Weaviate vs Neo4j: Choosing the Right Vector Database for Your Needs
As AI and data-driven technologies advance, selecting an appropriate vector database for your application is becoming increasingly important. Weaviate and Neo4j are two options in this space. This article compares these technologies to help you make an informed decision for your project.
What is a Vector Database?
Before we compare Weaviate 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), and Weaviate
- 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.
Weaviate is a purpose-built vector database and Neo4j is a graph database with vector search as an add-on. This post compares their vector search capabilities.
Weaviate: Overview and Core Technology
Weaviate is an open-source vector database designed to simplify AI application development. It offers built-in vector and hybrid search capabilities, easy integration with machine learning models, and a focus on data privacy. These features aim to help developers of various skill levels create, iterate, and scale AI applications more efficiently.
One of Weaviate's strengths is its fast and accurate similarity search. It uses HNSW (Hierarchical Navigable Small World) indexing to enable vector search on large datasets. Weaviate also supports combining vector searches with traditional filters, allowing for powerful hybrid queries that leverage both semantic similarity and specific data attributes.
Key features of Weaviate include:
- PQ compression for efficient storage and retrieval
- Hybrid search with an alpha parameter for tuning between BM25 and vector search
- Built-in plugins for embeddings and reranking, which ease development
Weaviate is an entry point for developers to try out vector search. It offers a developer-friendly approach with a simple setup and well-documented APIs. Deep integration with the GenAI ecosystem makes it suitable for small projects or proof-of-concept work. The target audience for Weaviate are software engineers building AI applications, data engineers working with large datasets and data scientists deploying machine learning models. Weaviate simplifies semantic search, recommendation systems, content classification and other AI features.
Weaviate is designed to scale horizontally so it can handle large datasets and high query loads by distributing data across multiple nodes in a cluster. It supports multi-modal data, works with various data types (text, images, audio, video) depending on the vectorization modules used. Weaviate provides both RESTful and GraphQL APIs for flexibility in how developers interact with the database.
However, for large-scale production environments, there are several considerations to keep in mind:
- Limited enterprise-grade security features
- Potential scalability challenges with multi-billion vector datasets
- Manual management required for newly released tiered storage options
- Horizontal scale-up requires assistance from Weaviate engineers and cannot be done automatically
This last point is particularly noteworthy, as it means organizations need to plan ahead and allocate time for scaling operations, ensuring they don't approach their system limits without proper preparation.
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
When choosing between Weaviate and Neo4j for vector search, you should compare them across the key areas to see their strengths and trade-offs.
Search Methodology
Weaviate is purpose built for vector search, using HNSW (Hierarchical Navigable Small World) indexing for fast and accurate approximate nearest neighbor (ANN) search. It also allows for hybrid queries by combining vector similarity with keyword search, so you can mix semantic and structured filtering in one query. Neo4j uses HNSW as well, but it integrates vector search into its graph database. So Neo4j can combine semantic similarity search with graph queries, which is useful for applications where understanding the relationships between data points is important, like recommendation systems or fraud detection.
Data
Weaviate is great at handling unstructured and multi-modal data, text, images, audio, video. It works seamlessly with external embedding models, so it’s versatile with different data formats. Neo4j treats vectors as properties of nodes or relationships, so it’s more suitable for structured or semi-structured datasets where relationships are important. It can combine graph queries with vector search to get more insights in relationship heavy datasets.
Scalability and Performance
Weaviate supports horizontal scaling by distributing data across nodes in a cluster, which helps with large datasets and high query loads. However scaling for extremely large datasets might require manual management and help from Weaviate engineers. Neo4j is optimized for graph workloads, and while its vector search is fast, it might not handle massive vector only datasets as well as Weaviate. For mixed workloads that involve both graph traversals and vector search, Neo4j is a balanced approach.
Flexibility and Customization
Weaviate is developer friendly with its RESTful and GraphQL APIs and plugins for embedding generation and reranking. It simplifies workflows and is perfect for AI-first projects. Neo4j has advanced tuning options for vector search behavior, like connection density and neighbor tracking. It’s flexible when you need fine grained control over both graph and vector data queries.
Integration and Ecosystem
Weaviate integrates well with AI and machine learning frameworks so it’s a natural fit for applications that focus on semantic search and recommendation systems. Neo4j being a mature graph database has a broader ecosystem with connectors to analytics tools, data pipelines and visualization platforms. So Neo4j is more suitable for enterprise environments where data integration across systems is important.
Ease of Use
Weaviate is designed to be simple, with an easy setup and well documented APIs so even developers new to vector databases can use it. Neo4j requires knowledge of its graph model and Cypher query language. While that’s a steeper learning curve, it pays off for use cases that benefit from combining graph and vector capabilities.
Cost
Weaviate is open source and has managed services optional, so it’s a cost effective solution for vector only use cases. Neo4j’s pricing reflects its enterprise features and graph database, so it might be more expensive but often justified for organizations that need robust graph and AI capabilities.
Security
Neo4j has enterprise level security, role based access control, advanced authentication and encryption so it’s suitable for compliance heavy or sensitive applications. Weaviate is secure but lacks some of the advanced security features of enterprise systems, so it might not be suitable for organizations with very high security requirements.
When to use Weaviate
Weaviate is great for projects where vector search is key, especially for large scale distributed data. It supports multi-modal data types like text, images, audio and video and is perfect for AI driven applications like recommendation systems, semantic search and content classification. Weaviate can combine vector similarity search with structured filtering and can handle many query patterns. For companies focused on unstructured data and need fast approximate nearest neighbor search at scale Weaviate is a developer friendly and performance oriented solution.
When to use Neo4j
Neo4j is great for use cases where the relationships between the data points are as important as the data itself. Applications like fraud detection, social network analysis or recommendation engines that rely on graph traversal combined with semantic similarity can leverage Neo4j’s unique combination of graph and vector search. It’s great in enterprise environments that require a robust ecosystem, advanced security features and seamless integration with analytics or visualization tools. For projects where understanding and navigating the relationships in the data is key Neo4j is a flexible and powerful solution.
Conclusion
Weaviate and Neo4j are different tools for different needs. Weaviate is great for vector centric workloads, multi-modal data and ease of use and is perfect for AI driven applications. Neo4j is great for scenarios where relationships are key, it’s a mature graph database with vector search. The choice between them should be based on your project’s requirements, data types, query complexity and how important relationships vs semantic similarity is. Choose the right tool and your application architecture will match your goals and scale.
The choice between Weaviate and Neo4j depends on your specific use case, the nature of your data, and your future scalability needs. Both technologies continue to evolve, so it's worth keeping an eye on their development as you make your decision. Remember that in some cases, a hybrid approach using both technologies might be the optimal solution, leveraging the strengths of each for different aspects of your application. As with any technology decision, it's advisable to conduct thorough testing with your specific datasets and use cases before making a final choice.
Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
VectorDBBench is an open-source benchmarking tool designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems such as Milvus and Zilliz Cloud (the managed Milvus) using their own datasets, and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.
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
- What is a Vector Database?
- Weaviate: Overview and Core Technology
- Neo4J: The Basics
- Key Differences
- When to use Weaviate
- When to use Neo4j
- Conclusion
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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