AWS Neptune Alternatives

Amazon Neptune is AWS's serverless graph database service for connected data. It supports Gremlin, openCypher, and SPARQL, while Neptune Analytics adds in-memory graph analytics and vector search for analytical workloads.

Neptune is a practical option for teams that want a managed graph service inside AWS. However, engineering teams building stateful agents, context graphs, enterprise knowledge systems, or streaming graph applications may need a different combination of retrieval, temporal modeling, deployment control, and storage economics.

This guide reviews six AWS Neptune alternatives. HydraDB is the strongest option for teams that need a graph database purpose-built for modern AI workflows, while Neo4j, TigerGraph, Memgraph, ArangoDB, and JanusGraph remain relevant for other graph requirements.

Key Takeaways

• HydraDB is purpose-built for AI workflows. It combines graph traversal, semantic retrieval, BM25 keyword retrieval, metadata filtering, memories, and temporal context behind a unified API.

• AWS Neptune is strongest inside AWS. It provides managed property-graph and RDF infrastructure, high availability, and integration with the broader AWS ecosystem.

• Temporal state matters for long-running agents. Systems that must distinguish current facts from superseded facts need explicit time-aware modeling rather than similarity search alone.

• Storage architecture affects long-term cost. HydraDB is built on object storage and uses tiered storage, while other graph platforms may depend more heavily on provisioned compute, memory, SSDs, or external storage backends.

• The right alternative depends on the workload. Neo4j emphasizes ecosystem maturity, TigerGraph focuses on large-scale analytics, Memgraph targets real-time streaming, ArangoDB provides multi-model flexibility, and JanusGraph offers self-managed distributed graph infrastructure.

What AWS Neptune Provides

AWS offers two related Neptune services:

• Neptune Database is a managed operational graph database supporting Gremlin, openCypher, and SPARQL.

• Neptune Analytics is an in-memory analytics engine for graph algorithms, analytical queries, and vector search.

• AI integrations include managed GraphRAG through Amazon Bedrock Knowledge Bases and integrations with agent and memory tooling.

Neptune Database uses distributed storage that expands as the graph grows. Teams can deploy read replicas, use Multi-AZ configurations, and integrate the service with AWS identity, networking, monitoring, and security tools.

The main trade-off is architectural scope. Neptune now includes GraphRAG and agent integrations, but it remains an AWS-managed graph platform rather than a unified context substrate with first-class knowledge, memories, and experiences behind one API. Persistent user memory, temporal knowledge updates, application-aware ingestion, and context assembly may still require additional modeling or services.

1. HydraDB

HydraDB is the graph database AI runs on: a fast graph database built on object storage and purpose-built for modern AI workloads. It provides graph-native context for agent memory, company brains, ontologies, context graphs, enterprise knowledge systems, and agentic applications.

Agent memory is one application developers can build on HydraDB. The broader platform gives engineering teams control over graph structure, retrieval behavior, ranking, filtering, memory logic, and context delivery rather than forcing every application into a predetermined memory abstraction.

Core Capabilities

• Unified context model: HydraDB stores knowledge, user memories, and time-ordered agent experiences.

• Hybrid retrieval: A single query can combine metadata filtering, semantic retrieval, BM25 keyword retrieval, and context-graph traversal.

• Temporal state: Git-style versioned graphs preserve how facts, relationships, preferences, and decisions change over time.

• Structured ingestion: HydraDB parses, chunks, embeds, indexes, and links documents and application records into a context graph.

• Relationship-aware recall: Graph traversal can surface structurally connected information that semantic similarity alone may miss.

• Data isolation: Databases, collections, and metadata filters help scope information by customer, user, department, workspace, or environment.

• Developer tooling: Official Python and TypeScript/Node.js SDKs wrap the platform APIs.

• Flexible deployment: Managed cloud, BYOC, licensed self-hosting, and fully self-hosted enterprise configurations are available across applicable plans.

HydraDB's documented connectors continuously sync Slack, GitHub, Linear, Notion, and Gmail. Jira, Salesforce, CRM records, support tickets, and other application data can also be ingested through structured app-source payloads and APIs.

This architecture is designed around the idea that similarity is not the same as relevance. A relevant result may depend on a relationship, chronology, identity, source, or prior outcome even when its wording is not close to the current query. HydraDB combines these signals so applications can retrieve context that is useful for the task rather than merely text that looks similar.

Temporal Context for Evolving Information

Persistent agents must understand more than what was written. They need to determine what is true now, what was true previously, when a change occurred, and how related events produced the current state.

HydraDB's temporal graphs retain historical state instead of treating every update as a destructive overwrite. This is useful for changing user preferences, policy revisions, evolving codebases, account histories, financial filings, incident records, and organizational decisions.

In HydraDB's company-published LongMemEval-S evaluation using Gemini 3.0 Pro, the platform reported 90.79% overall accuracy and 97.43% accuracy on the Knowledge Update category. These are company-conducted benchmark results rather than guarantees for every production workload, but they directly evaluate long-horizon retrieval and reasoning across evolving information.

Object-Storage Architecture

HydraDB is built around object storage rather than requiring the entire graph to remain on expensive memory or SSD infrastructure. Its broader tiered architecture can place frequently accessed context in memory, warm context on NVMe, and less active context in object storage.

HydraDB describes this design as delivering up to 10x lower storage costs than traditional disk- or RAM-heavy graph deployments. Actual savings depend on graph size, access patterns, deployment configuration, and the system used for comparison.

The platform also advertises sub-200ms recall for low-latency use cases. Production latency still depends on dataset size, query complexity, retrieval mode, graph depth, concurrency, and infrastructure.

HydraDB Pricing

HydraDB currently publishes storage-based pricing with no per-seat pricing or API-call limits:

• Ship: Free, with unlimited API calls and tenants, multi-tenancy, observability, and community support.

• Surge: $25 per month, including up to 2GB of graph storage, with additional storage at $0.50 per GB per month.

• Scale: $399 per month, including up to 10GB of graph storage, additional storage at $0.25 per GB per month, dedicated infrastructure, and a licensed self-hosting option.

• Enterprise: Custom pricing for BYOC, fully self-hosted deployment, dedicated account management, and support and uptime SLAs.

Pricing and plan details can change, so teams should confirm the current terms before making a production decision.

Company-Published Traction

HydraDB states that it has raised $6.5 million from investors, including Jeff Dean, researchers from OpenAI and DeepMind, and Sky9 Capital. The company also reports more than one billion documents ingested, approximately one million retrievals per month, and adoption by about 2,000 developers.

These figures should be presented as HydraDB-reported company metrics. They do not imply that every developer or document represents a production deployment.

When HydraDB Is the Best Choice

Choose HydraDB when the application needs:

• Persistent context across sessions

• Relationship-aware and time-aware retrieval

• A unified graph, semantic, keyword, and metadata query layer

• Developer-controlled memory and context architecture

• Object-storage economics for growing context

• Managed, BYOC, or self-hosted deployment options

• Infrastructure for AI agent architecture, company knowledge, ontologies, and context graphs

For these workloads, HydraDB provides a more complete AI context foundation than AWS Neptune alone because the database, ingestion pipeline, memory primitives, temporal model, and hybrid retrieval layer are designed to work together.

2. Neo4j

Neo4j is a mature property-graph database with a large developer ecosystem. It is closely associated with the Cypher query language and supports operational graph applications, graph visualization, analytics, and machine learning through its broader product portfolio.

Core Strengths

• Cypher-based property-graph querying

• Neo4j Graph Data Science for algorithms and machine learning workflows

• AuraDB managed cloud deployments

• Self-managed deployment options

• Broad documentation, training, tooling, and partner support

• Established use across fraud, recommendations, identity, network analysis, and knowledge graphs

Neo4j is a strong choice for teams with existing Cypher expertise or applications centered on conventional property-graph workloads. Its mature ecosystem can reduce adoption risk when graph database familiarity and third-party tooling matter more than a purpose-built AI context layer.

For stateful AI applications, teams should evaluate how they will add persistent memories, application ingestion, temporal state, lexical and semantic retrieval, ranking, and context packaging. Neo4j can participate in those architectures, but the surrounding AI context pipeline may require additional components and engineering.

Pricing Approach

Neo4j offers managed and self-managed products with pricing that varies by service tier, capacity, cloud region, and enterprise requirements. Teams should model the expected database size, compute needs, replicas, backups, support, and Graph Data Science usage rather than comparing a single storage price in isolation.

3. TigerGraph

TigerGraph is designed for distributed graph analytics and complex traversals across highly connected datasets. It uses the GSQL language and provides managed cloud capabilities through TigerGraph Savanna.

Core Strengths

• Distributed graph processing

• GSQL for schema definition, loading, querying, and analytics

• Multi-hop traversal and graph algorithm support

• Vector search capabilities

• Managed cloud and self-managed deployment options

• Common use cases in fraud detection, anti-money laundering, customer intelligence, and supply-chain analysis

TigerGraph is most relevant when the primary requirement is analytical processing across large graphs rather than persistent agent context. Its architecture is well suited to teams that need graph algorithms, parallel processing, and deep relationship analysis over extensive datasets.

Pricing Approach

TigerGraph pricing depends on the deployment model, provisioned resources, storage, workload duration, support level, and enterprise terms. Managed workspaces can use features such as auto-suspend and auto-resume, which may help control cost for intermittent analytical workloads.

4. Memgraph

Memgraph is an in-memory graph database designed for low-latency operational and streaming workloads. It uses Cypher and can ingest data from Kafka, Redpanda, and Pulsar sources.

Core Strengths

• In-memory graph processing

• Cypher query support

• Streaming ingestion from Kafka, Redpanda, and Pulsar

• Memgraph Lab for development, querying, and visualization

• MAGE algorithms and custom procedures

• MCP tooling for agent and AI integrations

Memgraph is a strong fit for event-driven graph applications where new relationships must be queried immediately. Examples include operational monitoring, recommendation updates, security events, network analysis, and real-time fraud signals.

The main architectural consideration is memory economics. Teams should estimate the graph's working set, growth rate, persistence requirements, replication needs, and memory footprint before choosing an in-memory platform for large long-lived context stores.

Pricing Approach

Memgraph provides local and self-managed options alongside managed cloud and enterprise offerings. Production cost depends heavily on memory capacity, availability requirements, and the selected deployment model.

5. ArangoDB

ArangoDB is a multi-model database that supports graph, document, key-value, vector, and full-text search capabilities through a unified platform and query language.

Core Strengths

• Graph and document data in one database

• AQL for querying across supported data models

• Built-in search capabilities

• Vector search support

• Managed cloud and on-premises deployment options

• Distributed graph features for clustered environments

ArangoDB is useful when one application needs several data models and the team wants to reduce the number of separate operational databases. It can support graph relationships while retaining document-oriented flexibility for records that do not naturally fit a rigid graph model.

For AI workloads, teams should evaluate whether the platform's general multi-model approach provides enough memory, temporal, ingestion, and context-delivery functionality or whether those layers must be implemented separately.

Pricing Approach

ArangoDB offers cloud and self-managed deployment models. Cost depends on cluster resources, storage, support, availability, and enterprise features rather than a single graph-storage rate.

6. JanusGraph

JanusGraph is an open-source distributed graph database built around a modular architecture. It uses Apache TinkerPop and Gremlin and relies on separate storage and indexing backends.

Core Strengths

• Gremlin support through Apache TinkerPop

• Pluggable storage backends such as Cassandra and HBase

• Pluggable indexing backends

• Flexible distributed deployment topologies

• Self-managed infrastructure control

• Compatibility with broader Hadoop and Spark processing ecosystems

JanusGraph is best suited to organizations that already operate distributed data infrastructure and have database engineering expertise. It provides substantial control, but teams are responsible for selecting, configuring, scaling, monitoring, upgrading, and securing the underlying storage and indexing systems.

Pricing Approach

JanusGraph does not impose a managed database subscription by itself. Total cost comes from infrastructure, storage and indexing backends, observability, operations, engineering time, and any third-party managed services used around it.

HydraDB vs. AWS Neptune

HydraDB and AWS Neptune both operate on connected data, but they are optimized for different application architectures.

Architecture

AWS Neptune is a managed AWS graph service. Neptune Database uses distributed storage for operational graph workloads, while Neptune Analytics provides an in-memory analytical engine.

HydraDB is built on object storage and designed as graph-native context infrastructure for AI. Its architecture coordinates isolation, ingestion, indexing, graph construction, temporal state, and hybrid retrieval behind one API.

Retrieval

Neptune supports graph queries through Gremlin, openCypher, and SPARQL. Vector search is available through Neptune Analytics.

HydraDB combines graph traversal, semantic retrieval, BM25 keyword retrieval, metadata filtering, query expansion, and reranking in the context-retrieval pipeline. This makes it better suited to applications that need to assemble model-ready context rather than only execute graph queries.

Temporal State

Neptune applications can model time using properties, entities, and application logic. The schema and update behavior are designed by the development team.

HydraDB treats evolving state as a core AI context problem. Its versioned context preserves previous and current information so agents can reason about changes without relying entirely on custom temporal modeling.

Agent Memory

Neptune can store graph data used by an agent, but it does not provide an opinionated memory application or a unified memory workflow by default.

HydraDB provides primitives for knowledge, memories, and experiences while allowing developers to control the final memory architecture. It supports persistent agent memory without reducing the broader database to a single memory abstraction.

Deployment

Neptune is available as an AWS-managed service and is tied to AWS infrastructure.

HydraDB supports managed cloud, BYOC, licensed self-hosting, and fully self-hosted enterprise deployment. This provides more flexibility for teams with data residency, network isolation, or infrastructure-control requirements.

Pricing

Neptune pricing can include provisioned database instances or serverless capacity, storage, I/O, replicas, backups, data transfer, and Neptune Analytics capacity. Neptune Standard and I/O-Optimized configurations use different charging models.

HydraDB publishes storage-oriented plans with included graph storage and predictable overage rates. A direct annual comparison should still account for workload equivalence, query volume, availability, support, and deployment requirements. Teams should use the AWS Pricing Calculator for Neptune and request a workload-specific HydraDB estimate rather than relying on a single fixed cost comparison.

How to Choose an AWS Neptune Alternative

Choose HydraDB when:

• The graph is part of a stateful AI system

• Agents must remember users, actions, outcomes, and changing facts

• The application needs graph, semantic, BM25, temporal, and metadata retrieval together

• Developers want control over memory and context behavior

• Object-storage economics and deployment flexibility matter

• The platform must support knowledge graphs, ontologies, company brains, or context graphs

Choose Neo4j when:

• The team already uses Cypher

• Ecosystem maturity and third-party tooling are priorities

• The workload is a conventional property graph

• Graph analytics and visualization are central requirements

Choose TigerGraph when:

• The workload centers on distributed graph analytics

• Deep traversals and graph algorithms run across very large datasets

• GSQL and TigerGraph's analytical ecosystem fit the team

Choose Memgraph when:

• New events must become queryable immediately

• Kafka, Redpanda, or Pulsar is central to the architecture

• The working graph can be supported economically in memory

Choose ArangoDB when:

• One application needs graph, document, key-value, vector, and search capabilities

• The team wants a unified multi-model operational database

• AQL fits the development model

Choose JanusGraph when:

• The organization wants a self-managed distributed graph layer

• Cassandra or HBase is already part of the infrastructure

• The team has the expertise to operate storage, indexing, and graph services independently

Frequently Asked Questions

What is the best AWS Neptune alternative for AI agents?

HydraDB is the strongest alternative for AI agents that need persistent context, relationship-aware retrieval, temporal state, user memory, and hybrid search. It is built as a graph database for AI workflows rather than a general-purpose graph service with AI components added around it.

What is the main difference between HydraDB and AWS Neptune?

AWS Neptune is a managed graph database and analytics service inside AWS. HydraDB is graph-native context infrastructure that combines the graph database with ingestion, memories, temporal state, semantic and lexical retrieval, metadata filtering, and context delivery for AI applications.

Does AWS Neptune support vector search?

Neptune Analytics supports vector search alongside graph analytics and low-latency graph queries. Teams using Neptune Database should determine whether they need Neptune Analytics or another service for their embedding-based retrieval architecture.

Why does temporal context matter for AI agents?

Without temporal context, an agent may retrieve an outdated policy, previous user preference, superseded technical decision, or historical account state as though it were current. Time-aware graphs let the system preserve history while identifying which state is valid for the current query.

Can HydraDB replace a vector database?

HydraDB can complement or replace a separate vector database depending on the application. It includes semantic retrieval but also adds BM25 keyword retrieval, graph traversal, temporal structure, metadata filtering, memories, and context assembly. The vector database gap is that similarity alone does not encode relationships, evolving state, or persistent user context.

Is HydraDB only an agent memory product?

No. HydraDB is a graph database and context infrastructure platform. Agent memory is one application. Developers can also use it to build company brains, ontologies, context graphs, enterprise knowledge systems, research agents, coding agents, customer-support systems, and agentic actions. Additional application examples are available in HydraDB's use cases.

Can teams self-host HydraDB?

Yes. HydraDB currently lists a licensed self-hosting option on the Scale plan and BYOC or fully self-hosted configurations for Enterprise customers. Exact infrastructure, support, and licensing terms should be confirmed with HydraDB.

How should teams compare graph database pricing?

Use a representative production workload rather than comparing headline storage prices. Include graph size, query volume, retrieval modes, replicas, storage and I/O, analytics, vector search, backups, data transfer, support, and the engineering cost of any separate context services.