Amazon Neptune Analytics
https://aws.amazon.com/neptune/
Vector index on graph nodes queryable via openCypher. Mem0 integration GA 2025; Cognee integration for agentic RAG. Combines semantic recall with multi-hop traversal in one managed service.
At a glance
- Type
- Vector + graph in one managed service
- Tier
- T1
- Section
- Knowledge-graph platforms
- Created
- 2023-11
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Pay-per-use
- Funding
- searched not found
Taxonomy
- storage
- graph
- retrieval
- graph-traversal
- persistence
- long-term
- update
- overwrite
- unit
- fact
- governance
- inspectable
- conflict
- overwrite
When to use
Optimised for: relationship modeling + reasoning + governance over pure vector
Anti-fit: not for purely-vector or simple-RAG use cases (graph adds setup cost)
Pros & cons
Pros
Managed graph database with vector + graph hybrid for AWS-native architectures; first-class integration with Bedrock and SageMaker.
Cons
AWS-only; vector + graph hybrid is newer than dedicated competitors; pricing complexity around graph queries.
Claims & capabilities
Native graph-memory + vector search managed by AWS.
Technical surface
- API surface
- REST, openCypher, Gremlin, SPARQL, SDK: AWS SDKs
- Backend storage
- custom (AWS Neptune managed)
- Deployment
- Managed-only
- Embedding model
- BYO
- Multi-tenancy
- hard-isolation
- MCP
- no first-party MCP adapter published as of 2026-05; community connectors may exist.
- A2A
- no Google A2A (Agent2Agent) integration documented as of 2026-05.
- OpenTelemetry
- first-class — CloudWatch / OTel
Compare Amazon Neptune Analytics with…
Similar systems
Other knowledge-graph platforms in the catalog, ranked by inbound references.
- Neo4j T1
Property graph DB with Cypher; native vector search; Aura Agent + MCP server for graph-as-memory. $100M GenAI investment in 2025.
- AllegroGraph (Franz) T1
RDF triple/quad store with RDFS++ / OWL reasoning. v8.4 (May 2025) added an NLQ interface.
- Apache AGE T2
openCypher over Postgres. Pairs with pgvector for graph + vector hybrid retrieval. Azure Database for PostgreSQL ships AGE with ai_extension LLM functions for entity extraction directly in SQL.
- ArangoDB T1
HybridGraphRAG combines vector search, graph traversal, full-text in one AQL query. ArangoGraphML for ML pipelines; LangChain integration.
- Dgraph T2
Vector indexing on any node. Google Gen AI Toolbox integration; LangChain agent orchestration.
- Diffbot T1
Auto-built knowledge graph from web crawl. 1T+ facts, 10B+ entities. GraphRAG-fine-tuned model based on open-source Llama 3.3.
Related systems
References (3)
- Cognee integrates with — Cognee integration for agentic RAG
- Mem0 integrates with — Vector index on graph nodes queryable via openCypher. Mem0 integration GA 2025
- Mem0 depends on at runtime — aph nodes queryable via openCypher. Mem0 integration GA 2025; Cognee integration for agentic RAG. Combines semantic
Referenced by (3)
- Mem0 builds on — AWS architecture: build persistent memory with Mem0 open source, Amazon ElastiCache for Valkey and Amazon Neptune Analytics
- Mem0 integrates with — Neptune Analytics Mem0 integration GA 2025
- Strands Agents Memory (AWS) builds on — Memory via Mem0 integration (ElastiCache for Valkey + Neptune Analytics)