Stardog is an enterprise knowledge graph platform that unifies all your data, including structured and unstructured sources, into a single, queryable network to accelerate analytics and power AI applications.
Stardog is an enterprise knowledge graph (EKG) platform designed to unify disparate and siloed data sources for large organizations. It leverages a semantic layer using W3C standards like RDF and OWL to model complex relationships and create a connected web of business knowledge. Stardog primarily serves data architects, data scientists, and chief data officers in data-intensive sectors such as life sciences, financial services, and manufacturing. Its core value proposition lies in providing a flexible, reusable data fabric that enables complex query answering and logical inference without the costly process of physically moving data. This 'virtual graph' capability, combined with its support for both graph and relational data models and built-in machine learning, sets it apart from traditional databases and other graph solutions.
Data architects, data scientists, data engineers, and chief data officers within large enterprises looking to unify complex data landscapes.
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2015
Arlington, USA
Stardog Cloud Free
A forever-free tier for individual developers and small projects. Includes a limited number of credits, models, and virtual graphs to explore the platform.
Free
Stardog Cloud
A fully-managed cloud solution for teams and enterprises. Offers dedicated compute, increased limits, advanced security, and enterprise support. Pricing is usage-based and customized.
Custom
Stardog Enterprise
A self-hosted offering for environments with specific security, compliance, or infrastructure requirements. Provides full control over the deployment. Pricing is based on an annual subscription.
Custom
A better choice for teams focused purely on property graph data structures and its mature Cypher query language, where semantic inference is not a primary requirement.
Choose TigerGraph when raw query speed for deep link analysis on massive datasets is the top priority, as it's built for high-performance graph analytics.
Ideal for teams heavily invested in the AWS ecosystem needing a fully-managed service that supports both SPARQL (RDF) and Gremlin (Property Graph) standards.
A strong alternative for use cases requiring a rigorous, logical schema and automated reasoning built into the core language (TypeQL) and database engine.
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