Mike's Notes
Pipi 9 uses graph databases and, so far, has no need for vector databases.
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Last Updated
04/01/2026
Vector Database vs Graph Database: Key Technical Differences
By: Guy Korland
FalkorDB: 27/10/2024
Guy Korland serves as CEO at FalkorDB, where he drives graph database
architecture for generative AI and retrieval-augmented generation
workflows. He holds a PhD in Computer Science from Tel Aviv University and
brings over 20 years of experience in database engineering. He previously
led Redis’ incubation arm as SVP & CTO, oversaw platform architecture
as GM & CTO at Stor.ai (Self-Point), co-founded and served as CTO of
Shopetti, and directed R&D as VP at GigaSpaces.
Unstructured data is all the data that isn’t organized in a predefined
format but is stored in its native form. Due to this lack of organization,
it becomes more challenging to sort, extract, and analyze. More than 80% of
all enterprise data is unstructured, and this number is growing.
This type of data comes from various sources such as emails, social media,
customer reviews, support queries, or product descriptions, which businesses
seek to extract meaningful insights from. The rapid growth of unstructured
data presents both a challenge and an opportunity for businesses.
To extract insights from unstructured data, the modern approach involves
leveraging large language models (LLMs) along with one of two powerful
database systems for efficient data retrieval: vector databases or graph
databases. These systems, combined with LLMs, enable organizations to
structure, search, and analyze unstructured data.
Understanding the difference between the two is crucial for developers
looking to build modern AI applications or architectures like
Retrieval-Augmented Generation (RAG).
In this article, we dive deep into the concepts of vector databases and
graph databases, exploring the key differences between them. We also examine
their technical advantages, limitations, and use cases to help you make an
informed decision when selecting your technology stack.
What is a Vector Database?
Vector databases excel at handling numerical representations of
unstructured data — called embeddings — which are generated by machine
learning models known as embedding models, unlike traditional databases that
focus on structured data like rows and columns. These embeddings capture the
semantic meaning (or, features) of the underlying data. Vector databases
store, index, and retrieve data that has been transformed into these
high-dimensional vectors or embeddings.
You can convert any type of unstructured or higher-dimensional data into a
vector embedding – text, image, audio, or even protein sequences – and this
makes vector databases extremely flexible. When this data is converted into
vector embeddings, the data points that are similar to each other are
embedded closer in the embedding space. This allows for similarity (or,
dissimilarity) searches, where you can find similar data using their
corresponding vector representations.
In that sense, vector databases are search engines designed to efficiently
search through the higher dimensional vector space.
For example, in a word embedding space, words with similar meanings or
those that are often used in similar contexts would be closer together. The
words “cat” and “kitten” would likely be near each other, while “automobile”
would be farther away. In contrast, “automobile” might be close to words
like “car” and “vehicle”.
The vector representation of these words might look like this:
"cat": [0.43, -0.22, 0.75, 0.12, ...]
"kitten": [0.41, -0.21, 0.76, 0.13, ...]
"automobile": [0.01, 0.62, -0.33, 0.94, ...]
"car": [0.02, 0.60, -0.30, 0.91, ...]
In this context, the vector representations of the words “cat” and “kitten”
are closer to each other in the vector space due to their semantic
similarity, while “automobile” and “car” would be farther from them but
positioned closer to each other.
illustration of a vector representations of words
How does this help build retrieval systems in LLM-powered
applications?
An example is a Vector RAG system, where a user’s query is first
converted into a vector and then compared against the vector embeddings in
the database of existing data. The vectors closest to the query vector are
retrieved through a similarity search algorithm, along with the data they
represent. This result data is then presented to the LLM to generate a
response for the user.
Vector databases are valuable because they
help uncover patterns and relationships between high-dimensional data
points.
However, they have a significant limitation: interpretability. The
high-dimensional nature of vector spaces makes them difficult to visualize
and understand. As a result, when a vector search yields incorrect or
suboptimal results, it becomes challenging to diagnose and troubleshoot the
underlying issues.
What is a Graph Database?
Graph databases work fundamentally differently from vector
databases.
Rather than using numerical embeddings to represent data, graph databases
rely on knowledge graphs to capture the relationships between
entities.
In a knowledge graph, nodes represent entities, and edges represent the
relationships between them. This structure allows for complex queries about
relationships and connections, which is invaluable when the links between
entities are as important as the entities themselves.
In the context of our earlier example involving “cat,” “kitten,”
“automobile,” and “car,” each of these concepts would be stored as nodes in
a knowledge graph. The relationship between “cat” and “kitten” (e.g., “is a
type of”) would be represented as an edge connecting those two nodes.
Similarly, “automobile” and “car” might have an edge representing a
“synonym” relationship. This would capture the
“subject”-“object”-“predicate” triples that form the backbone of knowledge
graphs.
Nodes: "cat", "kitten", "automobile", "car"
Edges:
(kitten) -[: IS_A]-> (cat)
(automobile) -[: SYNONYM]-> (car)
Graph databases are ideal when your data contains a high degree of
interconnectivity and where understanding these relationships is key to
answering business questions. Also, unlike vector databases, knowledge
graphs stored in a graph database can be easily visualized. This allows you
to explore intricate relationships within your data.
Modern graph databases support a query language known as Cypher,
which allows you to query the knowledge graph and retrieve results. Let’s
look at how Cypher works using the example of a slightly more complex
knowledge graph.
knowledge graph flowchart of Barcelona FC and La Liga
To create the graph shown in the above image, you will need to construct
the nodes and relationships that represent the different entities and their
connections. You can use a graph database like FalkorDB to test the queries
below.
Here’s how we create the nodes:
// Creating Player nodes
CREATE (:PLAYER {name: 'Pedri'}), (:PLAYER {name: 'Lamine
Yamal'});
// Creating Manager node
CREATE (:MANAGER {name: 'Hansi Flick'});
// Creating Team node
CREATE (:TEAM {name: 'Barcelona'});
// Creating League node
CREATE (:LEAGUE {name: 'La Liga'});
// Creating Country node
CREATE (:COUNTRY {name: 'Spain'});
// Creating Stadium node
CREATE (:STADIUM {name: 'Camp Nou'});
You can now create the relationships using Cypher in the following
way:
// Players play for a team
MATCH (p:PLAYER {name: 'Lamine Yamal'}), (t:TEAM {name:
'Barcelona'})
CREATE (p)-[:PLAYS_FOR]->(t);
MATCH (p:PLAYER {name: 'Pedri'}), (t:TEAM {name: 'Barcelona'})
CREATE (p)-[:PLAYS_FOR]->(t);
// Manager manages a team
MATCH (m:MANAGER {name: 'Hansi Flick'}), (t:TEAM {name:
'Barcelona'})
CREATE (m)-[:MANAGES]->(t);
// Team plays in a league
MATCH (t:TEAM {name: 'Barcelona'}), (l:LEAGUE {name: 'La Liga'})
CREATE (t)-[:PLAYS_IN]->(l);
// Team is based in a country
MATCH (t:TEAM {name: 'Barcelona'}), (c:COUNTRY {name: 'Spain'})
CREATE (t)-[:BASED_IN]->(c);
// Players have nationality
MATCH (p:PLAYER {name: 'Lamine Yamal'}), (c:COUNTRY {name:
'Spain'})
CREATE (p)-[:NATIONALITY]->(c);
MATCH (p:PLAYER {name: 'Pedri'}), (c:COUNTRY {name: 'Spain'})
CREATE (p)-[:NATIONALITY]->(c);
// Team's home stadium
MATCH (t:TEAM {name: 'Barcelona'}), (s:STADIUM {name: 'Camp
Nou'})
CREATE (t)-[:HOME_STADIUM]->(s);
As you can see, Cypher queries are easily readable and self-explanatory.
You can query the graph using the following example, where we search for
players who play for Barcelona, along with their nationalities.
MATCH (p:PLAYER)-[:PLAYS_FOR]->(t:TEAM {name:
'Barcelona'})-[:BASED_IN]->(c:COUNTRY)
RETURN p.name AS Player, c.name AS Nationality;
Here’s the example output you will get:
| Player |
Nationality |
| Lamine Yamal |
Spain |
| Pedri |
Spain |
Graph databases are purpose-built to efficiently store, query, and navigate
complex knowledge graphs. Designed for handling large-scale knowledge
graphs, they offer advanced search and querying capabilities.
These databases are especially effective for applications requiring deep
relationship analysis, such as GraphRAG systems, where knowledge graphs can
be integrated with LLMs.
Key Differences between Vector Database and Graph Database
As we saw above, vector databases are optimized for similarity searches
across high-dimensional data using vector embeddings generated by machine
learning models. In contrast, graph databases are designed to model
relationships between entities, making them ideal for
tasks that require analyzing and understanding the connections between
data points.
Here is a detailed breakdown of the key differences:
| Feature |
Vector Database |
Graph Database |
| Data Model |
Represents data as vectors in a high-dimensional space. |
Represents data points as nodes (entities) connected by edges
(relationships).
|
| Query Capabilities |
Efficiently handles similarity search based on vector representations
|
Effective for navigating & managing relationships. Involves graph
traversal, subgraph matching, and shortest-path algorithms.
|
| Performance Considerations |
Well-suited for large-scale, real-time similarity searches. |
Optimized for graph-based operations, such as network analysis and graph
traversals.
|
| Scalability |
Can scale horizontally to handle massive datasets and high-throughput
queries. Scales with the number of data points.
|
Can scale both horizontally and vertically to accommodate large graphs
and complex queries. As it doesn’t have any schema, data can be easily
added and modified. Scales with complexity and relationships of added
data.
|
| Indexing |
Vector databases rely heavily on ANN search for grouping the closest
data points.
|
Graph databases may use a combination of inverted indexes and
graph-specific methods like adjacency matrix or GraphBLAS.
|
Key Similarities between Vector Database and Graph Database
Despite their differences in data representation and use cases, vector
databases and graph databases share several core similarities, especially in
how they support modern AI-driven applications and handle complex
datasets.
Both systems are designed to go beyond traditional relational databases,
allowing developers to extract deeper insights from more complex and often
unstructured data.
Here is a breakdown of their similarities.
| Feature |
Vector Database |
Graph Database |
| Advanced Querying Capabilities |
Enables similarity search via Approximate Nearest Neighbor (ANN) algorithms. |
Allows relationship-based queries using traversal algorithms. |
| Handling Complex and Large Datasets |
Designed for large, high-dimensional datasets like embeddings. |
Optimized for complex, highly interconnected datasets with numerous relationships. |
| Optimized for Modern AI Applications |
Frequently used in AI/ML applications such as recommendation systems, semantic search, etc. |
Ideal for applications requiring knowledge representation. |
| Support for Low-Latency Queries |
Provides low-latency similarity search using efficient ANN algorithms. |
Optimized for real-time graph traversals and querying of relationships between entities. |
| Powering Recommendation and Search Systems |
Powers similarity-based recommendations and semantic search. |
Powers relationship-based recommendations and complex search queries. |
| Integration with AI Models |
Seamlessly integrates with AI models (e.g., LLMs) to transform data into vector embeddings, and also convert user queries into vectors for similarity search. |
Seamlessly integrates with LLMs to transform data into knowledge graphs during ingestion, and convert natural language queries to Cypher during retrieval. |
Vector Database vs Graph Database: Use Cases
When choosing between vector databases and graph databases, the decision
largely depends on the nature of your data and the types of queries you need
to perform. Below are key use cases for both, along with specific examples
illustrating their advantages across various fields.
Fraud Detection
Graph Databases:
- Graph databases are highly effective in fraud detection due to their
ability to model complex relationships between entities such as users,
transactions, accounts, and devices.
- In financial systems, fraud often occurs within networks of interactions,
where suspicious behavior is revealed through unusual patterns.
- A graph database can analyze these relationships to identify potential
fraud by traversing the network and detecting anomalies, such as unusual
fund transfers or connections between seemingly unrelated accounts.
- For instance, a query might explore the paths between accounts to uncover
suspiciously interconnected transactions indicative of a money laundering
scheme.
Vector Databases:
- While vector databases are less commonly used for direct fraud detection,
they can contribute by detecting anomalous behavior based on historical data
patterns.
- By embedding user behavior (e.g., browsing history, transaction patterns)
as vectors, vector databases can identify instances where behavior deviates
significantly from typical patterns through dissimilarity search. These
deviations might suggest fraud and prompt further investigation.
Scientific Research
Graph Databases:
- In scientific research, graph databases are invaluable for modeling complex
systems where relationships between entities are critical.
- For example, in biological research, entities like proteins, genes, and
diseases are represented as nodes, while interactions between them (e.g.,
protein-protein interactions) are represented as edges.
- Researchers can use graph traversal algorithms to uncover hidden
connections between diseases and genetic markers, leading to new insights in
genomics and drug discovery.
- Knowledge graphs are also used in academic networks to trace citations and
collaborations between researchers, identifying influential papers or
emerging trends in a field.
Vector Databases:
- Vector databases can also be applied in scientific research, particularly
in fields like bioinformatics, where high-dimensional data such as DNA
sequences or protein structures are common.
- By converting these biological structures into vector embeddings,
researchers can perform similarity searches to identify patterns in large
datasets.
- For instance, vector databases can be used to compare protein structures,
searching for similar sequences across vast biological datasets to identify
evolutionary relationships or potential drug targets.
eCommerce
Graph Databases
- In ecommerce, graph databases are highly effective for recommendation
systems and customer journey analysis.
- By modeling the relationships between customers, products, and
transactions, ecommerce platforms can generate personalized recommendations
by traversing the graph to find connections between users with similar
purchasing histories or interests.
- Additionally, graph databases can track inventory, supplier relationships,
and logistics, optimizing the entire supply chain by analyzing relationships
across the network.
Vector Databases
- Vector databases enhance ecommerce applications by enabling personalized
recommendations based on user behavior and product similarities.
- By converting user interactions (e.g., clicks, purchases) and product
descriptions into vector embeddings, ecommerce platforms can use vector
databases to identify products similar to those users have interacted
with.
- This technique is widely used in product recommendation engines, where
users are presented with items similar to their previous searches or
purchases, boosting engagement and conversion rates.
Media and Entertainment
Graph Databases
- The media and entertainment industry benefits from graph databases by
modeling content recommendation networks and social relationships.
- For example, streaming platforms like Netflix and Spotify use graph
databases to map user preferences, social connections, and content
relationships (e.g., actors, genres, directors).
- These platforms can then traverse the graph to recommend new movies or
songs based on the preferences of similar users or related content.
Additionally, graph databases can manage complex relationships between media
assets (e.g., episodes, seasons, franchises) and their metadata.
Vector Databases
- In media and entertainment, vector databases enable content-based search
and recommendation systems by using vector embeddings for media
content.
- For instance, a vector database can store embeddings of movies, TV shows,
or songs, capturing their semantic features.
- Users can search for media by uploading images, audio, or even
descriptions, and the vector database will return content that is
semantically similar.
- In applications like music discovery, vector databases help recommend songs
with similar audio features, while in video search, they enable finding
visually similar content based on user preferences or searches.
structure of a knowledge graph
How to Choose between Vector Database and Graph Database
Choosing between a vector database and a graph database depends on several
key factors, including the nature of your data, your application’s
requirements, and how you intend to query and use the data.
Below are the most important considerations to guide your decision-making
process:
Understand Your Data
The first step in choosing between a vector or graph database is
understanding the type of data you are working with.
- Vector Database: If your data is high-dimensional, such as images,
multilingual text, audio, or video, then a vector database is a better fit.
For instance, if you are working with embeddings from image recognition
models, a vector database allows you to store these vectors and efficiently
perform similarity searches between them.
- Graph Database: If your data is knowledge-oriented and the
relationships between entities are of primary importance, then a graph
database is the right choice. For example, if you are modeling social
networks, supply chains, or recommendation systems, where the relationships
between entities (nodes) drive your queries and insights, graph databases
are optimized for these scenarios.
Performance and Scalability Needs
Both vector and graph databases are designed to scale, but they have to be
managed differently as the dataset grows.
Vector Database: Vector databases excel in low-latency searches even
with millions of vectors. Techniques like Approximate Nearest Neighbor (ANN)
algorithms ensure that similarity searches can be performed in near
real-time, making them ideal for large-scale AI/ML applications. If your
application requires fast retrieval of items based on vector similarity, and
you expect the dataset to grow continuously, vector databases are optimized
for this.
Graph Database: Graph databases, while scalable, face more
challenges with performance as the graph becomes more interconnected and
deeper. If your application requires complex, multi-hop queries across
deeply connected data, you will need to ensure your graph database can
handle the load. However, for applications that involve exploring
relationships (e.g., shortest paths, friend-of-a-friend queries), graph
databases offer performance advantages over relational models. Be mindful
that as the graph grows, advanced partitioning and optimization strategies
may be needed to maintain performance. In such scenarios, you should
consider a graph database known for its low latency and scalability.
Evaluate the Specific Advantages of Each Technology
Weigh the advantages and trade-offs of each database based on the technical
requirements of your application.
Graph Database: If relationship analysis and graph traversal are
core to your application, then graph databases are unmatched in their
ability to model and query complex, interrelated data. The flexibility to
modify schema on the fly and the power to model rich, interconnected data
make graph databases the best choice for knowledge-centric
applications.
Vector Database: Offers clear advantages for AI-powered applications
that rely on embeddings. However, they lack interpretability and are not
ideal for applications that require understanding relationships between data
points.
An Integrated Solution with FalkorDB
FalkorDB is a low-latency graph database graph with select vector
capabilities. It offers high-speed performance for both graph traversals and
vector similarity searches.
Some key features of FalkorDB include:
- Integrated Data Management: FalkorDB’s unified structure allows for
concurrent storage and querying of graph relationships and vector
embeddings. This integration eliminates the need for multiple specialized
databases, simplifying data architectures.
- Advanced Query Processing: The system employs algorithms to optimize
queries that involve both graph connections and vector similarities.
- Robust Scalability: FalkorDB maintains rapid response times even as data
volumes expand, making it suitable for evolving data needs and streaming
data.
- Streamlined Operations: By combining graph and vector functionalities,
FalkorDB reduces the complexity associated with managing and synchronizing
separate database systems.
This approach offers a compelling solution for organizations seeking to
leverage both semantic relationships and vector-based similarity in their
data operations, all within a single, powerful platform.
Knowledge Graph Ecosystem
Additionally, FalkorDB comes with an ecosystem of tools that simplify the
process of building applications that derive insights from unstructured
data. Here are some:
GraphRAG-SDK
- This SDK is designed to simplify the creation of Graph Retrieval-Augmented
Generation (GraphRAG) systems. It integrates with FalkorDB and LLMs like
OpenAI’s GPT and Google’s Gemini. It enables developers to build knowledge
graphs from unstructured data and query them using LLM-generated Cypher
queries.
- The SDK is particularly useful for building AI systems that require
reasoning over complex data relationships, such as in finance, legal, or
healthcare domains.
FalkorDB-Browser
- This tool is a visualization interface for exploring and managing graph
data stored in FalkorDB. It allows users to interactively navigate through
nodes and edges, facilitating data exploration in large knowledge
graphs.
- The browser is ideal for users who need to visually understand the
structure of their data or monitor real-time changes in a dynamic graph
system
FalkorDB CodeGraph
- This tool transforms a codebase into a knowledge graph that visualizes
relationships between different code entities like classes, functions, and
variables.
- By analyzing the structure of the code, developers can gain insights into
dependencies, detect bottlenecks, and optimize software projects.
Knowledge Graph Ecosystem
Based on the detailed walkthrough above, you now have a comprehensive
understanding of vector databases and graph databases. This knowledge equips
you to choose the most suitable database type for your project, depending on
your specific data structures and query requirements.
To get started, here are the links to the documentation, cloud platform,
and community channels of FalkorDB.
