AWS Neptune

Serverless graph database for connected data, built for applications that need to model and query relationships at scale. Supports both property graph and RDF paradigms, with Gremlin, openCypher, and SPARQL as query languages. Delivers fast traversal across billions of vertices and edges, with built-in graph algorithms and vector search for generative AI workloads.

Amazon Neptune differentiates from self-managed graph databases by offering fully managed operations, automatic capacity scaling, and native integration with the broader AWS analytics stack. GraphRAG integration with knowledge bases improves accuracy and explainability for generative AI applications. Commonly used for customer 360, fraud detection, recommendation engines, and knowledge graphs across enterprises.

Key features:

• Property graph and RDF support with Gremlin, openCypher, and SPARQL
• GraphRAG and vector search for generative AI applications
• Built-in graph algorithms, up to 128 TiB per cluster, 15 read replicas
• ACID transactions, automated backups, encryption at rest and in transit
• Neptune ML for graph-based predictions, LangChain and LlamaIndex integration

Enhances AI applications through GraphRAG and knowledge graphs that improve retrieval accuracy. Customer 360 and identity graphs power personalization and marketing. Fraud detection models relationships between people, places, and transactions for pattern discovery. Cybersecurity teams model IT assets and relationships for investigation. Developers connect via standard drivers, bulk load from S3, and integrate with streaming and ETL pipelines.