Rocketgraph: In-Memory Graph Analytics Platform
Rocketgraph helps enterprises and government agencies analyze complex relationships across large datasets. It is designed for use cases such as fraud detection, anti-money laundering, and vulnerability management.
At a glance
- Category
- Data & Analytics
- Best for
- Enterprise companies, Federal government agencies, Cybersecurity analysts, Financial crime investigators
- Pricing
- Rocketgraph uses a pay-as-you-go billing model. A 30-day free trial is available.
- Key use cases
- Fraud Detection, Anti-Money Laundering (AML), Vulnerability Management, Knowledge Graph Creation, Path Finding
- Integrations
- Oracle, MongoDB, Databricks, Snowflake, TigerGraph
- Official website
- rocketgraph.com
Rocketgraph is an in-memory graph analytics platform designed to handle large datasets, including hundreds of billions of nodes and edges. Rather than acting as a traditional database, it operates as an analytics engine that supports deep traversals and graph-wide scans to help users identify patterns.
The software is designed for organizations and government agencies operating in environments such as cybersecurity, financial crime investigation, and infrastructure management.
Through an interface called Mission Control, the platform allows analysts to ask questions in natural language. The system then generates Cypher queries, which may reduce the need for specialized data science expertise to perform complex analysis.
Buyers can choose between a fully-managed cloud service called Aurora or an on-premise deployment for specific security or infrastructure requirements.
Key Features
Operates in-memory to reduce latency bottlenecks associated with disk-based systems.
A natural language interface that helps translate plain-English questions into Cypher queries and schemas.
Supports searching the entire graph without requiring sampling or partitioning.
Supports connections to large language models including OpenAI, Anthropic Claude, AWS Bedrock, and private on-premises models.
Designed to manage datasets consisting of hundreds of billions of nodes and edges.
Available as a fully-managed cloud service (Aurora) or via on-premise installation.
Use Cases
Building entity-centric graphs of customers and devices to identify anomalous links and mule clusters.
Resolving entities across payments and watchlists to expose circular flows and intermediaries.
Mapping assets and SBOMs to exploit signals to prioritize items based on business impact.
Developing knowledge graphs to enhance organizational data offerings.
Using Breadth First Search features to identify specific paths within a network.
Best For
Integrations
Pricing
Rocketgraph uses a pay-as-you-go billing model. A 30-day free trial is available.
FAQ
No, it is an in-memory graph analytics platform designed to act as an accelerator for existing workflows rather than a primary database.
When using agentic query and schema features, only data labels are sent to the LLM, not the actual record values.
Aurora is a fully-managed cloud service, while on-premise deployment is for organizations with existing hardware footprints or strict security requirements.
Yes, it is designed for scale and has been used for graphs ranging from a few thousand edges to hundreds of billions.
Source category: Data & Analytics
Source subcategory: Graph Analytics
