Knowledge graph vs vector database

A vector database finds text that is semantically similar to a question, which makes it strong for searching documents and weak for counting, aggregating, or filtering exact records. A knowledge graph stores entities and the typed relationships between them, which makes it strong for precise multi-hop questions across systems and weak for fuzzy recall over unstructured writing.

What a vector database actually does

A vector database stores embeddings: numeric representations of text, images, or audio, positioned so semantically similar items sit close together in a high-dimensional space. A question is also converted to an embedding, and the database returns the stored items nearest to it, usually measured by cosine similarity.

For the wider context, see our knowledge graph glossary of 40 terms.

This is the mechanism behind most retrieval-augmented generation, or RAG, setups: a policy clause is embedded once and later retrieved because its meaning overlaps with a question, even if the wording differs. Ask "can I cancel mid-contract" and a search can surface a clause about "early termination" without either phrase matching literally. That is the value: recall based on meaning, not keyword overlap.

What similarity search does not do matters just as much. It does not know one number is greater than another, and it does not know two records refer to the same customer unless the text says so directly. It returns items ranked by a distance score, nothing more. Whatever a language model builds from retrieved text depends on that retrieval step finding the right passages first.

What a knowledge graph stores instead

A knowledge graph stores entities as nodes, such as a customer, an invoice, or a support ticket, and relationships as typed edges: this invoice belongs to this account, this ticket was filed by this customer. There is no similarity math in a basic traversal. "Which accounts have an overdue invoice and an open ticket" is answered by walking explicit edges, filtering each by status and date, and intersecting the results.

This is closer to a relational join than a search engine, except the joins already exist as edges rather than being written fresh each query. The graph does not guess a relationship. Either an edge exists, built during entity resolution, or it does not, and the traversal returns exactly what the structure supports, naming specific records rather than a ranked list of relevant text.

Where similarity search wins

Vector search is the right tool when a question is really about unstructured content and the goal is finding relevant material, not computing an exact number. Searching a knowledge base for "how do we handle a refund on a partially shipped order" works well with embeddings, because policy wording rarely matches a customer's phrasing exactly. Surfacing similar past tickets, contract clauses, or reviews are all recall problems over text.

It also scales well to large, messy document collections. There is no need to model the internal structure of a thousand PDFs before searching them. Embed the text, store the vectors, and similarity search surfaces reasonable candidates without a schema designed first.

Where similarity search breaks down

The failure shows up once a question needs an exact answer. "How many enterprise accounts churned last quarter" is a counting and filtering question: match a segment, a date range, and a status, then produce a number. A vector database has no native concept of "last quarter" or "count," because dates and counts are not what embeddings encode. Retrieval might surface relevant-looking documents, but nothing guarantees the number is correct or complete.

Multi-hop questions compound the problem. "Which suppliers tied to accounts with overdue invoices also have quality complaints in the last 90 days" spans several systems and exact conditions chained together. Similarity search was never built to chain exact filters across systems. This is covered further in knowledge graph vs RAG: RAG layers on similarity search and inherits its weakness at exact, multi-hop reasoning.

Where a graph wins

A graph is built for the cases similarity search struggles with. Numeric aggregation, filtering by date or status, and multi-hop traversal are native graph operations, because entities and relationships already exist as explicit structure. "How many enterprise accounts churned last quarter" becomes a filter on entity type, a filter on churn date, and a count.

Multi-hop questions are where the gap is largest. A question touching CRM accounts, ERP invoices, and support tickets requires walking pre-established edges, resolved once when the systems were connected. In a similarity-search setup, no equivalent structure exists to walk, and each added hop increases uncertainty about completeness, whereas a graph traversal either finds the connected records or does not.

Where a graph is the wrong tool

The graph's precision is also its limit. It does not natively understand unstructured writing. A ticket node can hold its status and timestamp, but finding tickets describing a similar problem in different words needs text search layered in.

Building the graph has real upfront cost too. Someone must define what counts as an entity, resolve records across systems describing the same thing under different names, and maintain that resolution as source systems change. A vector database can start returning useful results as soon as documents are embedded, with far less modeling work.

Hybrid architectures

In practice, more capable systems increasingly use both. A common pattern uses the graph to answer the exact, structured part of a question, such as which accounts meet a set of criteria, then uses vector search over unstructured content tied to those specific entities, such as the tickets or contracts linked to the accounts the graph identified. The graph narrows the problem to a precise set of records, and similarity search finds relevant meaning within that scoped set, rather than searching the entire corpus and hoping the ranking surfaces the right thing.

This division plays to each tool's actual strength: exact filtering and traversal from the graph, semantic recall from the vector store, applied to a scope the graph has already narrowed. Systems that only implement one half tend to be honest about it, which is worth checking before assuming a product handles both kinds of question well.

Traceability and auditability

The two approaches differ sharply in how easy it is to check an answer afterward. A graph traversal produces a specific path: this account, connected to this invoice, connected to this payment record, each an identifiable node with a source and a timestamp. Anyone reviewing the answer can follow that path back to the underlying records and confirm it independently, which matters for auditability in finance, compliance, and operations contexts where a wrong answer has a real cost.

A similarity-search answer is harder to audit the same way, because the connection between retrieved passages and the final answer runs through a ranking score and, often, a language model's synthesis of that text. There is no equivalent of "this is the exact edge that produced this number." That does not make similarity search untrustworthy for its intended use, finding relevant text, but it is the wrong foundation for answers that need to be checked line by line against source records.

What this means for choosing a tool

The filter is the shape of the question. If most questions look like "find documents about X," a vector database over that content is the right foundation. If most questions look like "how many," "which accounts," or "trace this back to," spanning more than one system, a knowledge graph is right, because those are structural questions with exact answers, not recall problems. Teams needing both should expect a hybrid setup rather than forcing every question through one tool.

Comparison

Aspect	Vector database	Knowledge graph
What it stores	Embeddings of text, images, or other content	Entities and typed relationships between them
Best question type	"Find text similar to this" over documents	"How many," "which," "trace this across systems"
Numeric accuracy	Weak, no native concept of counting or aggregation	Strong, filters and aggregates on exact fields
Multi-hop reasoning	Weak, no structure to chain exact conditions across systems	Strong, traverses pre-established edges across systems
Traceability of an answer	Limited, depends on ranking and synthesis, hard to audit line by line	High, each traversal step maps to a specific node and edge
Handling of unstructured documents	Strong, built for semantic recall over free text	Weak on its own, needs text search layered in
Setup and maintenance	Lower upfront cost, embed and store	Higher upfront cost, entity resolution required and maintained

Where SIGNLD fits

SIGNLD builds and maintains a private knowledge graph across the systems a company already runs, connecting read-only to 800 plus integrations, including spreadsheets, and resolving entities across them so cross-system questions can be traversed rather than re-modeled each time. It works without a data warehouse, and a company can typically connect its first system in around 15 minutes and get a first answer in minutes.

Every answer comes back as a Decision Brief: the finding, evidence links back to the exact source records involved, a confidence score, and a recommended action, which is the traceability a graph structure makes possible and a similarity-ranked answer generally cannot match. Where a question touches unstructured content, such as notes attached to a record, that content is read in context rather than searched blind across an entire corpus. Any AI inference SIGNLD performs runs on a private LLM powered by AWS Bedrock, on a single-tenant AWS Bedrock instance, and is never trained on your data. More detail on the graph model is on how it works, and definitions of the underlying terms are on the concepts glossary.

Key takeaways

A vector database stores embeddings: numeric representations of text, images, or audio, positioned so semantically similar items sit close together in a high-dimensional space.
Vector search is the right tool when a question is really about unstructured content and the goal is finding relevant material, not computing an exact number.
In practice, more capable systems increasingly use both.
The filter is the shape of the question.
Business decisions carry real cost if wrong, and compliance or leadership teams often need to verify a number against underlying records.

FAQ

Is a vector database the same thing as RAG?

No. A vector database is the storage and retrieval mechanism. RAG, retrieval-augmented generation, is the broader pattern of retrieving relevant text and passing it to a language model to generate an answer. Most RAG systems use a vector database for retrieval, but the two terms are not interchangeable. More on how this compares to a graph is in knowledge graph vs RAG.

Can a knowledge graph search unstructured text at all?

On its own, a basic graph traversal does not understand the meaning of free text the way similarity search does. Practical systems that need both usually attach vector search to specific graph nodes, such as documents linked to an account, so text search happens within a scope the graph has already narrowed rather than across everything.

Which one should a company build first?

It depends on the question mix. A company mostly asking questions about existing documents and policies should start with vector search over that content. A company mostly asking questions that span multiple business systems and need exact numbers should start with a knowledge graph, since that is the gap a vector database does not close.

Does a knowledge graph replace a vector database entirely?

Not necessarily. They solve different problems. A company can maintain a knowledge graph for structured, cross-system, exact questions and a vector store for unstructured document search, using each where it is actually strong rather than forcing one tool to do both jobs.

Why does exact traceability matter for business answers specifically?

Business decisions carry real cost if wrong, and compliance or leadership teams often need to verify a number against underlying records. A graph traversal produces a checkable path back to source data. A similarity-ranked answer is harder to verify the same way.

If your team keeps hitting the limits of similarity search on questions about counting, filtering, or tracing across systems, a graph-based approach is worth testing on your own data. Try SIGNLD free.

Knowledge graph vs vector database for business questions

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