Knowledge Graph vs Vector Database: Which Is Better for Enterprise AI?

In the knowledge graph vs vector database choice, the difference is what each one stores. A vector database stores chunks of text as embeddings and returns the chunks closest in meaning to a question; a knowledge graph stores entities, such as people, accounts and projects, and the relationships between them, so an AI can follow connections instead of matching text. For enterprise AI, a vector database is the faster way to answer questions found in one document, and a knowledge graph is the more reliable way to answer questions that span many documents and tools.

That is the short answer. The useful answer depends on what your company actually asks its AI, what your data looks like, and who will maintain the system. I work at Coworker, which builds a company knowledge graph called OM2, so I have a view, and I say where vector search is the better choice. Every research result below links to its source; I read them on November 9, 2026.

If you are new to the concept, start with what an enterprise knowledge graph is and how a context graph differs from a knowledge graph.

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

Both are ways to give a language model the right context before it answers. That pattern is retrieval-augmented generation, or RAG. "Vector RAG" retrieves similar chunks. "GraphRAG" retrieves from a graph. My post on RAG vs MCP covers how either one reaches the model.

How does a vector database work?

A vector database turns each chunk of text into an embedding, a list of numbers that places the chunk in a space where similar meanings sit close together. When a question comes in, it is embedded the same way, and the database returns the nearest chunks. Pinecone describes semantic search as finding the records "most similar in meaning and context to a query," also called nearest neighbor search (Pinecone docs).

Most production systems use approximate nearest neighbor search, which trades a little accuracy for a lot of speed. You do not always need a separate product: pgvector adds exact and approximate nearest neighbor search to Postgres, and many graph databases, including Neo4j, now have vector indexes too.

Vector search is popular for good reasons. It works on any text without a schema, it handles different wording for the same idea, and it is cheap to set up. OpenAI's file search tool, for example, stores your files in vector stores and retrieves from them with semantic and keyword search (OpenAI docs).

The limit is that each chunk is judged on its own. Ask "which customers who asked for SSO this quarter are also up for renewal?" and the answer is not in any single chunk. It lives in the links between support tickets, CRM records and calendars, and similarity search has no notion of those links.

How does a knowledge graph work?

A knowledge graph stores facts as nodes and relationships: "Acme" (account) "is owned by" "Priya" (person); "Ticket 431" "was raised by" "Acme"; "SSO" "was requested in" "Ticket 431." The meaning lives in the structure. To answer a question, the system finds the entities the question is about and follows their relationships to the facts connected to them.

Building one from company data takes four jobs:

1. Extraction. Pull entities and facts out of documents, messages, tickets and records. LLMs now do much of this.
2. Entity resolution. Decide that "Acme," "Acme Corp" and "acme.com" are the same account. Atlan's guide calls this the step most likely to stall a build and estimates 6 to 12 weeks to construct a graph for one domain (Atlan).
3. Schema or ontology. Decide which entity and relationship types exist. TigerGraph warns that retrofitting a schema at enterprise scale means re-extracting and re-ingesting everything, which typically takes months (TigerGraph).
4. Freshness. Update facts as the business changes, and know when a fact stopped being true.

This is why "knowledge graph" means two different purchases: a graph database where you do these jobs yourself, or a platform that does them for you. I compare both kinds in my roundup of knowledge graph software.

Knowledge graph vs vector database: which gives more accurate answers?

Neither wins everywhere. The best evidence says they win on different kinds of questions.

The most careful head-to-head

Han and colleagues at Michigan State compared vector RAG and several GraphRAG methods under one evaluation protocol across question answering and summarization benchmarks. Their finding: "RAG performs better on single-hop and detail-oriented factual queries, whereas GraphRAG is more effective on multi-hop, reasoning-intensive questions" (Han et al., arXiv 2502.11371). They also found that reranking and iterative retrieval help both.

Structured enterprise data

In a 2023 benchmark on an industry-standard insurance data model, data.world's researchers found GPT-4's average accuracy across 43 business questions was 16.7% when querying the SQL database directly and 54.2% when querying a knowledge graph built over the same data (Sequeda, Allemang and Jacob). That compares a graph with raw SQL, not with vector search, the vendor ran it, and models have improved since. It still shows how much an explicit model of entities and relationships helps an LLM reason about business data.

Questions about a whole dataset

Microsoft Research's GraphRAG paper targets "global" questions, such as "what are the main themes across these documents?", that no single chunk can answer. Its method has an LLM build an entity graph and pre-write summaries for groups of related entities (Edge et al., Microsoft Research). Microsoft reports this produces "more comprehensive and diverse answers than standard RAG for large-scale tasks" (BenchmarkQED).

Combining the two

BlackRock and NVIDIA researchers tested HybridRAG, which merges context from a knowledge graph and a vector store, on financial earnings call transcripts, and report it outperformed both vector RAG and GraphRAG on their own, at both the retrieval and answer generation stages (Sarmah et al., arXiv 2408.04948).

Two cautions. Most of these benchmarks use public datasets such as Wikipedia questions or novels, not the mix of Slack threads, tickets and CRM fields a company runs on. And results depend heavily on how well each system was built. A sloppy graph loses to a good vector index.

What questions does each one get wrong on company data?

Abstract benchmarks are hard to map to your own data, so here are the patterns I see in enterprise questions.

Where vector search struggles

• Relationship questions. "Who owns the Acme account, and what did they promise in the last call?" The owner is a CRM field and the promise is in a transcript. Similar chunks will not join them.
• Counting and lists. "How many customers asked for SSO this quarter?" Nearest neighbor search returns the top few similar chunks, not every match, so the count comes out wrong.
• Change over time. "What changed on the Globex deal since last week?" Similarity has no idea which chunk is newer or which fact replaced another.
• Same name, different thing. "Phoenix" can be a project, a customer and a city. Without entities, chunks about all three look alike.

Where a knowledge graph struggles

• Anything it did not extract. If the extraction step missed a fact, the graph cannot return it, while a vector index still holds the raw text.
• Wording-level questions. "Find the paragraph in our security policy about password rotation." That is a lookup of a passage, and vector or keyword search does it well.
• New or unstructured topics. A graph only knows the entity and relationship types it was built to capture.

This is why the context you hand a model matters more than the store you pick. Long, loosely relevant context also degrades answers, a problem I cover in context rot and context windows.

What does one question look like in each system?

Take a question a customer success lead might ask an AI assistant on a Monday morning: "Which of my accounts raised a billing issue in the last two weeks, and did anyone reply?"

In a vector database

The question is embedded and compared with every chunk in the index. The nearest chunks are probably support tickets and Slack messages that mention billing. Some will belong to this person's accounts and some will not, because "my accounts" is a relationship stored in the CRM, not a phrase in the tickets. "The last two weeks" may be handled by a date filter if someone added one at ingestion. "Did anyone reply" needs the thread structure, which chunking often breaks apart. The model then reads several long chunks and tries to assemble an answer. It may be right. It may also miss an account whose ticket used the word "invoice" instead of "billing," or include one that belongs to a colleague.

In a knowledge graph

The system starts from the person, follows "owns" to their accounts, follows "raised" to tickets created in the last two weeks, filters to tickets about billing, and checks each for a reply. The model receives a short, structured list: account, ticket, date, replied or not, with a link to each source. The answer is complete only if extraction caught every ticket and classified each one correctly, which is the graph's weak point. But it does not depend on the right chunks happening to score highest.

What the example shows

Questions that start from a person, an account or a project and ask about related records favor the graph. Questions that start from a topic and ask for a passage favor vectors. Most real assistants get both kinds in the same afternoon, which is the practical case for using both.

What does each one cost to build and run?

Vector databases are cheap to start

Embedding text costs little, and setup is mostly chunking and indexing. The hidden cost shows up at answer time: if retrieval returns long, loosely relevant chunks, the model reads more tokens for every question, and that is billed on every call. I break down how this adds up in AI token cost.

Knowledge graphs cost more up front

Extraction with an LLM means model calls over your whole corpus, plus the work of entity resolution and schema design described above. Microsoft's own LazyGraphRAG post is frank about this: it introduced LazyGraphRAG because full GraphRAG's up-front indexing is expensive, and reports LazyGraphRAG's indexing costs are "identical to vector RAG and 0.1% of the costs of full GraphRAG" (Microsoft Research).

The trade can flip at answer time

A graph that returns a few precise facts gives the model less to read than a stack of chunks, so per-question cost can drop even when building the graph cost more. Whether it does depends on how much you ask. A team that runs thousands of agent tasks a day pays the indexing cost once and the reading cost every time.

How do permissions work in each?

This is the question enterprise security teams ask first, and it applies to both.

Company documents carry access rules: this folder is HR only, this channel is private, this deal is visible to the sales team. When documents are chunked and embedded, those rules have to travel with every chunk and be checked at query time. The OWASP RAG Security Cheat Sheet calls missing per-chunk access control "the most common compliance failure in enterprise RAG deployments" (OWASP).

A knowledge graph has the same problem in a different shape: facts extracted from a private document must stay private, and a relationship that crosses two documents must respect the stricter of the two. Neither technology solves this by itself. Whichever you choose, ask how permissions are captured at ingestion, checked at retrieval, and updated when someone loses access.

Is GraphRAG the same as a knowledge graph?

Not quite. A knowledge graph is the data: entities, relationships and facts. GraphRAG is a retrieval method that uses a graph to pick what context goes to the model. Microsoft's open-source GraphRAG library builds its own graph from your text with an LLM, then searches it, and its repository now says the project is "largely in maintenance mode" (GitHub).

So you can have a knowledge graph without GraphRAG (a graph you query directly for analytics), and you can have GraphRAG without a hand-built knowledge graph (an LLM-generated graph over a document set). For agents, what matters is whether the graph reflects your actual business entities and stays current.

Do you have to choose between a knowledge graph and a vector database?

Usually not. The line between the two products has blurred:

• Graph databases add vectors. Neo4j supports vector indexes in both Community and Enterprise editions, so similarity search and graph traversal can live in the same database (Neo4j docs).
• Vector tools add structure. OpenAI's file search already combines semantic and keyword search, and many teams add metadata filters for dates, owners and permissions.
• Hybrid retrieval is a research pattern. HybridRAG and the Han et al. study both point toward using each method where it is strong.

A practical split: use vector or keyword search to find passages, and a knowledge graph to know who and what those passages are about. Even Glean, which runs a knowledge graph behind its search, argues for combining the two in its own comparison.

How do you decide which one your company needs?

Then ask three more questions. Who will maintain it once the pilot is over? How will permissions work? And how will your AI tools reach it: through your own retrieval code, or a standard interface such as the Model Context Protocol?

Should you build it yourself?

Many teams start by building: a vector database, an embedding job, some retrieval code. It works for a demo. The hard parts come later: keeping indexes current across dozens of tools, enforcing permissions, resolving entities, and deciding what to do when a vector store cannot answer relationship questions. Adding a graph on top multiplies that work.

Build when retrieval is your product, or when your data is one well-defined domain your team understands. Buy when the goal is giving company-wide AI the context of your business and the data lives in the SaaS tools everyone uses. I go through the trade-offs in build vs buy an AI context layer, and the wider architecture in the enterprise AI stack.

How does Coworker approach this?

Coworker builds a company knowledge graph for you. OM2, Coworker's organizational memory, connects to 50+ work tools and turns every message, doc and meeting into small, sourced facts linked to the people, accounts and projects they mention. When a fact changes, OM2 marks the new one current and keeps the old one as history, and it answers with exact graph queries within each person's access. As Coworker's platform page puts it, enterprise search returns documents to read, while OM2 pre-synthesizes facts, "so it answers relationships, not just lookups."

Answers are scoped to what each person can already see in the source tools, and the same graph is available in Coworker's apps or in Claude, ChatGPT and Cursor through Coworker MCP. For a specialized domain, OM2 Custom Graph drafts an ontology from your data, or uses yours, with a person approving each change.

On Coworker's own benchmark of 100 tasks, Claude working through Coworker MCP was compared with Claude using its native connectors. With Coworker Learning on, the full set ran 75.5% cheaper and context-heavy retrieval tasks about 9x cheaper, and reviewers preferred Coworker's answers 84.5% of the time in blind comparisons. These are Coworker's own statistically significant benchmarks comparing Coworker MCP with Claude's native tooling.

Where it does not fit: if you need a vector index you control for a search product, or a graph database you query with your own schema, use the tools built for that, such as pgvector or Neo4j. Coworker is for giving AI the context of your company, not for building your own retrieval engine. More background in the AI memory layer and context layers. To see it on your data, book a demo.

Frequently asked questions

Is the vector database outdated?

No. Vector search remains the standard way to find passages by meaning, and it keeps getting built into other systems, including Postgres through pgvector and graph databases such as Neo4j. What has changed is that many teams no longer treat it as the whole answer for enterprise AI, because similarity alone misses relationships, counts and change over time.

Is the knowledge graph still relevant?

Yes, and more so for AI agents. Agents need to know who owns what, what changed and how things connect, which is what a knowledge graph stores. Research comparing the two finds graph-based retrieval does better on multi-hop questions, while vector retrieval does better on single-passage lookups (Han et al.).

Can Neo4j be used as a vector database?

Yes. Neo4j supports vector indexes on node and relationship properties in both Community and Enterprise editions, and you can combine vector search with graph traversal and full-text search (Neo4j docs). Teams with large, purely vector workloads may still prefer a dedicated vector store.

Does ChatGPT use a vector database?

OpenAI's file search tool, which lets its models retrieve from your uploaded files, stores documents in vector stores and retrieves with semantic and keyword search (OpenAI docs). OpenAI does not publish the full internals of ChatGPT itself. For connecting ChatGPT to company data, see my post on ChatGPT and MCP.

Is GraphRAG better than RAG?

For multi-hop questions and summaries across a whole dataset, research generally favors GraphRAG; for detailed single-passage questions, standard vector RAG does as well or better (Han et al.). GraphRAG also costs more to index, which is why Microsoft built LazyGraphRAG with indexing costs at 0.1% of full GraphRAG.

Which is cheaper, a knowledge graph or a vector database?

A vector database is cheaper to set up. A knowledge graph costs more to build because of extraction, entity resolution and schema work, but it can cost less per question if it gives the model a few precise facts instead of long chunks. Total cost depends on how many questions you run.

What is hybrid RAG?

Hybrid RAG retrieves context from both a vector store and a knowledge graph, then gives the combined context to the model. The vector side finds relevant passages by meaning; the graph side adds the entities, relationships and facts connected to the question. In the HybridRAG study on financial earnings call transcripts, the combined approach outperformed both vector RAG and GraphRAG on their own (Sarmah et al.). The trade-off is running and keeping two systems in sync, which is why some teams buy a platform that handles both.

Do I need a graph database to have a knowledge graph?

Not necessarily. A graph database is one place to store a knowledge graph, but platforms such as Coworker and Glean build and serve a company knowledge graph without you running a database. You need your own graph database when you want to design the schema and query it directly.

Related reading

• Best knowledge graph software for enterprise AI
• What is an enterprise knowledge graph?
• What is a context graph?
• AI agent memory
• Mem0 vs Zep vs Letta
• Best MCP servers for databases
• LangChain vs LlamaIndex
• Best enterprise search software