RDFox
7.8#2 in rdf databasesFree planFree planWebAPILinuxmacOS
VisitRDFox vs Stardog
What both publish
| Fact | RDFox | Stardog |
|---|---|---|
| Deployment | RDFox can run on a single machine or as a cluster distributing processing across multiple machines. | Stardog can run as a managed AWS cloud service, in customer cloud environments, or on premises through Docker, Kubernetes, AWS Marketplace, and Azure Marketplace. |
| Documentation depth | The documentation includes user guides, REST and Java API references, Javadoc, shell instructions, operations manuals, high availability and Docker deployment chapters. | Official documentation includes administration, querying, virtualization, inference, constraints, tutorials, API references, examples and application-building guidance. |
| Founded | 2017 | 2006 |
| Headquarters | Oxford, United Kingdom | New York, New York, United States |
| Integrations | RDFox was made available on AWS Marketplace through a DataLens partnership with pay-as-you-go deployment and connectors for SQL, CSV, XML, JSON, REST and unstructured data. | Connectors cover databases including Snowflake, BigQuery, Oracle, PostgreSQL, MongoDB, Elasticsearch, Salesforce, Jira, Splunk, and REST sources. |
| Rating on Capterra | 0.0 out of 5 from 0 reviews | 5.0 out of 5 from 7 reviews |
| Rating on G2 | 4.8 out of 5 from 14 reviews | 4.2 out of 5 from 18 reviews |
| Rating on GetApp | 0 out of 5 from 0 reviews | 5.0 out of 5 from 7 reviews |
RDFox plans
- Free evaluationFree
- RDFox free evaluation licenseFree
- AWS Marketplace RDFoxPriced on request
- RDFox commercial licensePriced on request
Stardog plans
- Stardog Cloud FreeFree
- Stardog FreeFree
- Stardog Cloud EnterprisePriced on request
- Stardog EnterprisePriced on request
Pick RDFox if…
RDFox may suit teams building RDF knowledge-graph systems that need rule-based reasoning, APIs, or deployment across different environments. Its evaluation license is limited to evaluation and non-commercial use.
Pick Stardog if…
Stardog suits organizations that need to unify data across cloud and on-premises sources for analytics, AI, search, or applications. It is also relevant to teams that need graph-based data modeling, standards-based querying, and deployment options spanning managed cloud and self-hosting.