Managing data effectively has become a growing challenge as the amount of information businesses deal with expands at an incredible pace. To make sense of it all, organizations turn to databases that don’t just store data but also uncover meaningful connections within it. That’s where graph databases stand out—they reveal relationships and insights traditional databases often overlook.

AWS Neptune, Amazon's fully managed graph database, is one of the most well-known options in this space. While it’s packed with features and widely used, it’s not the right fit for everyone. Some organizations need specialized capabilities, prefer open-source tools, want the flexibility of on-premise deployments, or aim to avoid vendor lock-in. Others face unique workload demands or budget constraints that lead them to explore alternatives.

In this post, we’ll break down the basics of AWS Neptune, why some businesses seek other options, and introduce seven excellent graph database alternatives to help you find the right match for your needs.

What is AWS Neptune? 

AWS Neptune is a fully managed graph database service provided by Amazon Web Services. It is designed to work with highly connected datasets and support popular graph models like property graphs and RDF (Resource Description Framework). With Neptune, developers and data engineers can build and run applications that need to navigate billions of relationships in real-time, offering faster query responses and more efficient data handling compared to traditional relational databases.

Fully managed service

Neptune simplifies database management by automating hardware provisioning, software patching, and setup. By eliminating these operational complexities, AWS enables developers to focus on application development rather than database administration. Additionally, the service offers automated backups, database snapshots, and point-in-time recovery, ensuring reliability and reducing administrative effort.

High performance and scalability

Neptune delivers robust performance through its purpose-built graph database engine, capable of processing over 100,000 graph queries per second. Its infrastructure supports automatic storage scaling up to 128 TiB, ensuring seamless data growth without performance degradation. With up to 15 read replicas across three Availability Zones, Neptune ensures high availability and minimal replica lag for read operations. The serverless option dynamically adjusts capacity based on workload, offering significant cost savings, potentially reducing costs by up to 90%.

Multi-model support

Neptune caters to diverse data modeling requirements, supporting both the Property Graph model and the RDF model. Developers can use Apache TinkerPop Gremlin and openCypher query languages for property graphs or SPARQL for RDF data. Its purpose-built storage optimizes both models for efficient navigation and querying, making it ideal for applications that need to handle highly connected datasets.

Integration with AWS ecosystem

As part of the broader AWS ecosystem, Neptune integrates with various services to provide a comprehensive solution. Developers can leverage Amazon S3 for data integration through the bulk loader, manage security using AWS IAM and KMS, and monitor performance with Amazon CloudWatch. Neptune supports event-driven workflows via AWS Lambda and advanced data extraction using tools like Amazon Comprehend, Rekognition, and Textract. Network isolation is ensured through Amazon VPC, enabling secure and scalable graph database operations for diverse business needs.

How does AWS Neptune work?  

AWS Neptune is designed to efficiently store and process relationships between data entities. Its architecture and operational model focus on delivering consistent performance, easy integration, and high availability.

Core components of Neptune’s architecture:

1. Cluster model: Neptune uses a cluster-based architecture. A cluster consists of a primary instance (which handles all write operations) and zero or more read replicas. Write requests go to the primary, while read requests can be load-balanced across multiple replicas to enhance scalability and reduce latency for read-heavy workloads.
2. Storage layer: Neptune’s storage is distributed, fault-tolerant, and self-healing. It uses a log-structured storage engine that continuously backs up data to Amazon S3, ensuring durability and rapid recovery.
3. Querying and indexing: Neptune supports Gremlin (a graph traversal language) and SPARQL (a semantic query language) to interact with your graph data. The service automatically indexes edges and vertices, allowing for efficient query execution and retrieval of complex relationship patterns.
4. Security and compliance: Neptune integrates with Amazon Identity and Access Management (IAM) for authentication and authorization. It supports encryption at rest and in transit, ensuring data security. It also complies with industry-standard certifications to meet enterprise governance requirements.

Operational flow:

• Data modeling: You start by modeling your domain as nodes (vertices) and edges, capturing entities and their relationships.
• Data loading: You load your data into Neptune, often from CSV files stored in Amazon S3 or by using its bulk load feature.
• Query execution: Developers and analysts run queries using Gremlin or SPARQL to explore relationships, derive insights, or power graph-driven application features.
• Maintenance and scaling: AWS manages instance sizes, failover, patching, and continuous backups. Horizontal scaling for reads is as easy as adding replicas.

Though Neptune simplifies the complexity of deploying and managing graph databases, certain constraints like being tightly coupled with the AWS ecosystem may not suit all organizational requirements.

Why do you need AWS Neptune alternatives?

While AWS Neptune is an excellent choice in the graph database arena, there are multiple reasons why businesses, developers, and data practitioners might look for alternatives. Every environment, budget, and use case is different, and understanding these factors will help you choose a solution that better fits your organization’s long-term goals.

Avoiding vendor lock-in

Relying heavily on a proprietary AWS service may create dependency on one provider’s pricing, features, and roadmap. Companies looking to diversify their infrastructure or maintain the freedom to switch cloud providers easily may prefer a solution that’s more cloud-agnostic or open-source.

On-premise or hybrid deployment needs‍

AWS Neptune is a managed cloud service. Some organizations require on-premise deployments due to regulatory, compliance, or data residency needs. They may also need hybrid setups that span multiple environments and require a graph database solution that can be run anywhere.

Customization and extensibility‍

While Neptune supports both property graphs and RDF, some specialized graph use cases may necessitate deeper customization. Organizations might need specific query languages, unique indexing strategies, or plugins that Neptune doesn’t offer.

Cost considerations‍

For some workloads, cost optimizations matter more than convenience. AWS Neptune’s pricing model (pay-as-you-go for instances, storage, and I/O) might not always be the most economical. Alternatives offering different pricing tiers, community editions, or usage patterns may result in lower total cost of ownership.

Community support and ecosystem‍

An active open-source community and a broad ecosystem of tools and libraries can accelerate innovation and problem-solving. Some alternatives have thriving user communities, extensive documentation, and a larger ecosystem of complementary tools.

In essence, while AWS Neptune is a powerful and easy-to-use service, it isn’t the one-size-fits-all solution. Your unique business context, technical requirements, and strategic vision can guide you towards an alternative that delivers better flexibility, control, or cost efficiencies.

Top 7 best AWS Neptune alternatives

PuppyGraph

PuppyGraph is the first and only graph query engine that allows users to query relational data as a graph without complex ETL processes, or a separate graph database. It integrates seamlessly with popular relational data lakes and warehouses, enabling high-performance analytics and simplifying data storage. Unlike Neptune, which primarily operates as a managed AWS service, PuppyGraph offers flexible deployment options, including forever free Docker containers, AWS AMI and GCP Marketplace, supporting both self-hosted and cloud environments. With no need for ETL pipelines or data migration, PuppyGraph can be up and running in just 10 minutes—delivering insights far faster than Neptune’s more complex setup process.

PuppyGraph shines in its integration capabilities, enabling direct querying of existing data sources such as Apache Iceberg, Delta Lake, Databricks, Snowflake, AWS Redshift, BigQuery, and SQL databases. This zero-ETL approach eliminates the need for cumbersome data migration, allowing users to perform analytics on their existing infrastructure. Neptune, while optimized for native graph storage, lacks this direct integration capability, focusing instead on purpose-built storage for graph operations.

Performance-wise, PuppyGraph supports petabyte-scale data and auto-sharding, making it ideal for large-scale analytics. It delivers impressive query speeds, handling 10-hop queries in just 2.26 seconds. In comparison, Neptune offers sub-100ms replica lag and up to 128 TiB of storage, excelling in high-availability scenarios with support for up to 15 read replicas.

Cost is another area where PuppyGraph stands out, offering a forever-free Developer Edition and a 30-day trial for its Enterprise Edition, with no specialized storage costs. Neptune follows a usage-based pricing model that includes instance hours, storage, and data transfer fees, making it potentially more expensive for large-scale operations.

PuppyGraph’s strengths lie in real-time querying, complex traversal capabilities, and its ability to integrate seamlessly with enterprise data lakes. For organizations seeking a powerful, cost-effective, and easy-to-deploy graph database, PuppyGraph provides a compelling alternative to AWS Neptune.

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Neo4j

Neo4j is an established graph database offering straightforward query capabilities, extensive scalability, and adaptable deployment across on-premise and cloud environments. Unlike Neptune, which is exclusively a managed service tied to AWS, Neo4j provides both self-hosted and fully managed deployment options. This flexibility enables organizations to choose between hosting Neo4j on-premises or deploying it on major cloud platforms such as AWS, Azure, and Google Cloud Platform. For teams seeking independence from the AWS ecosystem, Neo4j’s platform-agnostic capabilities can serve as a notable benefit.

From a data model perspective, Neo4j uses a native graph storage model configured for intricate relationship traversals, whereas Neptune supports both Property Graph and RDF models with query language options like Gremlin and SPARQL. Neo4j’s architecture performs effectively in handling complex query scenarios, as demonstrated by real-world use cases that report significantly faster search experiences and reduced data loading times.

In terms of scalability, Neo4j has demonstrated its capabilities with implementations managing hundreds of millions of nodes and relationships. Although Neptune supports up to 15 read replicas across three Availability Zones and can handle over 100,000 queries per second, Neo4j offers performance that remains competitive for demanding workloads. Neo4j’s greater efficiency in handling complex queries makes it a suitable choice for graph-intensive applications.

Cost-wise, Neo4j’s Community Edition is free, while its Enterprise Edition requires licensing. Organizations can adjust costs by self-hosting and managing infrastructure independently—a flexibility that AWS Neptune’s pay-as-you-go pricing and vendor lock-in do not provide.

Neo4j’s integration capabilities also stand out. Its adaptable architecture supports various environments, including microservices, allowing for relatively straightforward adoption in diverse use cases. With its combination of scalability, performance, and deployment options, Neo4j provides a viable alternative for organizations exploring graph database solutions beyond AWS Neptune.

Azure Cosmos DB

Azure Cosmos DB is a globally distributed, multi-model database that supports various data types and models, providing a variety of capabilities and straightforward integration with Microsoft's cloud ecosystem. Both AWS Neptune and Azure Cosmos DB are fully managed services, eliminating infrastructure management responsibilities. However, while Neptune is confined to the AWS ecosystem, Cosmos DB operates effectively within Azure, offering direct integration with a range of Azure services and support for multi-cloud architectures.

One of Cosmos DB's notable characteristics is its broad data model support. In addition to Gremlin for graph queries, Cosmos DB offers APIs for SQL, Cassandra, MongoDB, Table, and PostgreSQL, making it capable of adapting to diverse application needs. Neptune, on the other hand, focuses on Property Graph and RDF models, with support for Gremlin and SPARQL. Cosmos DB also allows for larger document sizes (up to 2MB), exceeding Neptune’s more restrictive limits.

When it comes to integration, Cosmos DB provides straightforward connectivity with Azure services and is configured for AI-powered applications and serverless computing. It also features multi-model API support, making it a flexible option for developers. Neptune performs effectively in its deep integration with AWS services like IAM, CloudWatch, and Lambda, making it suitable for organizations closely aligned with the AWS ecosystem.

Performance-wise, Cosmos DB is configured for distributed computing and offers dynamic autoscaling with single-digit millisecond latency. Its 99.999% multi-region availability SLA makes it a consistent option for critical applications. Meanwhile, Neptune focuses on graph-intensive workloads, performing effectively in managing billions of relationships with millisecond-level query performance.

In terms of use cases, Cosmos DB is suitable for AI assistants, IoT, and SaaS applications, while Neptune is tailored for recommendation engines, knowledge graphs, and network security applications. With its flexibility, integration features, and strong performance, Cosmos DB can be considered a reasonable option for those seeking an alternative to AWS Neptune.

ArangoDB

ArangoDB is a globally distributed, multi-model database that supports various data types and models, offering a variety of capabilities and straightforward integration with Microsoft’s cloud ecosystem. Unlike Neptune, which is exclusively available as a managed service within the AWS ecosystem, ArangoDB supports a wide range of deployment options. It can be self-hosted on-premises, deployed in self-managed cloud environments, or accessed through its fully managed ArangoGraph Insights Platform. This range of options makes ArangoDB suitable for diverse organizational requirements.

One of ArangoDB’s notable attributes is its support for multiple data models, including graph, document, and key-value, all accessible through its native AQL (ArangoDB Query Language). In comparison, Neptune focuses on graph models, supporting SPARQL, Gremlin, and OpenCypher for graph queries. ArangoDB’s ability to handle different data models within a single engine helps streamline data integration and management, making it generally adaptable to various use cases.

ArangoDB functions effectively with business intelligence tools. It provides native connectors for Tableau, Power BI, and Qlik, along with Grafana support for monitoring. Additionally, its Foxx framework enables the creation of custom API endpoints, improving application development capabilities. While Neptune integrates closely with AWS tools like Glue for ETL jobs and SageMaker for machine learning, ArangoDB’s platform-agnostic integrations offer more options for multi-cloud and hybrid environments.

Performance-wise, ArangoDB operates efficiently, using less disk space and achieving faster data loading speeds compared to competitors. Its query performance for complex scenarios supports its suitability for demanding applications. While Neptune provides robust performance for graph-specific tasks, ArangoDB’s multi-model capabilities make it a practical option for applications requiring multiple data representations.

TigerGraph

TigerGraph is an efficient graph database designed for real-time analytics and detailed examination of intricate data relationships using its native parallel graph architecture. Unlike Neptune, which is available only as a managed AWS service, TigerGraph offers various deployment options. These include self-hosted on-premises installations, self-managed cloud deployments on AWS, Google Cloud, and Azure, as well as TigerGraph Cloud, a fully managed service. This range of options makes TigerGraph a suitable choice for a variety of operational environments.

One of TigerGraph’s notable attributes lies in its scalability and processing capabilities. It has demonstrated the capacity to handle 36TB of raw data comprising 73 billion vertices and 534 billion edges. For deep-link OLAP queries, TigerGraph processes 9 billion vertices and 60 billion edges in under one minute. In comparison, Neptune focuses on steady analytical query performance and rapid loading of billions of triples, performing effectively in machine learning, knowledge graphs, and social networking applications.

TigerGraph uses its native query language, GSQL, and supports openCypher, providing capable tools for building complex queries. Neptune, on the other hand, supports Gremlin, SPARQL, and openCypher, making it flexible for different graph models.

Integration is another area where TigerGraph performs effectively. It supports major business intelligence tools like Tableau, Power BI, and Qlik, alongside Microsoft Entra ID for SSO. Its availability across multiple cloud providers broadens its applicability. Neptune’s integrations are closely aligned with the AWS ecosystem, offering straightforward connectivity with services like AWS IAM, SageMaker, and Glue.

Cost-wise, TigerGraph provides a free tier for entry-level use and scalable enterprise pricing based on ingestion volumes. Neptune’s costs are tied to database instance hours, storage, and data transfer, reflecting AWS’s pricing structure.

JanusGraph

JanusGraph is an open-source graph database designed for scalability and adaptability, supporting multiple storage backends and advanced indexing tools. Unlike Neptune, which is primarily available as a managed AWS service, JanusGraph supports various self-hosted setups. From minimalist single-server configurations to multi-server deployments, JanusGraph can be customized to suit specific operational needs. This flexibility allows users to maintain control over their infrastructure and adjust costs based on storage and index backend choices.

A notable aspect of JanusGraph is its support for multiple storage backends, including Apache Cassandra, HBase, and Amazon DynamoDB. This backend flexibility enables users to integrate JanusGraph with their existing data infrastructure. In contrast, Neptune uses a purpose-built distributed storage backend, which, while configured for graph operations, offers fewer configuration options.

JanusGraph performs effectively in complex graph traversals, using Gremlin as its primary query language. Neptune, meanwhile, supports multiple query languages—including SPARQL, Gremlin, and OpenCypher—catering to a broader range of graph models and use cases. Both databases are designed for performance, though Neptune’s AWS-managed infrastructure provides automatic scaling and can achieve faster query execution for certain operations.

Cost-wise, JanusGraph requires no licensing fees, as it is open-source. Users only incur costs for the underlying infrastructure, which can be configured for cost efficiency. Neptune’s costs include database instance charges, storage, and data transfer fees, aligning with AWS’s pricing model.

JanusGraph is suitable for use cases requiring complex traversals, mixed indexing, and custom deployment configurations. Its open-source nature and flexibility may be appealing to organizations emphasizing control and adaptability, while Neptune remains a viable option for those seeking a managed service closely integrated with the AWS ecosystem.

OrientDB

OrientDB is a multi-model database combining graph, document, key-value, and object-oriented storage in a single solution for broad-based data management. Unlike Neptune, which primarily operates as a managed AWS service, OrientDB supports self-hosted deployments on-premises and in cloud environments, with automation available through Ansible. This adjustability makes it appropriate for organizations with diverse infrastructure requirements or preferences for on-premises control.

OrientDB’s multi-model architecture supports document, graph, and key-value data models, providing a unified platform for handling varied data needs. Querying in OrientDB is facilitated with SQL extensions for graph operations, utilizing familiarity with SQL while avoiding the need for a proprietary query language. Neptune, in contrast, supports Gremlin, SPARQL, and OpenCypher, making it a flexible option for property graph and RDF models.

When it comes to performance, OrientDB offers features like parallel query execution, transaction log control, and I/O performance tuning. These capabilities can be notably beneficial for custom deployments with intensive data processing requirements. Neptune, backed by AWS’s infrastructure, scales automatically up to 128 TiB of storage, supports up to 15 read replicas, and provides sub-100ms replica lag for high availability.

OrientDB’s Apache 2 license involves no licensing costs, leaving infrastructure as the primary expense. Neptune follows AWS’s usage-based pricing, which includes costs for instances, storage, and I/O operations. This difference gives OrientDB users more direct influence over their operational costs, particularly in self-managed environments.

OrientDB is well-suited for complex query scenarios, multi-model data management, and business intelligence integration. Neptune, on the other hand, performs effectively in serverless applications, auto-scaling workloads, and integration within the AWS ecosystem. For organizations prioritizing adjustability and multi-model support, OrientDB is a viable option in the graph database landscape.

Conclusion

AWS Neptune is a solid option for a managed graph database, but it’s not the only choice. The right solution depends on your organization’s priorities—whether that’s deployment flexibility, cost, integration ease, or scalability. By exploring alternatives, you can find a graph database that fits your specific needs and goals.

Ready to see what PuppyGraph can do? Download the forever free PuppyGraph Developer Edition, or start a free 30-day trial of the Enterprise Edition.