. It takes care of infrastructure for you, so you can focus on building AI features instead of managing databases.
One of the key advantages of Zilliz Cloud is the automatic performance optimization. The system has AutoIndex technology which will choose the best indexing method for your data and use case. So you don’t have to spend time tuning parameters or comparing different index types. The platform also uses IVF (Inverted File) and graph-based techniques to speed up similarity search across large datasets.
The platform has enterprise features. You can deploy your vector databases across AWS, Azure or Google Cloud, with options to use Zilliz’s fully managed service or bring your own cloud account (BYOC). For organizations that handle sensitive data, Zilliz Cloud has security controls like encryption, access management and compliance tools. The system also supports different consistency levels so you can balance between fast updates and strong data consistency based on your needs.
Cost management is another important aspect of Zilliz Cloud. The platform uses tiered storage to automatically move less accessed data to cheaper storage options, so you can reduce cost without affecting performance. You can also choose compute resources that match your workload - for example, use more powerful instances for heavy processing tasks and lighter ones for simple queries. This flexibility helps you to optimize your spending while maintaining good performance.
For AI applications that need to search different types of data together, Zilliz Cloud supports hybrid search. You can search across text embeddings, image vectors and other data types in a single query. The platform also supports various similarity metrics like Cosine, Euclidean and Inner Product so it’s suitable for different machine learning models and use cases. As your data grows, the system can scale horizontally by adding more resources automatically so you can maintain good performance even under heavy workload.
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
Search Methodology
Zilliz Cloud: Built on the Milvus engine, Zilliz Cloud uses IVF and graph-based algorithms to search across large datasets. AutoIndex selects the best indexing strategy for your data and use case so you don’t have to tune parameters manually. This is useful for diverse applications as you don’t need to fine-tune.
Vald: Vald uses NGT (Nearest Neighbor Graph and Tree) algorithm, known for its fast and accurate nearest neighbor search. It can continue serving search queries while updating indexes, so minimal downtime and consistent performance. This dynamic indexing is a big plus for real-time applications.
Data Handling
Zilliz Cloud: Supports structured, semi-structured and unstructured data, Zilliz Cloud does hybrid search, you can query across multiple data types like text, image and video embeddings. This is perfect for AI applications that require multimodal search.
Vald: Also handles large unstructured datasets but focuses more on vector search rather than integrating multiple data types in a single query. Its data input and output customization makes it versatile but less hybrid than Zilliz Cloud.
Scalability and Performance
Zilliz Cloud: Horizontal scaling by adding resources as data grows. Cloud-native design ensures seamless scaling on AWS, Azure or Google Cloud. Automatic performance optimization and tiered storage for cost efficiency is perfect for growing datasets.
Vald: Vald also scales by distributing data and indexes across multiple machines. Replication and load-balancing mechanisms are in place for high traffic. But manual intervention may be required to fine-tune scaling in some cases.
Flexibility and Customization
Zilliz Cloud: Multiple deployment options, fully managed service or BYOC (Bring Your Own Cloud). Hybrid search and support for multiple similarity metrics (e.g. Cosine, Euclidean, Inner Product) makes it flexible for various ML models.
Vald: Built for high configurability, Vald is integratable with gRPC and has custom pipelines for data processing. While it’s flexible in architecture, it’s more focused on infrastructure level customization rather than end-to-end feature support.
Integration and Ecosystem
Zilliz Cloud: As a Milvus-based platform, it integrates with machine learning frameworks like LangChain, LlamaIndex and DsPy Also supports RESTful APIs for broader application compatibility.
Vald: Cloud-native focused, Vald is Kubernetes-centric and fits into modern DevOps workflows. Compatible with containerized environments and cloud platforms makes it a good fit for distributed systems.
Ease of Use
Zilliz Cloud: Developer friendly interface with good documentation, easy to set up and maintain. Fully managed, so you can focus on building features.
Vald: Powerful but has a steeper learning curve since it’s focused on customization and infrastructure level control. Good for developers who are comfortable with Kubernetes and distributed system management.
Cost
Zilliz Cloud: Tiered storage model that moves infrequently accessed data to lower cost storage, overall cost optimization. Flexible compute options so you can align spending with workload.
Vald: Open source so it can reduce initial cost but may require significant investment in infrastructure setup and maintenance. Trade-off is operational overhead vs long term flexibility.
Security
Zilliz Cloud: Enterprise grade security, encryption, role-based access control, compliance tools. Supports consistency levels for balancing update speed and data reliability.
Vald: Index replication for fault tolerance but no built-in enterprise grade security features. Developers need to implement additional security measures to protect data.
When to Use Zilliz Cloud
Zilliz Cloud is for organizations and developers who need to manage big AI applications with minimal ops. Fully managed service means no infrastructure to manage, so you can focus on building and scaling vector search powered features. Use cases like hybrid search across different data types (text, images, video), applications that need strong security features, scenarios that require cost effective tiered storage solutions fit well with Zilliz Cloud. It’s good for projects where fast setup, scalability and performance tuning is important.
When to Use Vald
Vald is for developers who want a highly customizable, Kubernetes native vector search solution. Dynamic indexing makes it a good fit for real-time applications that need continuous data updates without downtime. Projects with complex infrastructure needs like distributed systems that require granular control over data pipelines can leverage Vald’s flexibility and many integration options. If you want to build a custom vector search system that integrates deeply into your DevOps workflow, Vald has the tools and flexibility.
Summary
Zilliz Cloud is good for ease of use, hybrid search and enterprise grade security for big AI applications. Vald is good for real-time indexing and Kubernetes native customization, especially for developers who are comfortable with managing distributed systems. Ultimately it’s up to your use case, data diversity, ops needs and level of control. Evaluate these and you’ll know which one is for you.
Read this to get an overview of Zilliz Cloud 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 WilliamsChloe Williams is a technical writer at Zilliz.
- What is a Vector Database?
- Zilliz Cloud: Overview and Core Technology
- Vald: 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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