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Amazon Neptune

Amazon Neptune is a managed graph database for property-graph and RDF workloads using openCypher, Gremlin, SPARQL, and supported graph analytics or machine-learning integrations.

Explore pricing models, common use cases, infrastructure support, and the AWS services that commonly work with Amazon Neptune.

Amazon Neptune pricing and cost programs

Pricing model: Managed graph database usage

On-Demand
Available
Reserved Instances or reserved capacity
Available
Savings Plans
Service-specific
Spot
Not applicable

Billing dimensions: Instance or serverless capacity · Storage · I/O · Analytics capacity

Programs and modes: On-Demand Instances · Reserved Instances · Neptune Serverless · Neptune Analytics

Purchase models vary between provisioned database instances, Serverless, and Analytics.

Free Tier: Service-specific — verify current offers

Pricing reviewed 2026-07-25. Reviewed against the linked official AWS pricing page. Recheck regional rates and program terms before purchase.

Official AWS pricing

Official AWS sources reviewed 2026-07-21.

Why implement Amazon Neptune?

  • Stores and traverses highly connected data directly, avoiding complex relational joins or application-managed adjacency structures.
  • Supports common graph models and query languages on managed cluster storage with replicas, backups, monitoring, and failover.
  • Enables relationship-centric workloads such as fraud rings, knowledge graphs, recommendations, dependency maps, and identity graphs.

How to implement Amazon Neptune

  1. Choose a property-graph or RDF model and query language from the application's traversal and interoperability requirements, then model identifiers, labels, edges, and indexes before loading at scale.
  2. Create the cluster, subnet and parameter groups, security controls, encryption, writer, and reader instances in a VPC with required application connectivity.
  3. Load large datasets through Neptune's S3 bulk loader, connect with current drivers and retry behavior, and validate query results, plans, latency, backups, and failover.

Amazon Neptune best practices

  • Profile representative traversals, constrain starting points early, use bound parameters, limit returned paths, and avoid unbounded or unexpectedly high-fan-out queries.
  • Run production readers in multiple Availability Zones, use cluster endpoints appropriately, refresh DNS or connections after topology changes, and test failover.
  • Use supported instance families consistently where AWS recommends it, keep transactions short, encrypt connections, restrict access, and monitor memory, CPU, storage, query, and replica metrics.

Amazon Neptune use cases and server impact

  • Fraud and identity relationship detection
  • Knowledge graphs and entity resolution
  • Recommendation and network dependency analysis

Replaces self-managed graph database clusters and their replication or backup operations, but graph modeling and traversal performance remain application-level design work.

Official implementation resources

Commonly paired AWS services