Knowledge Graph vs Vector Database
Both help AI systems work with knowledge. But they work fundamentally differently — and which one you need depends on what you're trying to build.
Two approaches, different jobs.
A vector database stores embeddings— mathematical representations of meaning. Give it a piece of text, it converts it into a vector, and when you query it, it finds the most similar vectors. It's a similarity engine. Great for search. Great for recommendation.
A knowledge graph stores relationships— typed connections between entities. It doesn't measure similarity. It maps structure. Give it a query about what depends on what, and it returns the exact paths — not the closest matches.
One finds what's near. The other finds what connects.
Side by side.
| Dimension | Vector Database | Knowledge Graph |
|---|---|---|
| Core operation | Similarity search | Relationship traversal |
| Data model | High-dimensional vectors | Nodes + typed edges |
| Query type | "Find similar to X" | "Find how X relates to Y" |
| Deterministic? | No (same query, different results) | Yes (same query, same result) |
| Handles contradictions? | No (blends everything) | Yes (preserves both sides) |
| Best for | Search, recommendations, clustering | Reasoning, dependency mapping, ontology |
| Explainability | Black box (embeddings) | Auditable (every edge has source) |
When to use which.
Use a vector database when:
- • You need semantic search over unstructured text
- • Approximate results are acceptable
- • You're building recommendations or clustering
- • Speed matters more than precision
Use a knowledge graph when:
- • You need precise, deterministic answers
- • Relationships between things matter more than similarity
- • You need to surface contradictions across sources
- • You need traceability — every answer must cite its source
- • You're building AI context that needs to be reliable
The short version.
Vector databases and knowledge graphs are complementary, not competitive. A vector DB tells you what's similar. A knowledge graph tells you what's connected. Most serious AI systems need both.
The gap right now is that vector databases are easy to set up (just embed and go) while knowledge graphs require manual design — unless you use SILKLEARN, which builds the graph from your existing documents automatically.
Read the full blog postGet both — without the manual work.
SILKLEARN builds knowledge graphs from your existing documents. No ontology design. No manual structuring. Drop in your sources, get a graph you can query.