Graph vs Vector Databases: Why Enterprise AI Needs Both Similarity and Context

Generative AI agents and workflows often use vector databases to improve response accuracy, but vector databases miss essential context: the connection between facts that is frequently needed to provide trustworthy answers. 

For example, a vector database can tell you if a sentence is about Bob barbequing and what other sentences are about someone barbequing. However, if Person A, Bob, purchased Item B, a barbecue grill, and Item C, a long meat fork, is an accessory of Item B, then a similarity search will probably tell you that Bob is nothing like a barbecue grill, which is not very similar to a meat fork, which is also nothing like Bob. It won’t tell you that if Bob bought a barbecue grill, he might also wish to buy a meat fork.

On the other hand, graph databases were specifically designed to make connections like these across multiple relationship hops, but were not designed to find semantic similarity. A graph database might not be able to see any relationship between barbecue grill and smoker, while a vector database might tell you that they are semantically similar items.

The two types of databases have very different strengths and weaknesses. This article will guide you through a practical comparison of the two types of databases, how they work in an enterprise RAG (Retrieval Augmented Generation) AI workflow, and why you might want to consider using both.

You’ll learn:

• The value and shortfalls of graph databases and vector databases for RAG
• When to use one or the other in an enterprise AI project
• How using both provides a balance and unlocks capabilities that neither can deliver alone

As an example of how vector databases work, the word “king” might be stored with a high number for the dimensions: man + leader + inherited. You could ask, “What is like a king, but a woman?” Since the database stored queen with very similar numbers for: woman + leader + inherited, it would return correctly, “queen.” This makes vector databases powerful for document retrieval questions like, “What scientific papers were written about growing food in microgravity?”

Vector databases are also popular for LLM RAG context pipelines. Their Achilles’ heel for this use case is when multiple similarities between things need to be found to locate patterns, or when similarity is not the type of relationship that is relevant. For instance, a vector database can tell if two financial transactions look similar, but cannot tell if the people or organizations that made them are related to each other through mutual relationships, such as a fraud ring.

The core strength of graph databases is precisely traversing connections across multiple hops. They excel at surfacing deeper context about how things relate to each other, rather than how similar they are. For example, “king” and “serf” would likely be very closely related, while not very similar. In use cases where following the relationships is key, graph databases shine. Examples include fraud detection, knowledge graphs, supply chain, and customer 360. This article will discuss those in greater detail later.

Where vector databases find the probable similarity between two things, graph databases find the exact relationships and connections across multiple things.

Graph Database vs Vector Database – Key Differences

Looking closer at the two types of databases, one thing is clear: These are not competing to replace each other; rather, they are architecturally different tools specializing in answering different types of questions. 

Have a look at a direct comparison of features.

This article will focus a lot on explainability and hallucination in particular since they are the most commercially significant differences for enterprises evaluating AI infrastructure. The risk of error is lower in graph, due to defined and easily followed explicit relationships. A vector database’s similarity ranking is always going to be less precise. There are potentially thousands of dimensions that might have contributed to that ranking, and those dimensions are all artificial. Even if you could explain what each one meant, the relative importance of different aspects can’t be customized to put more weight on what is important for a particular situation. A graph lets you ask about the specific facts you care about right then.

When Vector Databases Are the Right Tool

In many cases, vector databases are the exact right tool for the job. Several examples of practical use cases for vector databases include:

• Document search and retrieval: As mentioned before, a semantic similarity search is exactly the type of thing needed to answer questions like, “What research papers from the last five years were written about genetic links to cancer?”
• Image search: Stored vector embeddings of images can be searched to compare the vector embeddings of the search image with stored images. This returns stored images that are highly similar to the searched image.
• Computer vision: This is often a more complex version of image similarity search. Captured images are broken into regions and vectorized. Vector databases perform similarity searches with newly acquired images to match what is now being captured with other images already obtained.
• Product recommendations: When user preferences and product attributes are converted to vector embeddings, finding semantically similar things becomes ideal for identifying what product should be recommended alongside another for a specific user. While this is an excellent vector use case, it is also one where adding graph relationship intelligence boosts effectiveness.
• RAG pipelines: This use case is driving a lot of adoption of vector databases. A vector database is a valuable component of RAG pipelines, but when used alone, it works best for early-stage or lightweight pipelines. Production RAG pipelines often combine multiple types of databases, including both vector and graph.

When Graph Databases Are the Right Tool

Sometimes graph databases – either by themselves or in coordination with vector databases – are the right solution. Relationship intelligence is essential to solving certain problems. There are several use cases where graph databases are required:

• Fraud and financial crime detection: Graph analysis surfaces shared resources, and relationship rings using traversal across accounts, devices, and transactions.
• Knowledge graphs for enterprise AI: Knowledge graphs unify enterprise data into a structured, explainable context that grounds LLM responses and reduces hallucinations.
• Supply chain dependency mapping: Relationships can determine risk across multi-tier supplier networks and relationship visibility often spotlights potential problems.
• Customer 360 and entity resolution: Uniting fragmented and duplicated identity data from across various systems can resolve entities into a single, queryable view.
• Agentic AI infrastructure: A graph database can provide the structured memory and multi-step context logic that autonomous AI agents need to reason and act reliably.

Why Enterprise AI Needs Both Graph + Vector Hybrid Search

Vector databases solve part of the problem of many RAG use cases by retrieving what looks relevant. Graph databases determine with certainty what is actually connected. GraphRAG enhances traditional vector-only RAG with knowledge graphs built into the LLM inference process. Enterprise AI needs both types of information. In fact, advanced graph databases like TigerGraph now have a multi-modal vector database integration that provides a graph database with vector search, vector similarity, and graph traversal within a single query. This hybrid search delivers several capabilities that neither type can deliver alone.

Here are examples of what a hybrid search enables you to do:

1. Improve retrieval relevance: Relevancy is the degree to which the response answers the specific question asked. Vector searches don’t natively understand multi-hop questions, relationships, or the reasoning chains that are so essential to agentic AI. These types of multi-relationship questions are far more suited to a graph database, but similarity is still needed to address some individual steps of the overall task.
2. Reduce hallucinations: Being confidently wrong, or citing details that do not actually exist, is the biggest problem with AI today.  When context determines correctness, a similarity search alone isn’t enough. Interpreting what connects one thing with another becomes key. Examples include seeing supply chain downstream dependencies or distribution networks, or understanding that a single customer moved across channels.
3. Increase accuracy: Reducing false positives and providing correct results are two aspects of the same thing, accuracy. Rather than simply finding things that are similar, such as two financial transactions, a hybrid graph and vector RAG workflow combines that with how things are related, such as if the accounts are using the same shared device.
4. Expand explainability: In a graph, you can trace outputs through explicit relationship paths to explain results. Similarity scores can partially support that, but the high-dimensionality equations used to create them are too complex to be used as explanations by themselves.
5. Enhance clarity: Instead of just fetching documents or embeddings, it traverses the relationships in a graph and retrieves the entities, relationships, behaviors, and rules to give full context that is clear, grounded, and explainable.
6. Reduce cost and complexity: A unique advantage of having both vector and graph capabilities in a single database is the simplification of your overall AI architecture and LLM tokens needed. Reducing infrastructure complexity also reduces maintenance and costs over time.

TigerGraph’s native graph and vector hybrid search capability is a good example of an enterprise-level implementation of this architecture. One query accomplishes both graph traversal and vector semantic similarity search. Together, vector search finds what is similar, and graph traversal uncovers how things are similar or related and why those similarities matter in real-world contexts. These two technologies are far more powerful when combined.

What’s the Difference Between Knowledge Graph and Vector Database?

Knowledge graphs are a type of graph database application. A knowledge graph is essentially a network of interconnected facts that unifies data from scattered sources, stores the data as entities and relationships, and makes it far easier to find the data and reason about it. 

For AI models and agents, knowledge graphs are the content layer AI needs to be confidently correct, rather than confidently wrong. A knowledge graph provides context so the AI can not only find information but also find how that information relates to everything else. 

That begs the question of where vector databases fit relative to knowledge graphs. Vector search expands semantic reach while the knowledge graph confirms structural validity and grounds the result in verified enterprise data. Knowledge graphs provide context, structured reasoning, explainability, logic, and even the relevant business policies to AI.

AI can work with a knowledge graph alone, and AI can work with a vector database alone, too. The best AI – with the highest accuracy rates, the most useful output, which is likely to provide an organization with the highest return on its investment – uses both a knowledge graph and a vector database.

Final Thoughts on Graph and Vector Databases for AI

Vector and graph databases solve different problems. The enterprises getting the most from AI are using both, with graph providing the structural and contextual foundation that vector alone cannot. Vector databases tell you what is similar. Graph databases tell you how things are related in a real-world context. Vector similarity tells you what is most alike. Graph relationships tell you why that is important.

Combining graph and vector databases in enterprise AI architecture is not just a nice to have; it creates reliable, intelligent, contextually aware AI applications that hallucinate less, are more accurate, and have the explainability that legal compliance requires.

To learn more, explore the TigerGraph hybrid graph and vector capability or request a demo.

FAQs

What is the difference between a graph database and a vector database?

Graph databases store data as entities and precise relationships.  (Sara) -[works_with]-> (Jim)Sara -works_with-> Jim. (Jim) -[works_at]-> (Costco)Jim -works_at-> Costco. A graph database answers specific questions like, “Where do Sara and Jim work?” Vector databases store things as a broad set of numerical values indicating how similar something is. They answer questions like, “Which thing is most like Sara? Jim or Costco?”

Can a graph database replace a vector database?

No. A graph database cannot replace a vector database and neither can a vector database replace a graph. They each store data differently and answer different types of questions. For example, if you wish to find all the books in a large library about frogs, even though the titles might not have the word “frogs” in them – it might say small amphibians or anura or little furless creatures that hop – a vector database is the only thing likely to work. If you want to know how many degrees of separation you have from Kevin Bacon on a social media platform, and who the connections are, a graph database is the only thing likely to work.

What is GraphRAG, and how does it relate to vector search?

RAG (Retrieval Augmented Generation) is a way to improve the accuracy, currency, and relevance of an AI’s output by providing it with information on a specific domain. This can be done with a graph database or a vector database. GraphRAG is a graph-enhanced data augmentation for AI that improves on vector RAG by adding structural context to a vector database’s similarity searches.

When should I use a knowledge graph instead of a vector database?

When the main types of analysis you need to do on the data focuses on relationship or context understanding, a graph database is essential. A graph database is also needed if your project has explainability requirements.

Does TigerGraph support vector search?

Yes, TigerGraph has native hybrid graph and vector capability that fully supports vector search.