# Graph Database vs Vector Database

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

## Quick Verdict

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

| Capability               | Graph Database                          | Vector Database                                |
|-------------------------|-----------------------------------------|------------------------------------------------|
| 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. |

### Graph Database Strengths

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

### Vector Database Strengths

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