Cloud for industry: sovereign, scalable, and resilient

Mechanical and plant engineering is in the midst of digital transformation. CAD systems, simulations, digital twins, Industry 4.0 applications, and intelligent production processes generate continuously growing volumes of data and require powerful computing resources. At the same time, companies must protect their technological know-how, shorten development cycles, and securely interconnect existing production facilities, robotics, machine controls, and IT systems.

T Cloud Public supports manufacturing companies with a scalable platform for engineering, simulations, and Industry 4.0 applications. CPU, GPU, and bare-metal resources are available on demand and support compute-intensive workloads as well as AI applications and industrial computing. European data centers and open technologies such as OpenStack facilitate the controlled modernization of existing IT landscapes and strengthen digital sovereignty.

Companies in the chemical, materials, metals, paper, and food industries continuously process large volumes of production, process, energy, and sensor data. At the same time, requirements for automation, resource efficiency, sustainability, and the secure processing of business-critical information are growing. Industry 4.0 concepts connect production facilities, sensors, and intelligent applications, creating the foundation for near-real-time data-driven decisions.

T Cloud Public provides the foundation for scalable data platforms, industrial AI, and modern automation solutions. Cloud-based services complement existing production systems, while time-critical controls can continue to be operated locally or at the edge. This creates an integrated architecture that meaningfully connects local production and scalable cloud resources.

Compute, storage, database, messaging, and AI services create a powerful foundation for modern industrial applications. Companies can centrally store data, analyze industrial processes, develop intelligent automations, and make production data usable for industrial AI applications. Open cloud technologies support flexible integration of existing systems and reduce dependence on proprietary platforms.

The automotive industry is increasingly evolving into a data-driven ecosystem. Software-defined vehicles, connected car services, digital customer applications, and intelligent production processes generate continuously growing volumes of development, vehicle, and user data. At the same time, requirements for data protection, information security, and control over sensitive information are increasing. Particularly for new digital services, AI applications, and data-driven business models, the question of where data is processed and which legal framework applies is becoming increasingly important.

As a European cloud platform, T Cloud Public complements existing cloud, multi-cloud, and on-premises landscapes with a sovereign infrastructure for sensitive and strategic workloads. High-performance compute, GPU, bare-metal, and platform services create the foundation for connected cars, data-driven services, AI applications, and modern development processes, while open technologies facilitate integration of existing systems and reduce long-term dependencies.

T Cloud Public complements existing cloud strategies with a sovereign cloud from Europe for sensitive data, new digital services, and AI-based innovations. Powerful infrastructure and platform services enable new workloads to be deployed quickly without replatforming existing architectures. Open technologies promote technological independence and reduce long-term vendor dependencies.

Pharmaceutical companies and life sciences organizations process large volumes of research, laboratory, production, and quality data. At the same time, requirements for data integrity, documentation, information security, and the controlled development of new drugs and medical devices are increasing. Cloud computing helps companies flexibly provision compute-intensive applications, efficiently analyze research data, and accelerate digital development processes.

T Cloud Public provides a powerful platform for research, development, and data-intensive analytics. Compute, GPU, storage, database, and container resources can be flexibly combined and support scientific simulations as well as AI-driven analyses. Open technologies facilitate the integration of existing laboratory, research, and quality systems into hybrid cloud architectures.

T Cloud Public combines compute, GPU, storage, networking, container, database, big data, and AI services into a powerful cloud platform for pharma and life sciences. Companies can flexibly expand research environments, build data platforms, and operate compute-intensive simulations cost-effectively. European data centers, open cloud technologies, and extensive security certifications create a solid foundation for modern research and development processes.

Research institutions and scientific organizations in Europe that meet the applicable eligibility criteria can obtain T Cloud Public cloud resources at preferential rates through the OCRE framework agreement.

Forschungseinrichtungen und wissenschaftliche Organisationen in Europa können bei Erfüllung der jeweiligen Förderkriterien Cloud-Ressourcen der T Cloud Public vergünstigt über den OCRE-Rahmenvertrag beziehen. 

No. T Cloud Public provides a public cloud platform with infrastructure and platform services. These include compute, storage, networking, containers, Kubernetes, databases, big data, AI, and backup services, among others. ERP, MES, PLM, LIMS, or QMS applications can be operated on this cloud infrastructure but are provided and managed by the respective company or a software partner.

Suitable applications include industrial data platforms, simulations, HPC workloads, development and test environments, IoT applications, AI models, digital twins, and backup and disaster recovery solutions. Whether an application should be operated in a public cloud depends on its protection requirements, latency, availability, data volume, and dependencies on existing production and OT systems.

Safety-critical controls, real-time systems, and applications tightly coupled to production facilities require individual assessment. Depending on the use case, a hybrid cloud architecture may be more appropriate, in which time-critical systems are operated locally or at the edge while data platforms, analytics applications, or AI workloads use the public cloud.

No. T Cloud Public can provide the technical infrastructure for such systems. Whether an existing application is cloud-ready must be assessed based on its architecture, vendor approval, licensing, interfaces, and operational requirements.

A hybrid cloud is appropriate when production facilities, OT systems, or safety-critical applications must be operated locally, while development environments, data platforms, or industrial AI applications run flexibly in the public cloud. This allows existing investments to be leveraged while integrating modern cloud technologies.

Production and OT systems should be connected via segmented networks, controlled interfaces, and clearly defined data flows. Identity management, encryption, logging, and restrictive access controls help securely connect production networks and cloud applications.

No. The cloud provides additional compute, storage, and network resources flexibly. However, an application must be technically designed to efficiently use additional resources. Only the combination of cloud architecture and application design enables high scalability.

High availability is achieved through appropriate architecture. Applications can be distributed across multiple availability zones, secured by load balancers, and automatically scaled as needed. In addition, backup and disaster recovery concepts help minimize downtime and data loss.

Backups are used to restore data. Disaster recovery additionally encompasses the organizational and technical measures used to restore complete applications or production processes within defined recovery time objectives following a major outage.

No. The provider's certifications and security services can support implementation and documentation efforts. The affected company remains responsible for risk management, governance, incident processes, supply chain security, business continuity, and the secure configuration of its own systems, among other things.

No. The TISAX certificate describes the assessed scope of the provider. The automotive customer must still assess their own scope, processes, access controls, applications, data flows, and vendor management. The provider's certificate is one component of the overall assessment.

In principle, a suitable cloud infrastructure can be part of a GxP-regulated overall system. Prerequisites include, among others, a supplier assessment, infrastructure qualification, application validation, documented processes, and appropriate measures for data integrity, backup, and recovery. Responsibility for validation remains with the regulated company.

T Cloud Public provides the agreed infrastructure and the respective platform services. For IaaS workloads, the customer is responsible for operating systems, applications, patches, user permissions, network configurations, monitoring, and the use of backup and security services, among other things. For managed or platform services, the division of responsibilities shifts but must be reviewed specifically for each service.

Production, quality, machine, and sensor data can be consolidated on a cloud data platform and used for analytics, forecasting, or AI applications. T Cloud Public provides compute, GPU, storage, and AI resources for this purpose. The development, training, and technical validation of models is carried out by the company or a specialized partner. High-performance GPU resources are available for compute-intensive AI workloads and can be used, for example, for NVIDIA-based training and inference scenarios.

Open technologies such as OpenStack, Kubernetes, containers, and standardized APIs facilitate portable cloud architectures and reduce dependence on individual vendors. In addition, documented interfaces, infrastructure as code, and tested exit strategies support a future provider change.

Cloud costs depend on factors including computing power, storage, data transfer, and platform services used. Transparent budgets, tagging, monitoring, automated scaling, and regular reviews help use resources efficiently and continuously optimize costs.