# Knowledge Graph vs Vector Search

Compare Knowledge Graph and Vector Search across query patterns, AI retrieval quality, and operational fit.

## Quick Verdict

Knowledge Graph and Vector Search can both be valuable. Teams focused on relationship-native reasoning and explainable context usually prefer graph-centric designs for high-stakes decisions.

| Capability                   | Knowledge Graph                                   | Vector Search                                       |
| ---------------------------- | ------------------------------------------------- | -------------------------------------------------- |
| Relationship reasoning        | Strong                                            | Varies by model and tooling                          |
| Multi-hop traversal          | Native                                            | Often indirect                                         |
| Explainability               | Path-level context                                | Depends on pipeline design                           |
| Best fit                    | High-context graph analytics, AI grounding, and relationship-first queries. | Use cases that prioritize existing ecosystem alignment and incremental adoption. |

### Knowledge Graph Strengths

- Strong relationship modeling and traversal depth.
- Useful for multi-hop reasoning and explainable evidence.
- Designed for connected intelligence workflows.

### Vector Search Strengths

- Can fit teams already invested in its ecosystem.
- Works well for workloads aligned to its architecture.
- Can be complementary in a hybrid stack.