Knowledge Graphs vs Vector Databases Compared

What Each Does Well

Vector Databases

Vector databases excel at semantic similarity. They find content that discusses the same topic as the query, even when the exact words differ. "How do I reset my password" and "guide to changing user credentials" have different vocabulary but similar meaning, and a vector database finds the connection because their embeddings are close in vector space. This makes vector databases the default choice for RAG, search, and recommendation systems where "find something topically similar" is the primary access pattern.

Vector databases also handle unstructured text naturally. You embed the text, store the embedding, and query by embedding similarity. No entity extraction, no relationship modeling, no schema design. This simplicity is a major advantage for getting a retrieval system running quickly. From raw text to searchable index can take a few hours of engineering work.

Knowledge Graphs

Knowledge graphs excel at relationship reasoning. They answer questions about how entities connect to each other, what depends on what, who is responsible for what, and what chain of relationships leads from one concept to another. These are questions that vector similarity cannot answer because the answer requires traversing explicit connections rather than finding semantically similar text.

Knowledge graphs also provide explainable retrieval. When a graph returns a result, you can trace the exact path through entities and relationships that led to it: "We found this document because it mentions PostgreSQL, which is used by the order service, which you asked about." Vector databases return a similarity score, which tells you how close the embeddings are but not why the content is relevant. For applications where users need to understand and trust retrieval results, explainability matters.

Where Each Fails

Vector Database Failures

Vector databases fail on multi-hop reasoning. If the answer requires following a chain of relationships (X uses Y which depends on Z), vector search only finds documents similar to the question about X. It cannot follow the chain to Z because there is no vocabulary overlap between the question and the Z documentation.

Vector databases fail on exact match queries. "What version of Node.js does the API gateway use" requires finding a specific fact, not a topically similar document. Embedding similarity is noisy for exact matches because subtle differences in phrasing can produce different similarity rankings. The correct answer (a configuration file containing "node: 18.2.0") might rank below a blog post about Node.js versioning strategies that is more semantically rich.

Vector databases fail on negation and absence. "Which services do NOT use Redis" requires knowing which services exist, which ones use Redis, and computing the complement. Vector similarity has no mechanism for negation because it measures presence of meaning, not absence.

Knowledge Graph Failures

Knowledge graphs fail on open-ended topical queries. "Tell me about best practices for API design" does not reference a specific entity, so there is no starting point for graph traversal. The answer is scattered across many documents that discuss API design from different angles. Vector search handles this naturally by finding all semantically relevant content.

Knowledge graphs fail when the relevant relationship has not been extracted. If the connection between two entities was not identified during graph construction (because the extraction prompt missed it, or because the relationship is implied rather than stated), the graph cannot traverse it. Vector search is more forgiving of incomplete data because it operates on the raw text rather than on extracted structures.

Knowledge graphs require more infrastructure and maintenance. Entity extraction, graph storage, relationship maintenance, and staleness management add engineering complexity that vector databases do not require. This is not a retrieval quality failure but a practical constraint that affects adoption.

Side-by-Side Comparison

Why the Best Systems Use Both

The most effective retrieval systems combine vector databases and knowledge graphs because their strengths are complementary and their failure modes are different. Vector search provides broad recall by finding topically relevant content. Graph traversal provides precise connectivity by following entity relationships. A query that mentions a known entity benefits from both: the vector path finds related discussions, and the graph path finds structurally connected information that the vector path misses.

The combination is especially powerful because the two methods rarely fail on the same queries. When vector search fails (multi-hop, exact match), graph traversal typically succeeds. When graph traversal fails (missing entities, open-ended topics), vector search typically succeeds. By combining both, the system's failure rate is the product of both failure rates rather than either one alone.

Adaptive Recall combines both approaches in its cognitive scoring model. Every recall operation runs vector similarity search and spreading activation through the entity graph simultaneously. The results are merged using a scoring formula that weights semantic similarity, graph connectivity, base-level activation (recency and frequency), and confidence. This gives you the complementary benefits of both retrieval methods through a single API call.