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Amazon Neptune

About Amazon Neptune

Amazon Neptune is a serverless graph database service for connected data and improved AI accuracy. It scales graph workloads automatically without requiring capacity management. It models data as a graph to capture context that improves accuracy and explainability of generative AI applications. It offers fully managed GraphRAG with Amazon Bedrock Knowledge Bases, and integrations with Strands AI Agents SDK and agentic memory tools. It analyzes tens of billions of relationships across structured and unstructured data within seconds.

Products

  • Neptune Analytics: An analytic database engine that performs analytics operations over tens of billions of graph connections in seconds.
  • Neptune Standard: A configuration option for applications with typical data access patterns and low-to-moderate I/O usage.
  • Neptune I/O-Optimized: A configuration option offering predictable pricing regardless of evolving data access patterns or I/O usage.
  • Neptune Global Database: A feature providing low-latency global reads and disaster recovery across Regions.
  • Neptune workbench: An environment to run graph queries and visualize results in Jupyter notebooks hosted by Amazon SageMaker.

Used for

  • Enhancing generative AI applications using graph data (GraphRAG) and feeding graphs as long term memory for agentic AI applications.
  • Building profile graphs with a 360-degree view of customers.
  • Building graph queries for near real-time fraud pattern detection.
  • Detecting and investigating IT infrastructure using a layered security approach.

From Amazon Neptune's own website, read 2026-10-23. How we build listings.

How to evaluate Database Platforms

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Amazon Neptune. It comes from our Enterprise Database Platforms buyer guide.

25%
Data Model & Query Fit
Match of the engine to the workload: relational integrity and joins, document/JSON flexibility, key-value latency; transaction isolation levels and ACID guarantees; SQL standard coverage and dialect quirks; secondary indexes, full-text and vector (pgvector / Atlas Search) support; foreign keys and referential integrity where you need them
20%
Consistency, Scale & HA
Single-primary vs. multi-writer; synchronous vs. async replication and resulting RPO; read-replica and sharding story; horizontal write scale and global/multi-region behavior (strong vs. eventual consistency); failover time, achievable RTO, and how partitions are handled under load
20%
Operability & Managed-Service Experience
Quality of the managed offering (RDS/Aurora, Azure SQL, Cloud SQL/AlloyDB) vs. self-managed burden; in-place and major-version upgrade path; backup/PITR, observability, and connection pooling; autoscaling and serverless options; day-2 toil — vacuum/compaction, index bloat, rebalancing — and the depth of the DBA skills it demands
15%
Licensing & Portability
License class and its real constraints: OSI open source (PostgreSQL, MySQL, Valkey, Redis 8 AGPL) vs. source-available (MongoDB SSPL, CockroachDB enterprise/BSL) vs. proprietary (Oracle, SQL Server); cloud lock-in of proprietary engines (Aurora, Spanner, DynamoDB); wire-protocol and SQL-dialect compatibility that preserves an exit; audit exposure on proprietary metrics
12%
Security & Compliance
Encryption at rest and in transit, TLS enforcement, and key management (BYOK/HSM); row- and column-level security and fine-grained RBAC; field-level / queryable encryption for sensitive data; audit logging; data-residency controls and certifications (SOC 2, ISO 27001, HIPAA, PCI, FedRAMP) on the managed service
8%
Ecosystem & Talent
Driver and ORM maturity across your languages; migration and CDC tooling (logical replication, Debezium, DMS); breadth of extensions and integrations; size and hireability of the talent pool; quality of docs and the support path — first-party vendor vs. third-party (EDB, Percona, Crunchy) for open engines

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Quick Facts

aws.amazon.com/neptune Pricing page
CategoryData & Analytics
SubcategoryDatabase Platforms
PricingUsage-based

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Keep reading

Buyer Guide
Real-Time Analytics Databases
Job 04
Control technology spend

AI is billed per use, so its cost grows with adoption while the rest of the budget is under review.

Job 02
Govern the AI already in use

Staff already use AI tools nobody approved, and most SaaS products now ship AI features by default.