SingleStore vs Neo4j Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare SingleStore 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.
SingleStore is a distributed, relational, SQL database management system and Neo4j is a graph database. Both have vector search as an add-on. This post compares their vector search capabilities.
SingleStore: Overview and Core Technology
SingleStore has made vector search possible by putting it in the database itself, so you don’t need separate vector databases in your tech stack. Vectors can be stored in regular database tables and searched with standard SQL queries. For example, you can search similar product images while filtering by price range or explore document embeddings while limiting results to specific departments. The system supports both semantic search using FLAT, IVF_FLAT, IVF_PQ, IVF_PQFS, HNSW_FLAT, and HNSW_PQ for vector index and dot product and Euclidean distance for similarity matching. This is super useful for applications like recommendation systems, image recognition and AI chatbots where similarity matching is fast.
At its core SingleStore is built for performance and scale. The database distributes the data across multiple nodes so you can handle large scale vector data operations. As your data grows you can just add more nodes and you’re good to go. The query processor can combine vector search with SQL operations so you don’t need to make multiple separate queries. Unlike vector only databases SingleStore gives you these capabilities as part of a full database so you can build AI features without managing multiple systems or dealing with complex data transfers.
For vector indexing SingleStore has two options. The first is exact k-nearest neighbors (kNN) search which finds the exact set of k nearest neighbors for a query vector. But for very large datasets or high concurrency SingleStore also supports Approximate Nearest Neighbor (ANN) search using vector indexing. ANN search can find k near neighbors much faster than exact kNN search sometimes by orders of magnitude. There’s a trade off between speed and accuracy - ANN is faster but may not return the exact set of k nearest neighbors. For applications with billions of vectors that need interactive response times and don’t need absolute precision ANN search is the way to go.
The technical implementation of vector indices in SingleStore has specific requirements. These indices can only be created on columnstore tables and must be created on a single column that stores the vector data. The system currently supports Vector Type(dimensions[, F32]) format, F32 is the only supported element type. This structured approach makes SingleStore great for applications like semantic search using vectors from large language models, retrieval-augmented generation (RAG) for focused text generation and image matching based on vector embeddings. By combining these with traditional database features SingleStore allows developers to build complex AI applications using SQL syntax while maintaining performance and scale.
Neo4j: Overview and Core Technology
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
SingleStore: Exact k-nearest neighbor (kNN) for high precision and Approximate Nearest Neighbor (ANN) for speed in large datasets. ANN methods use vector indexing algorithms like HNSW and IVF variants. SingleStore puts these into its SQL-based database so you can search vectors along with your structured data.
Neo4j: Uses HNSW graphs for fast ANN searches. This method navigates vector spaces using graph-based connections. Neo4j’s vector indexes are tightly coupled with its graph model so you can search semantically within graph-connected data.
Key Difference: SingleStore is better for hybrid queries (vector + relational SQL), Neo4j is better for semantic searches (vector + entities)) where relationships matter.
Data
SingleStore: Structured, semi-structured, unstructured. Vectors are stored in columnstore tables, so you can do high-performance analytical queries along with vector ops.
Neo4j: Primarily for graph data. Vectors are stored as properties of nodes or relationships so it’s great for applications that need both semantic similarity and graph-based context.
Key Difference: SingleStore is more flexible for mixed data types, Neo4j is graph-first.
Scalability and Performance
SingleStore: Designed for distributed scalability. As data grows, adding nodes means consistent performance. Its vector search is integrated with a distributed query engine, so you can do concurrent large-scale vector ops.
Neo4j: Scales well for graph workloads but can struggle with extremely large datasets due to graph traversal overhead. Its vector search requires optimizing HNSW parameters to balance performance and accuracy.
Key Difference: SingleStore is linearly scalable for huge datasets, Neo4j is better for graph-heavy applications.
Flexibility and Customization
SingleStore: Can combine vector search with SQL queries. Supports multiple vector indexing algorithms and allows users to tune indexing and query params.
Neo4j: Customization options for vector indexes (quantization, fine-tune graph params e.g. max connections). Relationship vector indexes add another layer of flexibility for complex graph use cases.
Key Difference: Both are customizable but SingleStore is SQL-based, Neo4j is graph-driven.
Integration and Ecosystem
SingleStore: Unified platform so you don’t need additional systems. Integrates well with modern data pipelines and AI/ML tools via SQL and supports popular embedding models.
Neo4j: Integrates well with graph-specific tools like Cypher query language and supports embedding models. Fits into ecosystems that are graph-heavy.
Key Difference: SingleStore simplifies integration by being a one-stop database, Neo4j complements graph-centric ecosystems.
Usability
SingleStore: SQL-native interface, so developers familiar with relational databases can use it. Setup and maintenance is easy for database pros.
Neo4j: Requires knowledge of graph database concepts and the Cypher query language, which may introduce a steeper learning curve for newbies.
Key Difference: SingleStore is easier for SQL folks, Neo4j requires graph knowledge.
Cost
SingleStore: One system for multiple functionality (relational and vector search) so you may not need to manage separate databases. Managed services can simplify cost management.
Neo4j: Pricing depends on workload size and features like managed services. For graph-heavy workloads, its specialized features may justify the cost.
Key Difference: SingleStore is consolidated, Neo4j may cost more for niche graph use cases.
Security
SingleStore: Encryption, authentication, role-based access control, GDPR compliance.
Neo4j: Encrypted communications, role-based permissions, auditing.
Key Difference: Same, depends on your organization.
When to Choose SingleStore
Choose SingleStore when you have large distributed data and need vector search tightly integrated into a relational database. It’s great for hybrid queries that combine vector similarity with structured data, like e-commerce apps that filter similar product recommendations by price or category. Plus SingleStore can scale horizontally and do both exact and approximate nearest neighbor searches so it’s perfect for high concurrency workloads like recommendation engines, AI chatbots and semantic search over massive datasets.
When to Choose Neo4j
Neo4j is a better fit when your use case involves graph based data with semantic and contextual relationships. Its vector search is great for applications that combine graph traversals with similarity queries, like social network analysis, fraud detection or recommendation systems that use both graph structure and embedding based similarity. If your application requires deeply connected data and insights from entity relationships—like finding movies in the same genre or era—Neo4j’s native graph database is the way to go.
Summary
SingleStore and Neo4j are both great tools, each for different use cases. SingleStore integrates vector search with relational data and scales for big data, Neo4j pairs semantic vector search with graph analytics for relationship based insights. Choose the right tool for your data, your queries and your performance requirements. Align your choice with your use case—hybrid queries across structured data or contextual graph based recommendations—and you’ll get the best results.
Read this to get an overview of SingleStore 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?
- SingleStore: Overview and Core Technology
- Neo4j: 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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