Neo4j vs AWS Neptune vs HydraDB

Selecting a graph database for AI applications requires more than comparing query languages or deployment models. Production agents need infrastructure that can preserve state across sessions, retrieve relationship-aware context, distinguish current facts from historical ones, and scale as knowledge accumulates.

Neo4j offers a mature property-graph ecosystem and managed AuraDB deployments. Amazon Neptune provides a fully managed graph service for teams building inside AWS. HydraDB, by contrast, is designed as a graph database for AI workflows, with graph-native context infrastructure, temporal state, multi-signal retrieval, and an object-storage-based architecture for persistent AI context.

For teams building stateful agents, company brains, context graphs, knowledge systems, or persistent memory, the most important differences are how each platform represents changing state, retrieves context, isolates workloads, and scales storage and compute.

Key takeaways

• HydraDB is positioned as a graph database for AI workflows rather than a packaged memory application. Agent memory is one application developers can build on top of its graph-native context infrastructure.

• HydraDB uses a versioned temporal graph to preserve changing state, helping applications reason about what is true now, what was true previously, and when information changed.

• HydraDB combines semantic, lexical, graph, temporal, and metadata signals in its retrieval architecture instead of relying on similarity search alone.

• HydraDB reports 90.79% overall accuracy on LongMemEval-S in its company-conducted evaluation. Separately, HydraDB reports sub-200ms retrieval for low-latency workloads, with actual latency depending on query and deployment characteristics.

• Neo4j is a strong fit for teams that prioritize a mature property-graph ecosystem, Cypher tooling, graph analytics, and managed or self-managed deployment options.

• Amazon Neptune is a strong fit for AWS-native teams that need managed graph infrastructure, property-graph and RDF support, and integration with the broader AWS platform.

• Direct cost comparisons require workload-equivalent configurations. HydraDB, Neo4j AuraDB, and Neptune meter different resources, so graph size alone is not enough to produce a defensible TCO comparison.

Understanding each database's core positioning

Neo4j

Neo4j is a mature property-graph database centered on the Cypher query language. Its ecosystem spans the managed AuraDB service, self-managed deployments, vector search, graph analytics, developer tooling, and integrations for application development.

Neo4j is well suited to established graph workloads such as fraud detection, identity and access analysis, recommendations, network analysis, and enterprise knowledge graphs. For teams with existing Cypher expertise or a need for a broad graph tooling ecosystem, Neo4j remains a conventional enterprise choice.

Amazon Neptune

Amazon Neptune is a fully managed graph database service in AWS. Neptune Database supports Gremlin and openCypher for property graphs and SPARQL for RDF data. AWS also offers Neptune Analytics for analytical graph workloads.

Neptune is especially relevant to organizations already standardized on AWS because deployment, security, monitoring, networking, and scaling can be integrated into existing AWS infrastructure. Its managed architecture reduces the operational burden of running graph database infrastructure directly.

HydraDB

HydraDB takes a different approach. It is designed as graph-native context infrastructure for modern AI applications, including persistent agent memory, company knowledge systems, context graphs, ontologies, and agentic workflows.

The platform is intended to give developers primitives for building their own context architecture rather than forcing a predetermined memory abstraction. Applications can ingest knowledge and user context, build a connected graph, retrieve useful state through a unified API, and pass that context to the language model or agent framework of their choice.

This positioning matters because AI agents need more than document lookup. They need infrastructure that can represent relationships, preserve changes over time, isolate customer or workspace data, and assemble context that reflects relevance rather than textual similarity alone.

Feature comparison across core capabilities

Query and retrieval capabilities

Neo4j uses Cypher for property-graph querying and supports vector indexes alongside graph traversal. Its Graph Data Science library adds graph algorithms and machine-learning workflows for analytical use cases.

Amazon Neptune supports Gremlin and openCypher for property graphs and SPARQL for RDF. Neptune Analytics adds graph algorithms and analytical capabilities for large connected datasets.

HydraDB uses API- and SDK-based access with a retrieval architecture that combines multiple context signals. Its hybrid retrieval approach can incorporate:

• Semantic retrieval for meaning-based matching

• Keyword and BM25 retrieval for lexical precision

• Graph relationships for structurally connected context

• Temporal signals for changing state

• Metadata filtering for deterministic scoping

• Reranking and query expansion for more deliberate retrieval modes

The core thesis is that similarity and relevance are not identical. A semantically similar chunk can still be wrong if it is outdated, belongs to the wrong tenant, or omits the relationship that makes the information useful.

Temporal context handling

Temporal state is one of the clearest differences for AI workloads.

Neo4j and Neptune can represent time in graph data, but applications generally need to design how historical states, events, or changing relationships are modeled and queried.

HydraDB makes versioned state a core part of its architecture. Its Git-style temporal graph uses append-oriented state transitions rather than treating every update as a destructive overwrite. That allows applications to reason over questions such as:

• What is true now?

• What was true at a previous point in time?

• When did this preference, relationship, or fact change?

• Which earlier state led to the current one?

This model is particularly relevant for agents that operate across long-running customer relationships, changing organizational decisions, evolving product data, or multi-session workflows.

Storage architecture

Neo4j uses a native graph storage engine and prices its managed AuraDB plans around provisioned database capacity.

Amazon Neptune uses a managed distributed storage layer, with pricing that can include database or serverless compute, storage, I/O, backup, and data transfer depending on the configuration.

HydraDB uses tiered storage across memory, NVMe SSD, and object storage. Frequently accessed context can remain in faster tiers while colder context can be retained more economically. This design is intended to support AI systems whose persistent context grows continuously over time.

For long-term AI context, this separation is important because retaining historical facts, agent experiences, and relationship history can become expensive when all graph state must remain on high-cost infrastructure.

Developer control and context architecture

HydraDB is designed to provide database and retrieval primitives rather than dictate one memory architecture. Developers can use the platform to build persistent memory, knowledge systems, context graphs, and other stateful AI infrastructure while retaining control over how context is scoped, retrieved, ranked, and delivered to the model.

That makes HydraDB relevant beyond memory alone. The same graph-native foundation can support knowledge graphs, company brains, ontologies, customer context, and agent decision histories.

Pricing models reflect different architectures

The three products use different pricing units, so their published prices should not be treated as directly interchangeable.

HydraDB pricing

HydraDB's current public pricing includes:

• Ship: Free, with unlimited API calls and tenants

• Surge: $25 per month, including up to 2GB of graph storage and $0.50/GB/month storage overage

• Scale: $399 per month, including up to 10GB of graph storage, $0.25/GB/month storage overage, dedicated infrastructure, and an option to self-host

• Enterprise: Custom pricing with BYOC and fully self-hosted deployment options

HydraDB describes its plans as storage-based pricing with minimum commitments and no per-seat or feature limits. Its current FAQ also states that customers pay for knowledge stored and queries served, so teams evaluating production usage should confirm the exact query-metering terms for their workload.

Neo4j AuraDB pricing

Neo4j currently lists:

• AuraDB Free: $0

• AuraDB Professional: $65/GB/month of provisioned database capacity, with a 1GB minimum

• AuraDB Business Critical: $146/GB/month of provisioned database capacity, with a 2GB minimum and a 99.95% SLA

• Dedicated and self-managed options: Available for organizations with additional infrastructure or isolation requirements

Neo4j's pricing page states that AuraDB capacity pricing includes storage, I/O, backups, and data transfer.

Amazon Neptune pricing

Amazon Neptune uses a consumption model that varies by deployment type and AWS Region. Depending on configuration, charges can include:

• Database instance or serverless compute capacity

• Database storage

• I/O requests or I/O-optimized pricing

• Backup storage

• Data transfer

• Read replicas and additional infrastructure

Because Neptune pricing depends heavily on workload and deployment choices, a single monthly figure does not represent all production configurations.

How to compare total cost of ownership

A defensible TCO analysis should compare equivalent workloads rather than equal graph sizes. Teams should model:

• Stored graph and historical data volume

• Query and write volume

• Concurrency

• Retrieval complexity and graph depth

• Availability and replication requirements

• Backup and retention requirements

• Support level

• Networking and data transfer

• Operational staffing

HydraDB's object-storage-oriented architecture is designed to make persistent context retention more economical as data grows, but the actual cost difference depends on the workload and deployment configuration.

Performance and reliability considerations

HydraDB performance data

HydraDB reports 90.79% overall accuracy on LongMemEval-S in its company-conducted evaluation. The same published work reports 97.43% on knowledge-update tasks and 90.97% on temporal reasoning.

Separately, HydraDB reports sub-200ms retrieval for low-latency agent workloads. These should be treated as distinct claims: LongMemEval-S evaluates memory and state correctness, while retrieval latency depends on query complexity, graph depth, dataset size, retrieval mode, and infrastructure.

For teams evaluating AI memory benchmarks, the safest approach is to treat vendor-published benchmark results as useful directional evidence and then reproduce representative workloads on proprietary data.

Neo4j and Neptune reliability

Neo4j AuraDB Business Critical includes a 99.95% SLA and multi-zone managed infrastructure. Performance depends on the provisioned database capacity and the characteristics of the graph workload.

Amazon Neptune is designed as a highly available managed service with replicated storage and read-replica options across Availability Zones. Performance and cost depend on instance or serverless capacity, query patterns, graph size, and selected storage mode.

Use case mapping: when each database fits

Best fit for HydraDB

HydraDB is particularly well aligned with:

• Persistent AI agent memory that must survive across sessions

• Temporal reasoning where facts, preferences, or relationships change

• Context graphs connecting people, documents, events, and decisions

• Hybrid retrieval that needs semantic, lexical, relational, temporal, and metadata signals

• Multi-tenant AI products that need isolated customer or workspace context

• Company knowledge systems that connect information across applications

• AI workloads with growing historical context where object-storage economics can matter

These patterns map directly to the broader HydraDB use cases for stateful AI systems.

Best fit for Neo4j

Neo4j is a strong fit for:

• Enterprise property-graph applications

• Teams with established Cypher expertise

• Graph analytics and data-science workflows

• Fraud, identity, recommendations, and network analysis

• Organizations that value a mature graph ecosystem and flexible deployment options

Best fit for Amazon Neptune

Amazon Neptune is a strong fit for:

• AWS-native architectures

• Teams that want a fully managed graph service

• Property-graph workloads using Gremlin or openCypher

• RDF and SPARQL workloads

• Applications that need AWS networking, IAM, monitoring, and infrastructure integration

Integration ecosystem

HydraDB integrations

HydraDB publishes a REST API plus official Python and TypeScript/Node.js SDKs. Its documentation and cookbooks show ingestion patterns for workplace knowledge sources including Slack, Gmail, Notion, Confluence, GitHub, and Linear.

HydraDB can also sit behind different model providers and agent frameworks because the database's role is to store, structure, and retrieve context. The application remains responsible for passing the retrieved context to the model or agent runtime it chooses.

This supports the broader goal of stateful AI infrastructure without forcing a specific LLM provider or memory abstraction.

Neo4j integrations

Neo4j provides drivers across major programming languages, Graph Data Science tooling, visualization and exploration tools, vector search, and integrations across the broader data and AI ecosystem.

Amazon Neptune integrations

Neptune integrates closely with AWS services and security controls. Applications can work with Neptune through AWS APIs, SDKs, graph drivers, and supported query languages, while infrastructure can be managed through standard AWS deployment and observability tooling.

Why HydraDB is designed for AI workflows

The strongest case for HydraDB is not that every graph workload should move to one database. It is that persistent AI systems introduce a combination of requirements that traditional graph deployments were not originally optimized around.

Temporal context is part of the data model. Stateful agents need to know not only which fact exists, but whether it is still valid. HydraDB's versioned graph is designed to preserve historical state so agents can distinguish current context from superseded information. This is central to time-aware retrieval.

Retrieval is multi-signal rather than similarity-only. AI context often depends on exact terms, relationships, time, metadata, and user history. HydraDB combines these signals so context assembly does not depend on embeddings alone.

Persistent context can grow without forcing every record into the hottest storage tier. HydraDB's memory, NVMe, and object-storage architecture is designed for workloads that accumulate context continuously across conversations, documents, events, and outcomes.

Developers retain control over the context system. HydraDB provides infrastructure for building memory, knowledge, and context systems rather than requiring teams to adopt one opinionated memory model. This is useful when the application needs custom graph structure, filtering, retrieval, ranking, or context-delivery behavior.

The platform is built for stateful agents. Persistent context lets applications carry forward user preferences, historical decisions, previous outcomes, and organizational knowledge across sessions. That is the foundation of long-term agent memory.

HydraDB's public site also states that it is SOC 2 and ISO 27001 certified. Surge includes access to SOC 2 and GDPR reports and a DPA, while Scale adds a self-hosting option for teams with stricter deployment requirements.

Frequently asked questions

How does HydraDB's temporal versioning differ from modeling events in Neo4j or Neptune?

HydraDB treats historical state as a native part of its graph architecture. Its versioned temporal model preserves state transitions so applications can reason about both current and previous facts. Neo4j and Neptune can represent timestamps, event nodes, versioned relationships, and other temporal structures, but teams generally define those patterns in the data model and application logic. HydraDB's advantage for stateful AI is that temporal change is part of the context architecture rather than an additional pattern the application must build from scratch.

Can teams migrate from Neo4j or Amazon Neptune to HydraDB?

Yes, but HydraDB should not be treated as a drop-in Cypher replacement. HydraDB exposes its own REST API and official SDKs, so migration is an application integration project rather than a connection-string change. For some organizations, the best transition is to introduce HydraDB first for new AI-native workloads such as persistent agent context, temporal memory, or relationship-aware retrieval while keeping an existing graph system for workloads that already depend on its query language or tooling.

How long does HydraDB take to integrate?

HydraDB's current public guidance says integration takes under a day for most teams. The company also states that developers can ingest their first records and run a first query in under 10 minutes, while enterprise pilots can receive support from a forward-deployed engineer. Applications can integrate through HydraDB's REST API or official Python and TypeScript/Node.js SDKs. A persistent memory guide can provide additional architectural context for teams adding long-lived agent state.

How do the three databases handle multi-tenant AI applications?

HydraDB includes tenant-oriented isolation in its API and pricing model, with additional metadata filtering for retrieval scoping. This allows teams to separate customer, workspace, or application context while still using a shared platform. Neo4j supports multiple databases and managed deployment patterns that can be used for workload or tenant separation. Neptune relies on AWS infrastructure, database clusters, IAM, networking, and application-level data modeling to define isolation boundaries.

What compliance options does HydraDB provide?

HydraDB's current public site states that it is SOC 2 and ISO 27001 certified. Its Surge plan includes SOC 2 and GDPR reports plus DPA access, while Scale includes an option to self-host and Enterprise supports BYOC and fully self-hosted deployments. Teams in regulated environments should still verify their specific requirements, data residency needs, security controls, and contractual terms before deployment. HydraDB's broader enterprise AI guidance outlines the security and governance considerations that become important as persistent agent context moves into production.