Ontotext – Onto The Cloud

This is an introductory post that outlines Ontotext’s offering of GraphDB on AWS and Azure Marketplace and why enterprises should leverage its capabilities for building their semantic applications

January 16, 2024 3 mins. read Sumit Pal

Much awaited and long overdue, the only fully benchmarked graph database on the market, GraphDB, is now available on Amazon Web Services (AWS) Marketplace and Azure Marketplace ready for enterprise adoption. Companies can now develop, build, and deploy high-performance, scalable graph-based semantic solutions natively on the cloud to connect the dots of their enterprise data seamlessly.

GraphDB on AWS and Azure is a fully managed RDF graph database with all the capabilities of regular GraphDB that facilitates easy provisioning with development, operational efficiency, and productivity. It provides an easy and secure way for enterprises to empower their solutions with cutting-edge knowledge graph capabilities.

Why GraphDB on AWS and Azure?

To meet strong and growing demand from a wide range of customers wishing to run GraphDB in the cloud – Ontotext recently released GraphDB on AWS Marketplace and GraphDB on Azure Marketplace. The goal is to simplify the procurement, provisioning, and maintenance of clients’ graph-powered applications. GraphDB robust cloud-based solution, coupled with the underlying IaaS, provides better service RPO/RTO over on-premise solutions.

GraphDB on AWS and Azure meets organizations’ requirements for securing massive, highly connected datasets at a granular level without compromising performance. It accelerates KG adoption, making it easier for organizations to build and deploy semantic solutions.

This offering radically speeds time to value, enabling customers to get to production faster than the equivalent on-premises option. Organizations can now focus entirely on quickly building performant, graph-powered applications, without worrying about provisioning infrastructure and regular maintenance and upgrades. 

Key reasons why enterprises should consider using GraphDB on AWS and Azure include:

  • Deployment: Ease of provisioning and managing deployments.
  • Scalability: Handling of small to large-scale projects with various instance types and configurations out of the box.
  • Fully Managed: Managing of the underlying infrastructure, letting users focus on model creation.
  • Costs: Decrease of the time and effort to onboard GraphDB in big enterprises with many internal IT policies.
  • 24×7 Support: Premium support, ensuring customers have 24X7 uptime.

Some specifics of GraphDB on AWS and Azure include:

  • Service is delivered through a 12-Core cluster, deployed on 3 machines distributed in 3 availability zones, designed to cater to high availability for both production and non-production environments ensuring seamless 24/7 operations and support.
  • The default configuration is sized for 1.5B triples. There are two options for deployment: customer deployed as self-managed service in their account or partner deployed as GraphDB SaaS.
  • It supports any instance type, but the recommendation is to choose memory-optimized ones with the default being one that gives the best performance/price.

Some of the key capabilities, features and benefits are summarized in the mind map below.


GraphDB on AWS and Azure significantly speed time to market, delivers ease of procurement, and mitigates technical challenges with faster and smoother onboarding while delivering ease of acquisition for procurement teams. Finally, a true semantic graph and knowledge reasoning engine is available on the cloud!  This will streamline the adoption of Ontotext’s GraphDB solutions by providing customers with a consolidated purchasing environment and integration with their AWS and Azure accounts.

Do you need help to get your enterprise-ready graph database now?

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Strategic Technology Director at Ontotext

Sumit Pal is an Ex-Gartner VP Analyst in Data Management & Analytics space. Sumit has more than 30 years of experience in the data and Software Industry in various roles spanning companies from startups to enterprise organizations in building, managing and guiding teams and building scalable software systems across the stack from middle tier, data layer, analytics and UI using Big Data, NoSQL, DB Internals, Data Warehousing, Data Modeling, Data Science and middle tier.