Stardog

Stardog is an enterprise graph platform that connects distributed data through a semantic layer. It suits teams building knowledge graphs or AI, analytics, search, and application workflows across varied sources. Its strongest case is combining virtualization with inference and graph tooling; the free plan is useful for a bounded start, but lacks several operational safeguards.

Overview

Stardog lets organizations query on-premises and cloud data without first moving or copying it. That makes it a practical option when information is spread across systems and teams need a shared semantic view. It is a specialized platform for graph-centered data work, rather than a general-purpose database for every workload.

Deployment can be managed as an AWS cloud service, run in a customer cloud environment, or hosted on premises through Docker, Kubernetes, AWS Marketplace, or Azure Marketplace. Stardog was founded in 2006 and is headquartered in New York. Its published SOC 2 Type I report covers controls relevant to systems containing Stardog Cloud customer data.

Key features

Virtualization and graph standards

Stardog’s virtualization layer accesses disparate data where it resides, avoiding a separate copy as the prerequisite for querying across sources. Connectors cover Snowflake, BigQuery, Oracle, PostgreSQL, MongoDB, Elasticsearch, Salesforce, Jira, Splunk, and REST sources. The breadth is useful for organizations with mixed environments, although the Free plan does not include the full range of connectors.

Support for SPARQL, SPARQL*, GraphQL, and W3C RDF standards makes the platform a natural fit for teams working with semantic models and knowledge graphs. SQL querying, API access, and scheduled queries broaden the ways applications and users can work with the data. Official BI integrations include Tableau, Power BI, cumul.io, Apache Superset, Siren, IBM Cognos, Metabase, and RapidMiner.

Inference, quality, and AI

The Inference Engine applies business rules at query time and exposes the logic behind its results. That explainability can help teams inspect how modeled rules affect an answer, rather than treating inferred relationships as opaque. SHACL constraints provide a way to validate data and flag inconsistencies across silos, which is valuable when a shared graph depends on keeping source data coherent.

Stardog Voicebox uses large language models to assist with knowledge-graph modeling, maintenance, and query writing. Built-in machine learning also supports similarity search and predictive analytics. These features put AI-assisted graph work alongside the core semantic layer, though the product’s central appeal remains connecting and querying enterprise data rather than being a standalone AI assistant.

Scale and operations

The graph database supports up to 100 billion data points per installation, with scale-out deployments up to 1 trillion triples. Those ceilings indicate a platform aimed at substantial enterprise graphs. Query result caching is supported, but Free excludes caching, high availability, backups, and LDAP integration, so its capabilities should not be mistaken for the operational package needed in production environments with those requirements.

Enterprise licenses include base support, a dedicated Customer Success representative, and access to Solutions Architects. The Enterprise plan also includes premium support, a 99.9% uptime SLA, and daily backups retained for 30 days. Those provisions make the paid tier more appropriate for operationally critical deployments.

Pricing

Stardog uses a freemium model. Free is listed as free and renews as a 1-year license. It allows up to 1 million stored edges across 3 databases and includes community support. Commercial use is permitted, making it a meaningful way to build or evaluate a smaller graph, but the edge cap and exclusions for high availability, caching, backups, LDAP, and the full connector set constrain its role as a production service.

Stardog Enterprise has custom pricing, with a custom stored-edge limit and unlimited databases. It adds premium support, a 99.9% uptime SLA, and daily backups retained for 30 days. Organizations that need those protections, broader connector access, or more than the Free plan’s fixed limits should expect to discuss a tailored deployment rather than rely on the entry tier.

Platforms

Stardog is available through API, Linux, macOS, Windows, and the web, with both cloud-hosted and self-hosted deployment options. The deployment range suits organizations with different infrastructure preferences, from a managed AWS service to customer-cloud or on-premises installations.

Who it's for

Stardog is best suited to enterprise data teams that need a semantic layer over sources they cannot or do not want to consolidate, especially where graph modeling, rules-based inference, data validation, or graph-backed AI and analytics matter. Its standards support and broad connector coverage make it a stronger fit for organizations already committed to graph-oriented data work than for teams seeking only a straightforward SQL federation layer.

Smaller teams can use Free commercially, but should treat its 1 million-edge ceiling and 3-database limit as real design constraints. If high availability, caching, backups, LDAP integration, or the complete connector set is necessary, Enterprise is the relevant tier. Organizations that do not need semantic modeling or inference may find Stardog’s specialized graph capabilities unnecessary.

Pros and cons

• Pro: Virtualization can query data across cloud and on-premises sources without moving or copying it, which helps preserve distributed architectures.
• Pro: SPARQL, SPARQL*, GraphQL, and W3C RDF support serve teams building standards-based semantic and graph applications.
• Pro: Query-time inference shows the logic behind results, while SHACL validation helps flag inconsistencies across data silos.
• Pro: Free allows commercial use and includes 3 databases, giving teams room to develop within a defined edge limit.
• Con: Free stops at 1 million stored edges and excludes high availability, caching, backups, LDAP, and the full connector set.
• Con: Enterprise pricing is custom, so organizations needing its operational safeguards must plan for a sales-led purchase rather than a transparent fixed price.

Alternatives

For a wider category comparison, explore Data Virtualization Software, Data Federation Software, RDF Databases, and Knowledge Graph Software.

• Query Streams is worth considering for a freemium option with a small free allowance: its Free plan provides 5 MB/month of query traffic, 1 data source, and capped scheduled-push and API or federate rows.
• Teradata VantageCloud is an alternative if you want a platform centered on Teradata Database and ClearScape Analytics with Open Analytics Framework; its VantageCloud Lake Standard plan uses custom pricing.
• Apache Drill suits those seeking free, downloadable software under Apache License 2.0, with embedded or cluster deployment.
• Denodo Platform may fit developers who want a free single-server Developer plan with 4 cores, 50 data products, 2.5 TB/year, and unlimited data sources, consumers, and Data Marketplace users.
• Spice.ai OSS is a free alternative distributed as an Apache 2.0-licensed self-hostable binary or Docker image.
• PrestoDB is a free, self-hosted open-source SQL query engine.
• Starburst Galaxy offers a Free plan forever with up to 3 clusters and standard execution for ad hoc queries, making it a potential fit for that bounded use case.
• Teiid is a free open-source data virtualization project.

Verdict

Choose Stardog if your organization needs to connect varied data through a semantic layer and values graph standards, explainable inference, and AI-assisted graph work. Its deployment flexibility and enterprise-scale graph limits strengthen that case. Look elsewhere if the Free plan’s edge cap and operational exclusions are unacceptable, or if your needs do not justify a specialized graph platform and custom-priced Enterprise tier.