Setting the file. One moment.
Subchapter 67.7
references/decision-guide.mdMarkdown6 KBView on GitHub
Use this sub-skill when a developer is choosing a database, when an agent needs to recommend Neptune vs. another AWS database, or when you need to determine whether a user’s problem is a graph problem even if they haven’t described it that way.
Most people who need a graph database don’t know they need one. They describe problems. If the user describes any of these, they likely have a graph problem:
| What the user describes | Why it’s a graph problem | Pattern |
|---|---|---|
| Data about the same real-world entity exists in multiple systems | Resolving identity requires traversing shared identifiers across sources | Customer 360 / identity resolution |
| They need to trace paths or chains (A→B→C→D) | Path traversal is a native graph operation; SQL JOINs degrade exponentially at 3+ hops | Fraud detection, supply chain |
| They need to understand what’s affected by a change | Impact propagation requires recursive downstream traversal | Service dependencies, supply chain |
| They need to find clusters or communities in data | Community detection algorithms operate on graph structure | Fraud rings, social networks |
| Their AI agent needs structured context beyond vector similarity | Graph provides relationship-aware retrieval that vectors miss | GraphRAG |
| Their agent needs to remember relationships across sessions | Entity-relationship memory is a graph; flat logs lose structure | Agentic memory |
| They need to explain WHY an AI made a decision | Knowledge graphs provide traceable reasoning paths | Semantic layer / ontology |
| They need to enforce business rules in AI workflows | Rules encoded as graph relationships can be traversed and validated | Semantic layer / ontology |
| They want recommendations based on what similar users did | Collaborative filtering through shared purchase/behavior edges | Recommendations |
| They need to trace who has access to what and why | Permission inheritance through nested groups/roles is a graph traversal | Access control |
If none of these apply, Neptune is probably not the right tool. See “When NOT to Use Neptune” in SKILL.md.
Does your application query relationships as a first-class concern?
If traversing connections between entities (friends-of-friends, transaction chains, entity hierarchies, network paths) is central to your query patterns, Neptune is the right choice. If relationships are incidental, use a simpler store.
| Signal | Recommended service |
|---|---|
| Multi-hop traversals (2 hops) are common | Neptune |
| Schema changes frequently; relationships are the data | Neptune |
| Fraud ring detection, shared-identity analysis | Neptune |
| Social graph: followers, recommendations, communities | Neptune |
| Knowledge graph, ontology, linked data (RDF) | Neptune |
| Primary pattern is key-value or single-item lookup | DynamoDB |
| Relational data, complex SQL joins, ACID transactions | Aurora PostgreSQL |
| Document-centric, flexible JSON schema | DocumentDB |
| Time-series telemetry | Timestream |
| Full-text search is primary access pattern | OpenSearch |
| Graph analytics in batch (PageRank, community detection) | Neptune Analytics |
1 hop: "Get all orders for customer X" → DynamoDB or Aurora is fine
2 hops: "Get all products bought by X's friends" → Neptune starts to win
3+ hops: "Find fraud rings connected to account X" → Neptune is the right toolChoose property graph (Gremlin or openCypher) when:
Choose RDF / SPARQL when:
| Factor | Neptune Database | Neptune Analytics |
|---|---|---|
| Workload type | Transactional (OLTP) | Analytical (OLAP) + vector search |
| Individual query latency | Milliseconds | Milliseconds (on loaded graph) |
| Full-graph algorithm latency | N/A (not designed for this) | Seconds to minutes (PageRank, community detection) |
| Data size | TB-scale persistent | GB-scale in-memory |
| Algorithms | Traversal queries | PageRank, community detection, shortest path at scale |
| Vector search | Not built-in | Built-in (embeddings stored on vertices) |
| Persistence | Durable, multi-AZ | Ephemeral (load from S3 or Neptune DB) |
| Cost model | Instance + storage | NCU hours while running |
| Use for GenAI | Persistent knowledge graphs | GraphRAG, agentic memory (graph + vector) |
Common pattern: Use Neptune Database as the live operational store, Neptune Analytics for periodic batch analysis (e.g., nightly fraud score computation), GraphRAG retrieval, or agentic memory with vector search.
Both query property graphs in Neptune. Choose based on team familiarity.
⚠️ Neptune’s openCypher support does not cover 100% of Neo4j Cypher. Before porting queries, check the Neptune openCypher compatibility docs (opens in a new tab).
analytics-vs-database (deep dive), data-modeling (next step)