Zilliz Cloud vs Neo4j Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Zilliz Cloud and Neo4j, 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 is a purpose-built vector database. Neo4j is a graph database with vector search capabilities as an add-on. 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.
Neo4J: The Basics
Neo4j’s vector search allows developers to create vector indexes to search for similar data across their graph. These indexes work with node properties that contain vector embeddings - numerical representations of data like text, images or audio that capture the meaning of the data. The system supports vectors up to 4096 dimensions and cosine and Euclidean similarity functions.
The implementation uses Hierarchical Navigable Small World (HNSW) graphs to do fast approximate k-nearest neighbor searches. When querying a vector index, you specify how many neighbors you want to retrieve and the system returns matching nodes ordered by similarity score. These scores are 0-1 with higher being more similar. The HNSW approach works well by keeping connections between similar vectors and allowing the system to quickly jump to different parts of the vector space.
Creating and using vector indexes is done through the query language. You can create indexes with the CREATE VECTOR INDEX command and specify parameters like vector dimensions and similarity function. The system will validate that only vectors of the configured dimensions are indexed. Querying these indexes is done with the db.index.vector.queryNodes procedure which takes an index name, number of results and query vector as input.
Neo4j’s vector indexing has performance optimizations like quantization which reduces memory usage by compressing the vector representations. You can tune the index behavior with parameters like max connections per node (M) and number of nearest neighbors tracked during insertion (ef_construction). While these parameters allow you to balance between accuracy and performance, the defaults work well for most use cases. The system also supports relationship vector indexes from version 5.18, so you can search for similar data on relationship properties.
This allows developers to build AI powered applications. By combining graph queries with vector similarity search applications can find related data based on semantic meaning not exact matches. For example a movie recommendation system could use plot embedding vectors to find similar movies, while using the graph structure to ensure the recommendations come from the same genre or era as the user prefers.
Key Differences
Search Methodology
Zilliz Cloud is built on Milvus engine and uses IVF (Inverted File) and graph-based indexing to speed up similarity search. AutoIndex automatically selects the best indexing strategy for your data so you don’t need to tune manually.
Neo4j uses Hierarchical Navigable Small World (HNSW) graph for vector search. This allows fast approximate k-nearest neighbor search by maintaining connections between similar vectors. Neo4j allows you to fine tune search parameters like max connections and nearest neighbors for more control on search accuracy and speed.
Data Handling
Zilliz Cloud manages vector embeddings and supports hybrid search. You can query across text, images and other data types and filter against metadata to get the most accurate and relevant results in one shot. This is very important for AI applications that require multimodal data handling.
Neo4j integrates vector search with its graph structure, you can combine vector similarity with graph queries. It supports vectors up to 4,096 dimensions and can be applied to both node and relationship properties, perfect for use cases like recommendation systems that rely on semantic and structural data.
Scalability and Performance
Zilliz Cloud’s architecture allows horizontal scaling, automatically distributes workload as data grows. Tiered storage moves infrequently accessed data to cheaper storage tiers.
Neo4j scales within its graph structure and vector indexes can be optimized for memory efficiency through quantization. But its scalability for large dataset may be limited compared to the distributed nature of Zilliz Cloud.
Flexibility and Customization
Zilliz Cloud provides flexibility in query design and supports multiple similarity metrics like Cosine, Euclidean and Inner Product for different machine learning models. AutoIndex reduces the need for manual tuning and delivers high performance.
Neo4j provides fine grain control over vector index behavior through parameter tuning. Its integration with graph queries allows for highly customized applications especially for datasets where relationships between entities matter.
Integration and Ecosystem
Zilliz Cloud integrates with popular AI and machine learning frameworks. Its BYOC (Bring Your Own Cloud) option supports deployment on AWS, Azure, Google Cloud so you can align with your existing infrastructure.
Neo4j’s ecosystem is graph database centric and connects well with data visualization, ETL pipelines and more. It’s great for developers who already work within a graph based paradigm.
Ease of Use
Zilliz Cloud makes setup and maintenance easy by being a fully managed service. Developers can focus on building applications without worrying about infrastructure. AutoIndex reduces the complexity of configuring the database.
Neo4j’s vector search is integrated into its query language, you need to be familiar with its syntax. While it has robust documentation, the learning curve can be steeper for developers new to graph databases.
Cost
Zilliz Cloud’s tiered storage and customizable compute resources allows cost optimization based on workload. Fully managed service eliminates infrastructure cost but this convenience may come at a higher price for small projects.
Neo4j’s cost depends on licensing and deployment complexity. While it provides flexibility in on-premise or cloud based setup, operational cost can increase for high scale, performance intensive application.
Security
Both platforms have enterprise grade security including encryption, access control and compliance features. Zilliz Cloud’s security is tailored for sensitive data handling, Neo4j supports authentication and role based access, so you can secure access to graph and vector data.
When to Choose Each
Zilliz Cloud is for applications that need to handle large scale distributed data with vector search. Automated indexing, hybrid search and seamless scalability makes it perfect for AI workloads especially those with multimodal data like text, images and audio. Fully managed service minimizes infrastructure overhead and enables rapid deployment and cost efficient operations for growing data.
Neo4j is for scenarios where graph relationships are a key part of the application. Vector similarity search with graph based queries makes it suitable for use cases like recommendation systems, fraud detection and knowledge graphs. If your data heavily relies on relationships and semantic context, Neo4j’s integrated graph and vector search gives you a unique advantage but requires more setup and optimization effort.
Summary
Zilliz Cloud is for AI centric applications that need scalable multimodal data handling with minimal management overhead. Easy to use and highly automated it’s perfect for developers who want rapid deployment.
Neo4j is a good choice for applications where graph relationships are key and vector search within the graph framework is a unique advantage. But it may require more effort to configure and optimize for large scale vector workloads.
Ultimately it depends on your project’s requirements. If your priority is a fully managed scalable solution for AI workloads, Zilliz Cloud might be the better choice. If your application heavily relies on graph based queries with vector similarity, Neo4j might be the way to go.
Read this to get an overview of Zilliz Cloud and Neo4j 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
- Neo4J: The Basics
- 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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