Amazon Neptune

Amazon Neptune is AWS’s fully managed graph database service, supporting both property-graph (Apache TinkerPop / Gremlin and openCypher) and RDF / SPARQL models. Neptune handles provisioning, patching, replication, and backup; users get a graph endpoint, AWS-native IAM authentication, and integration with the rest of the AWS data stack.

Key Features:

• Multi-Model. Same cluster can be used as a property-graph (Gremlin / openCypher) or an RDF triple store (SPARQL) — choose at query time.
• Managed Operations. Multi-AZ, automatic failover, point-in-time restore. Storage auto-scales up to 128 TiB per cluster.
• Read Replicas. Up to 15 low-latency read replicas; reads scale horizontally.
• Bulk Loader. Native CSV / RDF bulk import from S3 — the standard way to ingest hundreds of millions of edges.
• Neptune Notebooks. Hosted Jupyter for interactive graph exploration with prebuilt visualizations.
• Neptune ML. GNN training (deepGraphLib) and inference on stored graphs — node classification, link prediction, graph embeddings.
• Neptune Analytics. Newer in-memory analytical mode for whole-graph algorithms (PageRank, shortest paths, community detection) at much higher speed than the transactional engine.

Neptune vs. Neo4j:

• Neptune. Managed, multi-model (property + RDF), AWS-integrated. No self-hosted operations. Slightly behind Neo4j on Cypher feature richness.
• Neo4j. Original property-graph engine, deepest Cypher support, broader graph-algorithm library. Self-hosted or via Neo4j Aura (managed).

Use Cases:

• Identity / fraud-detection graphs — suspicious edges across accounts, devices, transactions.
• Knowledge graphs powering recommendation and semantic search.
• Network and IT-asset graphs for impact analysis (“what depends on this server?”).
• Bioinformatics and life-sciences ontologies (RDF / SPARQL via Neptune).
• Supply-chain and logistics graphs.