TL;DR - Amazon Neptune
- Amazon Neptune is a fully managed graph database service for building applications with connected data
- It supports both property graph and RDF models with Apache TinkerPop and SPARQL query languages
- Pricing based on instance size starting around $0.10/hour for the smallest instance
Pricing: Paid only
Best for: Enterprises & pros
4.4/5 across review platforms
Pros & Cons
Pros
- Managed graph database
- AWS integrated
- High availability
- SPARQL and Gremlin
- Good for knowledge graphs
Cons
- AWS lock-in
- Expensive
- Learning curve
- Complex pricing
- Limited features vs Neo4j
Ratings Across the Web
4.4(30 reviews)
Ratings aggregated from independent review platforms. Learn more
Key Features
Graph databaseManagedGremlinSPARQLServerless optionAWS
Pricing Plans
Free TrialMost Popular
On-Demand
$0.1/per db.r5.large hour
Pay-as-you-go
- Graph database
- Auto-scaling
- High availability
- All features
Serverless
Usage-based
Auto-scaling
- NCU-based pricing
- Scale to zero
- No capacity planning
About Amazon Neptune
By Toolradar Team·
Amazon Neptune is a graph database for connected data. When relationships between entities matter as much as the entities themselves—social networks, fraud detection, recommendations—Neptune handles queries that would crush relational databases.
Both property graph and RDF models are supported, so you can choose the approach that fits your data. Fully managed on AWS means no database administration headaches.
Organizations modeling complex relationships find Neptune makes queries natural that would require painful SQL joins. The graph model matches how you actually think about connected data.
Reviews
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Amazon Neptune FAQ
How does Amazon Neptune enhance generative AI applications?
Neptune improves the accuracy and explainability of generative AI applications by modeling data as a graph, which captures context. It offers fully managed GraphRAG with Amazon Bedrock Knowledge Bases and integrates with Strands AI Agents SDK and popular agentic memory tools to simplify AI application development.
What are the storage limitations and scaling capabilities of Amazon Neptune?
Neptune Database uses a distributed storage architecture that automatically expands as data volume increases, growing up to a maximum size of 128 TiB in supported AWS Regions. It supports unlimited vertices and edges, processes over 100,000 queries per second, and allows for up to 15 read replicas per cluster.
Which graph query languages are supported by Amazon Neptune Serverless?
Neptune Serverless supports the three most popular graph query languages: Apache TinkerPop Gremlin, openCypher, and SPARQL. This allows users to interact with their graph data using their preferred language.
How does Neptune Global Database provide disaster recovery and improve performance for globally distributed applications?
Neptune Global Database replicates data across multiple AWS Regions with minimal performance impact, enabling fast local reads with low latency in each Region. It provides disaster recovery by allowing a secondary Region to be promoted to take read/write responsibilities in case of a primary Region outage, with recovery typically under 1 minute.
What is the difference between Neptune Standard and Neptune I/O-Optimized cluster configurations?
Neptune Standard is designed for applications with typical data access patterns and low-to-moderate I/O usage, where you pay for database instances, storage, and pay-per-request I/O. Neptune I/O-Optimized offers predictable pricing for I/O-intensive applications by charging only for database instances and storage usage, with zero charges for read and write I/O operations, potentially saving up to 40% for workloads where I/O spend exceeds 25% of total database spend.
Can I pause a graph workload in Neptune Analytics and resume it later?
Yes, with Neptune Analytics, you can pause a graph workload when it's not in use and resume it later with a single action from the AWS Management Console, CLI, or API. When paused, you only pay 10% of the normal compute price, and all graph data and settings are fully preserved.
Source: aws.amazon.com