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Stardog Enterprise Knowledge Graph Platform transforms enterprise data infrastructure into a comprehensive end-to-end data fabric to answer complex queries across data silos.
4.2
Overview
Stardog, the industry's first cloud-native Enterprise Knowledge Graph platform, turns data into knowledge to power more effective digital transformations. Industry leaders including BNY Mellon, Bosch, and NASA use Stardog to create a flexible data layer that can support countless applications. With Stardog, customers reduce data preparation timelines by up to 90%. Stardog is the only data integration solution that combines graph storage, AI, and virtualization, allowing enterprises to answer their toughest questions. Stardog is a privately held, venture-backed company headquartered in Arlington, VA.
Highlights
- Modernize without disrupting legacy systems
- Seamlessly support multiple apps and data models
- Scale efficiently with virtualization
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Stardog Single Node - New VPC
Stardog Single Node - Existing VPC
64-bit (x86) Amazon Machine Image (AMI)
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Try this product free for 30 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
m5.2xlarge Recommended | $5.553 |
t2.medium | $5.553 |
r5.xlarge | $5.553 |
t2.large | $5.553 |
r5.2xlarge | $5.553 |
m5.xlarge | $5.553 |
m5.large | $5.553 |
t2.xlarge | $5.553 |
r5.large | $5.553 |
Dimensions summary
You pay by the hour based on the AWS EC2 instance size you run. The nine options map to standard instance families: t2 (medium, large, xlarge) for general-purpose burstable workloads, m5 (large, xlarge, 2xlarge) for balanced compute and memory, and r5 (large, xlarge, 2xlarge) for memory-heavy work. Larger instances add more CPU and memory, so the hourly rate rises with size. There is no upfront commitment. You choose the instance that fits your workload, and you can start or stop usage as needs change.
Top-of-mind questions for buyers
The t2 family gives burstable general-purpose compute for lighter or intermittent work. The m5 family balances compute and memory for steady workloads. The r5 family adds more memory per core, which suits memory-heavy graph queries and larger datasets. Larger sizes within each family add CPU and memory.
You pay the hourly software rate only while the instance runs. Stopped instances stop accruing software charges. Underlying AWS storage tied to a stopped instance may still incur separate AWS fees. Restarting resumes hourly software billing. There is no upfront commitment, so you meter running time only.
You get a knowledge graph platform that connects and queries data across sources. It includes virtual graphs, an inference engine, built-in machine learning, an integrated development studio, data quality tools, and a graph database. These capabilities apply across every instance option; the instance you pick affects compute and memory, not features.
www.stardog.com
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Release notes can be found here: https://www.stardog.com/docs/release-notes
Additional details
Usage instructions
Instructions can be found in: https://docs.stardog.com/install-stardog/aws-marketplace
Resources
Vendor resources
Support
Vendor support
Please visit Stardog Community Forum at info.stardog.com/community where you can find answers to your questions from our engineers and support team. Our community forum is also a great place for you to provide any feedback on our latest releases and platform. You can also find answers to common questions and more in Stardog's Documentation (info.stardog.com/docs).
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Functionality
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AI generated from product descriptions
Graph Storage Architecture
Cloud-native platform with graph storage capabilities for organizing and querying interconnected enterprise data
Data Virtualization
Virtualization technology enabling efficient scaling and access to data across multiple sources without physical data movement
AI-Powered Integration
Artificial intelligence capabilities integrated into the data integration solution for enhanced query processing and data understanding
Multi-Model Data Support
Support for multiple data models and applications simultaneously within a unified data fabric architecture
Legacy System Compatibility
Ability to modernize data infrastructure while maintaining compatibility with existing legacy systems and applications
Distributed Graph Database Architecture
Native distributed graph database designed to handle both real-time analytics and transactional workloads at scale
Query Performance and Scalability
Capable of executing over 100,000 deep link queries per second on a single machine with support for scaling to tens of terabytes of data
Managed Database Service
Fully managed cloud database service eliminating requirements for server configuration, backup management, and security patch administration
Multi-Query Language Support
Support for GSQL, GQL, and openCypher query languages with API-first architecture for integration with DevOps and data pipelines
Pre-built Solution Templates
Includes pre-built Starter Kits and Solution Kits with ready-to-use schemas, queries, and dashboards for fraud detection, customer intelligence, and cybersecurity use cases
Lakehouse Integration
Seamless integration with Apache Iceberg, Apache Hudi, Delta Lake, Hive, and MySQL without requiring ETL processes or data duplication
Auto-Sharding Architecture
Data auto-sharding with separated compute and storage layers enabling automatic scalability
Graph Query Engine
Query engine that transforms tabular datasets into graph structures for analysis without additional data movement
Low-Latency Query Processing
Optimized query performance for complex graph traversals such as multi-hop neighbor queries
Data Lake Compatibility
Support for querying existing data lake tables directly as graphs while maintaining original data storage format
Standard contract
No
No
Customer reviews
reviewer2866221
Voice search has simplified querying and AI modeling now accelerates secure data insights
Reviewed on Jun 29, 2026
Review provided by PeerSpot
What is our primary use case?
Stardog Enterprise Knowledge Graph Platform is a knowledge graph platform where we store triples, which are RDF compliant data in the database. In the project, we have a home comfort domain with different appliances. We store all the home comfort data in Stardog Enterprise Knowledge Graph Platform.
What is most valuable?
One of the features is the LLM-based voice box, which allows you to talk to your database using natural language. This LLM-based feature means you do not need to write all the SPARQL queries, those large queries. When you tell the LLM in natural language what fields and data you need from the triples, you can retrieve it using natural language without writing thousands of lines of query.
I would add information about ontology. In Stardog Enterprise Knowledge Graph Platform, you can create the ontology and the model. The model can now be created with the help of LLM as well. If you want to create a model, you can share the use case, stating the different data such as product, product customers, and sales count. You can give the data in a CSV file and provide the LLM with the use case, and it will create an ontology for you.
Stardog Enterprise Knowledge Graph Platform is highly secure because the answers are not hallucinated. The data comes from your database only, not from the LLM trained data. I am confident that the LLM-based capability of Stardog Enterprise Knowledge Graph Platform is excellent. They have beautifully built these AI capabilities, and they work very well.
What needs improvement?
I found that some of the documents are not up to date. Sometimes they update the software, but they do not update the documentation on time. This is one of the drawbacks I have seen.
Another software called Neo4j provides some more features compared to Stardog Enterprise Knowledge Graph Platform. That is the reason I have given a lower rating to Stardog Enterprise Knowledge Graph Platform. We have lost a few customers, but now Stardog Enterprise Knowledge Graph Platform has also started offering the ability to scrape entities and relationships from unstructured data, such as PDF and DOC files. This feature was not available earlier, but now they are providing it.
For how long have I used the solution?
I have been using Stardog Enterprise Knowledge Graph Platform for more than four years.
What do I think about the stability of the solution?
Stardog Enterprise Knowledge Graph Platform is very stable.
What do I think about the scalability of the solution?
It is very highly scalable. You can store a large amount of data.
How are customer service and support?
The customer service is very good. The only drawback is that they are located in the US right now. Recently, they have opened an office in India during IST hours. They provide very good support.
Which solution did I use previously and why did I switch?
We have used another graph database platform called Anzo. We had security issues and features issues with Anzo. The main reason was the security, and we had a lot of trouble with it. That led us to move from Anzo. Anzo is from the Cambridge Semantics stack. We moved from Anzo to Stardog Enterprise Knowledge Graph Platform.
How was the initial setup?
The initial setup is very good and cheap. The setup required very little.
What was our ROI?
We do save money from Stardog Enterprise Knowledge Graph Platform. We have compared between different graph databases, and we found that you can get Stardog Enterprise Knowledge Graph Platform Enterprise license compared to other graph databases for a much lower price.
What's my experience with pricing, setup cost, and licensing?
The pricing is very less compared to other graph databases. For setup cost, all you need is a VM. If you have a VM, you can set up Stardog Enterprise Knowledge Graph Platform easily.
Which other solutions did I evaluate?
We have evaluated Ontotext GraphDB.
What other advice do I have?
I recommend using the AI capabilities more, as that will help you develop faster and more accurately. Using AI in Stardog Enterprise Knowledge Graph Platform is very useful. I would rate this review an 8 overall.
Bhaumik Ganatra
Rich graph queries have streamlined reporting but support still needs improvement
Reviewed on Jun 08, 2026
Review provided by PeerSpot
What is our primary use case?
My main use case for Stardog Enterprise Knowledge Graph Platform was to keep our data in graph format in my previous organization, wherein the entire data model which I used in Power BI included Stardog as one of the data sources. While creating the data model, I was using Stardog Studio to modify or retrieve the actual data that I wanted in my Power BI model. I used to fire SPARQL queries in Stardog so that I could get my result.
A quick, specific example of how I used Stardog Enterprise Knowledge Graph Platform in one of my Power BI projects is that Stardog is considered a very robust platform, a graph platform, which was used in my organization to store graph format data. I used it, and while connecting it with Power BI, I was using an ODBC connector, such as the BI connector that is given with Stardog. That was the method of how I used it.
What is most valuable?
In my experience with Stardog Enterprise Knowledge Graph Platform, many graph platforms do not have a very user-friendly client experience. What I found was that Stardog Studio had a very rich client experience in which we were able to fire queries and do many things through drag-and-drop functionality. That was one of the best features I found in Stardog.
The rich client experience and drag-and-drop functionality in Stardog Studio made my work easier because my main use case to use Stardog was to retrieve the data which I needed in my Power BI data model. I was using it to fire SPARQL queries and get the exact data that I wanted. One of the best features that I liked was whenever I fire a query, the query runs quicker because I am only getting the top 100 or top 1000 records for my result. If I wanted the full data in my result, I just needed to tweak it a bit and then I would get my full result. The other feature was being able to export that data in any format that I need.
Stardog Enterprise Knowledge Graph Platform has positively impacted my organization since it has been used for more than four to five years, and everyone has been positively affected by the use of it. It was a tool that was used to store our very complex data in the form of data structure, and it was efficiently managed by Stardog.
What needs improvement?
I don't know what can be improved in Stardog Enterprise Knowledge Graph Platform since I was not working as a main developer in the Stardog team. For me, it was all acceptable when I used Stardog.
Maybe support for Stardog Enterprise Knowledge Graph Platform can be better, but there is nothing else I would mention.
For how long have I used the solution?
I have been using Stardog Enterprise Knowledge Graph Platform for around two years ago and had been using it till six months ago. That was in my previous role.
What do I think about the stability of the solution?
In my experience, Stardog Enterprise Knowledge Graph Platform can be stable.
What do I think about the scalability of the solution?
Stardog Enterprise Knowledge Graph Platform handles increasing data volumes well.
How are customer service and support?
There was a difficult scenario when I was first connecting Stardog Enterprise Knowledge Graph Platform with Power BI through ODBC connector, because at that time there was an issue where the actual connector port was not enabled from my client's side. When we asked this particular query to Stardog support team, they were not able to resolve it for months. Around three to four months they were not able to resolve this query, so it was a very difficult task to connect Power BI with Stardog ODBC.
My experience with customer support for Stardog Enterprise Knowledge Graph Platform was a bit weak because the problem that we told Stardog support was very basic, and they were not able to get an answer to that for months. It is considered weak support.
What other advice do I have?
I would rate Stardog Enterprise Knowledge Graph Platform around seven out of ten.
I chose seven out of ten because I have heard from my colleagues who were working on Stardog development that there were some features missing or it could be a bit better. The type of problem that I faced in the support has taken out three points. My overall review rating for Stardog Enterprise Knowledge Graph Platform is seven out of ten.
LeAnn W.
Amazing
Reviewed on Apr 16, 2026
Review provided by G2
What do you like best about the product?
The easy and precise way the program organizes data is simply amazing.
What do you dislike about the product?
There is a lot to learn but it is user friendly
What problems is the product solving and how is that benefiting you?
It is allowing us to input multiple sources of data and it's flexible design makes it easy.
Prashanth D.
Stardog Graph Database
Reviewed on Sep 17, 2023
Review provided by G2
What do you like best about the product?
Two things I like about stardog:
1) Stardog gives support for Semantic Data support ( RDF & OWL support). Also give responses in short time.
2) It allows easy to integrate data with graphs and also provides Virtualization.
1) Stardog gives support for Semantic Data support ( RDF & OWL support). Also give responses in short time.
2) It allows easy to integrate data with graphs and also provides Virtualization.
What do you dislike about the product?
Initial setup was complex and time consuming. UI was not friendly. Server Issues like crashes and downs are also experienced sometimes.
What problems is the product solving and how is that benefiting you?
I have integrated my private LLM with knowledge base graphs. Stardog is used as graph database, using sematic search retrives the related data. Therefore stardog knowledge graphs helps me to integrate with LLM.
Gokul N.
Powerful Knowledge graph platform
Reviewed on May 26, 2023
Review provided by G2
What do you like best about the product?
Supports RDF (Resource Description Framework) and OWL (Web Ontology Language), which are standard semantic web technologies. It also allows to import, store, and reason over RDF data, making it suitable for building intelligent applications.
What do you dislike about the product?
Requires high computing power for large scale data loads. Pricing is also on the higher side. May require a learning curve for users to fully leverage its potential.
What problems is the product solving and how is that benefiting you?
It enables the integration of disparate data sources by providing a unified platform for storing and querying knowledge graphs. It brings data from different domains, formats, and systems into a single coherent knowledge graph, facilitating data interoperability and integration.