Vector Database vs. Graph Database: Differences & Similarities

What is a vector database?

A vector database is a type of database designed to store, index, and search high-dimensional vector representations of data, typically generated by machine learning models. These vectors, also known as embeddings, capture the semantic meaning of unstructured data, such as text, images, audio, and video. Instead of using exact matching, vector databases perform similarity search using techniques like k-nearest neighbors (k-NN) to find the most relevant results based on proximity in a vector space.

Vector database benefits and limitations

Vector databases are well-suited for modern AI applications, but like any technology, they come with trade-offs. Understanding their strengths and limitations can help you decide when and how to use them effectively.

Benefits

• • Approximate nearest neighbor (ANN) search: Enables low-latency retrieval of similar vectors using efficient indexing techniques like hierarchical navigable small world (HNSW), inverted file (IVF), or product quantization (PQ).
  • Support for high-dimensional data: Optimized for storing and querying vector embeddings with hundreds or thousands of dimensions.
  • Semantic similarity search: Facilitates context-aware retrieval using embeddings from natural language processing (NLP), computer vision, or audio models.
  • Horizontal scalability: Many vector databases support sharding and distributed indexing to handle large-scale datasets and high query throughput.
  • Integration with AI/ML pipelines: Designed to work alongside embedding generation tools (e.g., OpenAI, Hugging Face, TensorFlow) and real-time inference workflows.

Limitations

• • Embedding dependency: Requires high-quality, model-generated embeddings as input; performance is tied to the quality and relevance of those vectors.
  • Complex deployment: Setting up vector indexing, managing latency-accuracy trade-offs, and integrating model inference pipelines can increase system complexity.
  • Limited relational querying: Not suited for complex joins, transactional integrity, or entity relationships typically handled by relational or graph databases.
  • Index tuning required: ANN methods may require fine-tuning of parameters (e.g., recall, efSearch, nProbe) to balance accuracy and latency.
  • Evolving standards: Lacks the mature ecosystem, query languages, and interoperability found in traditional database management systems (DBMS).

Vector database use cases

Vector databases are used in applications that require understanding the meaning or similarity of data rather than exact matches. These use cases typically involve unstructured data, such as text, images, audio, or video, and rely on vector embeddings generated by machine learning models. Let’s review this in greater detail below:

• • Semantic search: Enables context-aware search by matching user queries to conceptually similar documents, even when keywords don’t overlap.
  • Recommendation systems: Suggests products, content, or users by comparing vector embeddings to find items with similar behavioral or contextual patterns.
  • Image and video search: Finds visually similar images or video frames by comparing feature vectors extracted from media files using computer vision models.
  • NLP applications: Powers chatbots, question-answering systems, and content classification by storing and retrieving language model embeddings.
  • Anomaly detection: Identifies outliers in high-dimensional data by measuring how far a given vector deviates from normal patterns.
  • Personalization engines: Delivers tailored experiences by analyzing user behavior vectors and mapping them to similar profiles or preferences.

Vector database examples

Vector databases have capabilities tailored to specific performance needs, scalability, and integration with AI tools. Below are some of the most widely used databases:

• • Pinecone: A fully managed, cloud-native vector database built for real-time, low-latency similarity search. It offers automatic indexing, horizontal scaling, and tight integration with AI/ML pipelines.
  • Weaviate: An open-source vector database with built-in support for hybrid search, GraphQL, and automatic vectorization through integrated ML models.
  • Milvus: A highly scalable open-source vector database optimized for large-scale, high-throughput similarity search. It supports multiple indexing strategies and integrates with the Towhee vector pipeline.
  • FAISS (Facebook AI Similarity Search): A library developed by Meta for efficient similarity search and clustering of dense vectors. It’s commonly used in research and production environments, although it’s not a full-fledged database on its own.
  • Couchbase: A distributed NoSQL database that recently added vector search capabilities, enabling users to combine traditional document querying with high-performance semantic search in a single platform.
  • Qdrant: An open-source vector search engine designed for both cloud and edge environments. It’s known for its strong API support, fast performance, and integration with popular ML frameworks.

Each of these tools offers strengths, ranging from cloud-native simplicity to customizable indexing, making them well-suited for AI-driven applications such as semantic search, recommendations, and real-time personalization.

What is a graph database?

A graph database is a type of NoSQL database designed to store and navigate relationships between data using graph structures with nodes, edges, and properties. Nodes represent entities (like people or products), edges define relationships between them (such as “purchased” or “connected to”), and properties store relevant metadata. This structure enables the efficient querying of complex, interconnected data, making graph databases ideal for use cases such as social networks, fraud detection, and recommendation engines.

Graph database benefits and limitations

Graph databases are well-suited for applications that require relationship-heavy queries and flexible data models. They come with numerous advantages, but like any type of database, they may not be the right fit for all use cases.

Benefits

• • Efficient relationship traversal: Optimized for exploring and querying deeply connected data without performance degradation.
  • Flexible schema: Allows for dynamic and evolving data models without rigid table structures.
  • Intuitive data modeling: Graph structures closely mirror real-world relationships, making data models easier to design and understand.
  • Powerful query languages: Languages like Cypher and Gremlin enable expressive and efficient queries on complex relationships.
  • Ideal for specific use cases: Excels in scenarios such as social networks, knowledge graphs, recommendation engines, and fraud detection.

Limitations

• • Not ideal for all workloads: Less efficient than relational databases for transactional operations or flat, tabular data.
  • Limited support for large-scale analytics: Graph databases may struggle with traditional analytical queries and large-scale aggregations.
  • Steeper learning curve: Requires familiarity with graph theory and specialized query languages.
  • Ecosystem maturity: Smaller ecosystem and tooling compared to relational and document databases, depending on the vendor.
  • Scalability concerns: Some graph databases face challenges with horizontal scaling and distributed architectures.

Graph database use cases

Graph databases are particularly well-suited for applications that involve highly connected data and dynamic relationships. Their ability to model and traverse complex structures makes them ideal for a range of modern use cases where traditional databases fall short.

• • Social networks: Graph databases efficiently model and query user connections, enabling features like friend recommendations and influence analysis.
  • Recommendation engines: They power personalized suggestions by analyzing user-item interactions and similarity paths in real time.
  • Fraud detection: Graph structures expose hidden relationships between entities to identify suspicious patterns and anomalies.
  • Knowledge graphs: These systems unify and link diverse data sources, enabling semantic search and enriched data exploration.
  • Network and IT operations: Graphs help map infrastructure dependencies, supporting real-time impact analysis and root cause identification.
  • Supply chain and logistics: Modeling logistics networks as graphs allows for optimized routing, bottleneck detection, and disruption forecasting.
  • Identity and access management (IAM): Graphs clarify permission structures and detect risky access paths across users, roles, and resources.

Graph database examples

Graph databases are purpose-built to store and navigate relationships between entities. Here are some of the most well-known graph database technologies used across industries:

• • Neo4j: One of the most widely adopted graph databases, Neo4j supports ACID compliance and uses the Cypher query language to manage complex, connected data. It’s ideal for recommendation engines, fraud detection, and network analysis.
  • Amazon Neptune: A fully managed graph database service by AWS that supports both property graph (Gremlin) and semantic graph (SPARQL/RDF) models. It’s used to build graph-based applications with low-latency query performance at scale.
  • OrientDB: A multi-model database that integrates graph and document data models. Its hybrid approach makes it suitable for applications requiring both relationship-rich data and flexible document storage.
  • ArangoDB: A native multi-model database that supports graph, document, and key-value storage in one engine. It enables fast graph traversal and is ideal for applications needing real-time analytics and personalized recommendations.
  • TigerGraph: Known for its performance at scale, TigerGraph is designed for deep-link analytics, supporting use cases like real-time fraud detection, supply chain optimization, and enterprise knowledge graphs.
  • JanusGraph: An open-source, distributed graph database built to handle large-scale graph processing. It integrates with big data platforms such as Apache Cassandra, HBase, and Elasticsearch, and is popular in scenarios that require high scalability and fault tolerance.

Graph databases can reveal complex relationships at scale, making them indispensable in modern data architectures, especially when understanding context and connections is critical.

Vector vs. graph database comparison

While both vector and graph databases are designed for handling complex, non-tabular data, they serve distinct purposes and are optimized for different workloads. Vector databases enable similarity search across unstructured data like images or text embeddings, while graph databases focus on representing and querying relationships among entities. The table below highlights their key differences:

Vector databases provide ways to identify similar content, while graph databases provide deep insights into how that content is connected. Together, they power intelligent applications that combine context with semantic understanding.

Vector database and graph database similarities

Despite their distinct architectures and use cases, vector and graph databases share several key similarities. Both are designed to go beyond traditional relational databases by handling complex, high-dimensional, or relationship-rich data. These similarities make them essential components in modern AI, search, and recommendation systems.

Support for non-tabular data

Both databases are built to manage non-relational data structures. Vector databases handle embeddings from unstructured data, while graph databases manage entity relationships that don’t fit into rows and columns.

Advanced query capabilities

Unlike SQL-based systems, vector and graph databases support specialized queries, such as nearest neighbor search in vectors and multi-hop traversal in graphs, that uncover deeper insights and patterns.

Use in AI and machine learning workflows

Vector and graph databases are both frequently integrated into AI pipelines. Vectors represent learned features from models, while graphs can model reasoning, dependencies, and knowledge representation.

High performance at scale

Both types of databases are engineered for large-scale data and low-latency query performance, whether searching across millions of embeddings or traversing a large graph of interconnected entities.

Flexible schema design

They support schemaless or flexible data models, making it easier to improve applications and work with varied data types as needs change over time.

How to choose between vector and graph databases

Choosing between a vector database and a graph database depends on the nature of your data, the type of queries you need to run, and the goals of your application. Each database excels at solving different types of problems, so understanding their strengths will help guide your decision.

Use a vector database if:

• • Your application relies on similarity search across various types of unstructured data, including text, images, audio, and video.
  • You’re working with machine learning embeddings or powering features like semantic search, recommendations, or RAG.
  • You need fast, scalable ANN queries in a high-dimensional space.
  • Your focus is on content similarity, rather than explicit relationships between entities.

Use a graph database if:

• • Your data is highly interconnected, and you need to understand relationships between entities (e.g., people, products, events).
  • You’re building applications that require multi-hop queries, such as fraud detection, social network analysis, or knowledge graphs.
  • You need to model real-world connections with clear semantics and traversal logic.
  • Your queries involve patterns, paths, or dependency chains that are difficult to express in traditional databases.

Use both if:

• • You want to combine semantic similarity with relational context, for example, retrieving similar documents with a vector search, then exploring how those documents relate to others using a graph.
  • You’re building AI systems that require both understanding and reasoning, such as conversational agents, personalized search, or knowledge-augmented applications.

Ultimately, your choice should align with the problem you’re solving. If you need to find similarities, vector databases shine. If you need to find out how things are connected, graph databases are a better fit. In many modern applications, combining the two offers the most powerful solution.

We hope you have a better understanding of the differences and similarities between databases. If you’d like to dive deeper, you can review the related resources below:

• • A Guide to Vector Search – Blog
  • What are Vector Embeddings? – Blog
  • What is Vector Similarity Search? – Blog
  • About Vector Search – Docs

FAQs

Is a graph database the same as a vector database? No, a graph database and a vector database are not the same. Graph databases store and query relationships between entities, while vector databases store high-dimensional vector embeddings for similarity search.

What is the difference between a graph and a vector? A graph is a data structure composed of nodes and edges that represent entities and their relationships. A vector is a numerical array that represents data points in a multi-dimensional space, often used in machine learning and similarity search.

What is the difference between a graph store and a vector store? A graph store is optimized for managing relationships between data using nodes and edges. A vector store is designed for storing and searching vector embeddings based on similarity metrics, such as cosine distance or Euclidean distance.

What is the difference between a vector database and Neo4j? Neo4j is a graph database that excels at traversing relationships, whereas vector databases focus on ANN search for unstructured data, such as images, text, and audio, which are represented as vectors.

Can you use a vector database and a graph database? Yes, you can use both together to combine vector similarity search with relationship-based reasoning, such as using a vector database to find similar items and a graph database to explore their contextual relationships.