Amazon Neptune is AWS’s fully managed graph database, and in 2026 AWS repositioned it around a single headline use case: Graphs for AI, meaning GraphRAG and long-term agent memory delivered through Amazon Bedrock Knowledge Bases. Underneath that pitch are two distinct products, Neptune Database for transactional graph storage and Neptune Analytics for in-memory GraphRAG and vector search, plus native support for Gremlin, openCypher, and SPARQL in one service. This page covers what that repositioning actually means, and what it doesn’t solve.
Most explainers of “what Neptune is used for” still describe it as one general-purpose graph database, a framing AWS itself dropped when Graphs for AI became the lead pitch. That gap is where this page sits: not another AWS-docs rehash, and not a Neptune-versus-everything shootout, but the mechanics AWS keeps siloed across separate pages, plus the tradeoffs its own marketing page never states.
- Two products, one brand name: Neptune Database (transactional) and Neptune Analytics (in-memory GraphRAG and vector search) get described as a single product in most third-party explainers
- Real production evidence: Wiz, Trend Micro, and Coinbase all run Neptune in production for security, support, and clustering workloads, not just AWS demo scenarios
- Real limitations AWS doesn’t advertise: RDFS-only inference, a community-reported openCypher latency gap, and friction moving data back out
- The governance question underneath: a graph that improves AI accuracy still needs something deciding what an agent is allowed to retrieve from it
- A related but different question: what a knowledge graph is and what a graph database like Neptune stores are related, but not identical, questions
Below: what Neptune is used for, how its two products differ, whether it can power GraphRAG, real production deployments, how its architecture compares to the alternative most often raised, its limitations, and where a governed context layer fits above the graph.
| Field | Detail |
|---|---|
| What it is | AWS’s fully managed graph database service, actually two products: Neptune Database and Neptune Analytics |
| 2026 positioning | “Graphs for AI”, GraphRAG and long-term agent memory via Amazon Bedrock Knowledge Bases, now AWS’s headline use case ahead of Customer 360, fraud detection, and cybersecurity |
| Query languages | Gremlin (TinkerPop 3.3), openCypher v9, and SPARQL 1.1, all in one managed service |
| Scale | 100k+ queries per second, unlimited vertices and edges, up to 128 TiB storage per cluster, up to 15 read replicas |
| Production use | Wiz’s Security Graph (hundreds of billions of relationships; 40% faster investigations from the Bedrock AI layer built on top of it), Trend Micro’s Companion AI (20% better answer quality), Coinbase’s user-clustering system |
| Deployment model | Fully managed by AWS; Neptune Analytics adds serverless, capacity-unit-based GraphRAG and vector workloads on top of Neptune Database’s instance-based OLTP model |
What is Amazon Neptune used for?
Permalink to “What is Amazon Neptune used for?”Amazon Neptune is AWS’s fully managed graph database, and as of AWS’s 2026 repositioning, its headline use case is “Graphs for AI”: GraphRAG and long-term agent memory, ahead of the three use cases that used to define it, Customer 360, fraud detection, and cybersecurity.
AWS’s own product page now leads with Graphs for AI instead of the older “general graph database for connected data” framing, and names four official use cases in this order: Graphs for AI, Customer 360, Fraud Detection, and Cybersecurity. That reordering reads like a signal, though AWS hasn’t stated the reasoning behind it: moving a use case to the top of a product page is a choice, not an accident, and the most plausible read is that AI-agent workloads are pulling ahead of the fraud-and-recommendation graphs that used to be Neptune’s calling card.
No independent, non-AWS source has reframed “what Neptune is used for” around this shift yet. Most third-party tutorials and certification-prep content still describe Neptune as a general-purpose graph database, the same framing AWS itself has already moved past. That gap is structural, not a matter of any one article being outdated: AWS controls the definitional query, so anyone answering it starts from AWS’s own copy.
A graph engine like Neptune decides how data connects. What it doesn’t decide, on its own, is which of those connections an agent is allowed to use, a distinction that matters more as “Graphs for AI” becomes the reason teams adopt Neptune in the first place. What a context graph adds on top of a graph database is exactly that missing layer, and it’s a question worth asking before the graph is already in production.
What’s the difference between Neptune Database and Neptune Analytics?
Permalink to “What’s the difference between Neptune Database and Neptune Analytics?”“Amazon Neptune” is really two separate products under one brand: Neptune Database for transactional graph storage, and Neptune Analytics for the in-memory, vector-search-enabled layer that GraphRAG workloads actually run on. Most explainers of “what Neptune is used for” collapse that distinction, which is exactly where readers get confused about how GraphRAG on Neptune actually works.
Neptune Database is the original product: an OLTP-style transactional graph store, purpose-built for high-throughput read and write graph workloads like fraud detection, recommendation, and Customer 360. It supports Gremlin, openCypher, and SPARQL, according to AWS’s Neptune Features page. Neptune Analytics is the newer, purpose-built analytics engine: in-memory, with native vector search, designed specifically for the graph-traversal-plus-similarity-search pattern GraphRAG needs, similar in spirit to how a vector database handles similarity search on its own, per AWS’s Neptune Analytics documentation.
That distinction matters for the AI-agent framing specifically. Amazon Bedrock Knowledge Bases’ GraphRAG capability runs on Neptune Analytics, not the transactional Neptune Database product, a detail AWS keeps siloed across separate documentation pages rather than stating plainly in one place, as an independent technical walkthrough of the Bedrock-Neptune GraphRAG pipeline notes. A reader who only knows “Amazon Neptune” as one thing has no way to know which half of it their GraphRAG pipeline is actually running on.
Both products still sit on the storage side of a bigger distinction: the difference between a knowledge graph and a graph database, where the database stores and traverses connections and the knowledge graph adds semantic meaning on top. Neptune Database and Neptune Analytics both answer “how do I store and query a graph.” Neither answers “what does this connection mean, and who’s allowed to see it,” which is the layer this page returns to after the mechanics are settled. How that graph actually gets built in the first place, from source documents into vertices and edges, is a separate step from which product stores it; knowledge graph construction for AI already covers that step in detail, including an AWS Neptune plus Comprehend construction pattern.
| Aspect | Neptune Database | Neptune Analytics |
|---|---|---|
| Purpose | Transactional (OLTP) graph storage | In-memory analytics and GraphRAG substrate |
| Query languages | Gremlin, openCypher, SPARQL | openCypher, with native vector search |
| Data model | Property graph and RDF | Property graph, optimized for traversal plus similarity search |
| Typical use case | Fraud detection, Customer 360, recommendation engines | GraphRAG, agent long-term memory, graph analytics at scale |
| Scaling model | Instance-based, up to 15 read replicas | Serverless, capacity-unit-based |
Two products, one governance question
See how enterprises are thinking about the layer that governs context across every graph, warehouse, and lakehouse a team runs.
Get the AI Context StackCan Amazon Neptune power GraphRAG and AI agent memory?
Permalink to “Can Amazon Neptune power GraphRAG and AI agent memory?”Yes. Amazon Bedrock Knowledge Bases now offers what AWS calls one of the industry’s first fully managed GraphRAG capabilities, running on Neptune Analytics underneath, and separately, teams are pairing Neptune with the open-source memory framework Mem0 to give agents long-term, graph-structured memory.
The managed path is simple by design: point a Bedrock Knowledge Base at an S3 location, and Bedrock handles chunking and builds the GraphRAG retrieval workflow on Neptune Analytics, no separate graph-database setup required, according to AWS’s own GraphRAG build guide. Brad Bebee, General Manager of Amazon Neptune at AWS, frames the underlying reason graph retrieval matters at all: “If you think about vector similarity search as compared to graph search, in a vector, similarity is defined by mathematical distance in a really high dimensional space… You create a knowledge base using Amazon Bedrock, and you simply give it S3 location with documents; it does the chunking and builds the retrieval augmented generation workflow,” he told The Stack.
Beyond the managed GraphRAG path, teams pair Neptune with Mem0 to give agents company-wide, persistent memory structured as a graph rather than a flat vector store, per AWS’s machine learning blog. That pattern sits inside the broader question of what agent memory actually needs to persist across sessions, and how agent memory architectures choose between graph-structured and vector-only approaches; the best AI agent memory frameworks working with Neptune, including Mem0, sit inside that broader landscape rather than replacing it. Teams weighing vector store versus graph database for that memory layer, or comparing AI memory, RAG, and knowledge graphs more broadly, land on a similar answer either way: the storage pattern matters less than what governs what gets retrieved from it.
Whatever graph an agent’s memory runs on, something still has to decide what that agent is allowed to retrieve from it. That’s a separate layer from the graph engine itself, and it’s the layer this page comes back to once the production evidence and the tradeoffs are both on the table.
What are Amazon Neptune’s real-world use cases?
Permalink to “What are Amazon Neptune’s real-world use cases?”Beyond AWS’s own framing, three named production deployments show what “Graphs for AI” actually looks like in practice: Wiz’s cloud-security graph, Trend Micro’s Companion AI assistant, and Coinbase’s user-clustering system.
Wiz’s Security Graph, built on Neptune, analyzes “hundreds of billions of relationships,” according to AWS’s Database Blog, co-authored with Wiz CTO Ami Luttwak. The often-cited 40% figure sits one layer up from the graph itself: it’s the reduction in investigation time Wiz reports from the Bedrock-powered AI remediation guidance it built on top of that Neptune-backed graph, not from Neptune’s traversal alone, per AWS’s Wiz case study. Trend Micro’s Companion AI assistant saw a 20% improvement in answer quality after adopting Neptune with Bedrock. Shawn Tsai, Senior Architect at Trend Micro, put it this way in AWS’s published case study: “By using Amazon Neptune along with Amazon Bedrock, we can turn vast security data and knowledge gained over the years into actionable insights and democratize security best practices.”
Coinbase took a different route: it migrated its user-clustering system to Neptune Database specifically to handle interconnected data at scale, a Customer-360-adjacent use case rather than an AI-agent one, per AWS’s Database Blog. That detail matters because it shows the Graphs for AI positioning sits alongside Neptune’s older use cases rather than replacing them; Coinbase’s use case has nothing to do with GraphRAG at all.
Trend Micro’s result is the one that connects most directly to Neptune’s own claim of improving AI agent accuracy: better-connected data producing a measurably better answer is one concrete version of the broader argument that ungoverned context, not the model, is usually behind why AI agents fail in production and drive AI agent hallucination. But production evidence at three companies isn’t the same as a guarantee it will work the same way at a fourth, and that’s precisely where the honest tradeoffs later in this page come in.
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Take the Maturity AssessmentWhat’s the difference between Amazon Neptune and Neo4j?
Permalink to “What’s the difference between Amazon Neptune and Neo4j?”The two differ most in architecture. Neptune’s compute and storage are decoupled and fully managed by AWS, while Neo4j and its managed AuraDB use index-free adjacency with a more tightly coupled engine, and that architectural choice shows up in real, measurable tradeoffs rather than a simple better-or-worse answer.
Neptune’s decoupled compute and storage model is what deep AWS-ecosystem integration, S3, Glue, SageMaker, Bedrock, is built on top of; Neo4j’s more tightly coupled, JVM-based engine trades some of that elasticity for different latency characteristics, per FalkorDB’s comparison of the two architectures, a vendor-authored source with its own incentive to frame the comparison in FalkorDB’s favor, so treat the framing as informed but not neutral. The specific, commonly cited performance data point is more concrete: one community-reported benchmark, relayed via that same FalkorDB piece and originating from a Stack Overflow report rather than an independent or Atlan-verified test, measured openCypher queries on a 3.7 million-node graph running roughly 7 to 10 times slower on Neptune than on Neo4j AuraDB. That’s a single community-reported data point, not a settled, reproduced benchmark, and it shouldn’t be read as more certain than that.
This page doesn’t re-run the full feature-by-feature comparison. Two sibling pages already own that depth: Knowledge Graph Tools Compared covers the broader landscape, Neo4j, Stardog, Neptune, and GraphDB among them, and Neo4j GraphRAG vs. LlamaIndex vs. LangChain Graph Transformer covers the construction-library side of the Neo4j GraphRAG stack in detail. For teams evaluating Neo4j specifically for agent context graphs, that comparison goes deeper than this page needs to.
Teams actually building a graph from scratch, rather than choosing between vendors, are better served starting with how to build a knowledge graph for AI agents, which references Neptune as one storage option among several, or with the distinction a context graph draws from a knowledge graph before picking a database at all.
What are Amazon Neptune’s limitations?
Permalink to “What are Amazon Neptune’s limitations?”AWS’s “Graphs for AI” marketing doesn’t mention three tradeoffs that show up repeatedly in AWS’s own technical documentation and in independent practitioner reports: RDF inference depth, reported query latency, and how hard it is to move data back out of Neptune once it’s in.
Neptune’s RDF and SPARQL mode supports RDFS-level inference only, not full OWL 2 description-logic reasoning. That’s a stronger and more specific gap than it sounds: according to Amazon’s own Neptune Developer Guide, Neptune doesn’t natively materialize even RDFS inference without running an external reasoning step first, so a team expecting standard RDF reasoning out of the box needs to build that step themselves. The architecture tradeoff behind the reported latency numbers is related: Neptune’s decoupled compute and storage model, which enables its managed elasticity, is the same design choice one Reddit assessment called “a disjointed product that is a mashup of a bunch of AWS tools to emulate a graph database,” a single community view of a real architectural tradeoff, not a majority verdict or Atlan’s own opinion.
Migration and portability round out the list. Moving data out of Neptune is described as notoriously difficult, the tradeoff for the convenience of a fully managed service, per FalkorDB’s architectural comparison, a vendor-authored source, though the underlying complaint, deep AWS-ecosystem lock-in as the cost of that convenience, is a structural property of any fully managed service, not one this page can independently verify beyond that single account. None of this is a reason to avoid Neptune.
It’s a reason to know, before committing, that the semantic enforcement a semantic layer provides and the reasoning depth an ontology needs both sit above what Neptune’s RDFS-only mode natively delivers. That’s the same gap ontology-first design and ontology-versus-knowledge-graph thinking both address directly, and one that shows up again in how a metadata knowledge graph extends a raw graph database with the business context Neptune itself doesn’t track.
| What AWS’s positioning says | What the fuller picture is | Source |
|---|---|---|
| SPARQL 1.1 support for RDF graphs | RDFS-level inference only; no native RDFS materialization without an external reasoner, and no full OWL 2 reasoning | AWS Neptune Developer Guide, 2026 |
| Fully managed, elastic graph performance | One community-reported benchmark measured openCypher queries running roughly 7 to 10 times slower on a 3.7 million-node graph vs. Neo4j AuraDB | Stack Overflow report, via FalkorDB comparison, 2026 |
| Decoupled compute and storage for elasticity | The same decoupling is the architectural tradeoff one practitioner called “a disjointed… mashup,” versus Neo4j’s index-free adjacency | Reddit community assessment, 2026 |
| Deep AWS ecosystem integration | Migrating data out of Neptune is described as notoriously difficult, the lock-in side of that convenience | FalkorDB comparison (vendor-authored), 2026 |
How Atlan approaches governance for graphs like Amazon Neptune
Permalink to “How Atlan approaches governance for graphs like Amazon Neptune”Neptune’s RDFS-only inference gap, the same limitation that lets it read SPARQL but not reason over it without an external step, is one concrete example of a broader pattern: a graph database’s query languages describe how nodes connect, not what those connections mean to the business or who should be allowed to act on them. Those are two separate open questions Neptune’s marketing doesn’t answer, not one. The first is semantic: whether the graph carries the business meaning a person or an agent needs to interpret a connection correctly. The second is governance: what decides which parts of that graph an agent is allowed to retrieve from, and under whose policy. Neither is a Neptune-specific flaw; both sit above any graph engine, why AI agents need an enterprise context layer covers why, and it’s why the enterprise context layer sits above the graph rather than replacing it.
Atlan’s Enterprise Data Graph connects assets, lineage, business glossary terms, policies, and ownership into one governed graph that sits above whatever storage and traversal engine a team runs, Neptune or anything else. The business glossary layer addresses the semantic question, giving nodes and edges business definitions a raw graph database doesn’t carry on its own; it isn’t a substitute for RDFS or OWL reasoning, and doesn’t claim to be. A team can run Neptune underneath Atlan’s Enterprise Data Graph the same way a storage-and-traversal engine sits beneath a governed context-delivery layer.
The governance question is handled separately, through access policies and ownership rather than through the glossary. Atlan’s MCP server pattern delivers that governed context to AI agents the same way regardless of which underlying graph engine, including Neptune, a team runs, which is the same infrastructure question behind how to implement an enterprise context layer for AI and what context engineering actually means in practice. Teams asking whether their knowledge graphs are actually ready for that layer face the same test AI readiness versus knowledge graphs lays out, independent of which graph database sits underneath.
This isn’t a claim that Atlan replaces or competes with Neptune. Neptune is graph storage and traversal infrastructure; Atlan is the layer that governs what context gets delivered from it, the same relationship teams hit once they move past a single agent into a full AI agent harness. No customer has published a case study naming Neptune specifically, so the two accounts below speak to the same governed-context pattern in general, business glossary and MCP-delivered context at enterprise scale, rather than to Neptune by name.
See the context layer in action
Watch how Atlan governs the retrieval layer that graph databases like Neptune feed into, whatever query language sits underneath.
Watch the Demo SeriesThe graph engine question Amazon Neptune’s 2026 pitch doesn’t answer
Permalink to “The graph engine question Amazon Neptune’s 2026 pitch doesn’t answer”Amazon Neptune’s 2026 story is really two products under one name: Neptune Database for transactional graph storage, and Neptune Analytics for the in-memory, vector-search substrate that GraphRAG and agent memory actually run on through Amazon Bedrock Knowledge Bases. AWS’s own repositioning, from “general graph database” to “Graphs for AI”, is backed by real production evidence at Wiz, Trend Micro, and Coinbase, but it’s also honest to name what that pitch leaves out: RDFS-only inference, a reported openCypher latency gap against Neo4j, and AWS-ecosystem lock-in on the way out.
None of that makes Neptune the wrong choice. It means a graph that improves AI accuracy still needs a separate layer deciding what an agent can retrieve from it, a question that sits above the graph engine, not inside it, regardless of which vendor built the graph.
Put a number on the governance gap
See what a governed context layer above Neptune, or any graph, is worth before you scale agent workloads on top of it.
Calculate Your ROIFAQs about Amazon Neptune graph database
Permalink to “FAQs about Amazon Neptune graph database”1. What is Amazon Neptune used for?
Permalink to “1. What is Amazon Neptune used for?”Amazon Neptune is AWS’s fully managed graph database, repositioned in 2026 around “Graphs for AI”, GraphRAG and long-term AI-agent memory via Amazon Bedrock Knowledge Bases. It’s also used for Customer 360, fraud detection, and cybersecurity graphs. Production examples include Wiz’s Security Graph, Trend Micro’s Companion AI assistant, and Coinbase’s user-clustering system.
2. Is Amazon Neptune a NoSQL database?
Permalink to “2. Is Amazon Neptune a NoSQL database?”Yes, Neptune is a NoSQL, purpose-built graph database, not a relational database. It stores data as vertices and edges (property graph) or as RDF triples, and supports Gremlin, openCypher, and SPARQL as query languages rather than SQL.
3. What is the difference between Amazon Neptune and Neo4j?
Permalink to “3. What is the difference between Amazon Neptune and Neo4j?”The two differ mainly in architecture: Neptune uses a decoupled, fully managed compute and storage model, while Neo4j and AuraDB use index-free adjacency with a more tightly coupled engine. One community-reported benchmark found openCypher queries running roughly 7 to 10 times slower on Neptune than on Neo4j AuraDB on a 3.7 million-node graph, a real tradeoff, not a settled verdict either way.
4. Does Amazon Neptune support SPARQL?
Permalink to “4. Does Amazon Neptune support SPARQL?”Yes, Neptune supports SPARQL 1.1 for RDF graphs, alongside Gremlin and openCypher, all in one managed service. Its RDF mode supports RDFS-level inference only, and does not natively materialize even RDFS inference without an external reasoning step, nor does it support full OWL 2 description-logic reasoning.
5. What is Neptune Analytics vs. Neptune Database?
Permalink to “5. What is Neptune Analytics vs. Neptune Database?”Neptune Database is the original transactional graph store, built for high-throughput fraud detection, recommendation, and Customer 360 workloads. Neptune Analytics is the newer, in-memory analytics engine with native vector search, purpose-built for GraphRAG and graph analytics at scale, and it’s what Amazon Bedrock Knowledge Bases’ GraphRAG capability actually runs on underneath.
6. Can Amazon Neptune be used for GraphRAG?
Permalink to “6. Can Amazon Neptune be used for GraphRAG?”Yes, Amazon Bedrock Knowledge Bases offers a fully managed GraphRAG capability running on Neptune Analytics: point it at an S3 location and Bedrock handles chunking and builds the retrieval-augmented-generation workflow automatically. Teams also pair Neptune with the open-source Mem0 framework for graph-structured, persistent AI-agent memory.
7. What are Amazon Neptune’s biggest limitations?
Permalink to “7. What are Amazon Neptune’s biggest limitations?”Three come up most: its RDF mode supports RDFS-level inference only, not full OWL 2, and not even native RDFS materialization without an external reasoner; reported openCypher latency runs slower than Neo4j AuraDB in at least one community benchmark; and migrating data back out of Neptune is repeatedly described as difficult, the tradeoff for a fully managed, AWS-native service.
Sources
Permalink to “Sources”- Amazon Neptune product page, AWS
- Amazon Neptune Developer Guide, AWS Docs
- Amazon Neptune Features, AWS
- What is Neptune Analytics?, AWS Docs
- GraphRAG at Scale with Amazon Neptune and AWS Bedrock, Senzing
- Amazon Neptune: An overlooked AI accuracy game-changer? Interview with Brad Bebee, GM of Amazon Neptune, The Stack
- Company-wise memory in Amazon Bedrock with Amazon Neptune and Mem0, AWS Machine Learning Blog
- Build GraphRAG Applications with Amazon Neptune and Amazon Bedrock, AWS Builder Center
- The world is a graph: how Wiz reimagines cloud security using a graph in Amazon Neptune, AWS Database Blog
- Reimagining cloud security using Amazon Neptune graph database with Wiz, AWS case study
- Building an agentic AI solution using Amazon Neptune with Trend Micro, AWS case study
- How Coinbase provides trustworthy financial experiences through real-time user clustering with Amazon Neptune, AWS Database Blog
- AWS Neptune vs Neo4j: Architectural Differences for Production Workloads, FalkorDB
