GraphDB
6.5#13 in knowledge graph softwareFree planFree planWebAPILinuxmacOS
VisitGraphDB vs Stardog
What both publish
| Fact | GraphDB | Stardog |
|---|---|---|
| AI features | GraphDB lists GraphRAG, MCP support, no-code RAG chat in Workbench, precision entity linking, and natural-language graph queries from LangChain. | Stardog Voicebox uses large language models for knowledge-graph modeling, maintenance, and query writing, while built-in machine learning provides similarity search and predictive analytics. |
| Headquarters | No single head office; main offices are in Sofia, Vienna, and New York City | New York, New York, United States |
| Integrations | The page lists MongoDB, Kafka, JDBC, GraphQL, SQL-accessible data virtualization, and connectors for Lucene, Elasticsearch, and Solr. | Connectors cover databases including Snowflake, BigQuery, Oracle, PostgreSQL, MongoDB, Elasticsearch, Salesforce, Jira, Splunk, and REST sources. |
| Purpose | GraphDB is a semantic graph database supporting RDF and SPARQL for linking fragmented business data and powering real-time AI insights. | Stardog connects enterprise data into a semantic layer for AI, analytics, search, and applications. |
GraphDB plans
- GraphDB FreeFree
- GraphDB EnterprisePriced on request
Stardog plans
- Stardog Cloud FreeFree
- Stardog FreeFree
- Stardog Cloud EnterprisePriced on request
- Stardog EnterprisePriced on request
Pick GraphDB if…
GraphDB suits teams that need standards-based semantic data modeling, RDF/SPARQL querying, and reasoning across connected information. Enterprise is aimed at organisations requiring larger core counts, clustering, failover, load balancing, or commercial support.
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.