Run AI in production, not as an experiment – with scalable GPU infrastructure
Production AI workloads for the stable operation of LLMs, GPU cloud infrastructure with NVIDIA GPUs – operated in German data centres. Production AI goes far beyond classic analytics applications: while predictive models mainly recognise patterns and make forecasts, generative AI creates new content – such as text, code, images or technical designs.
- Production AI workloads – for the stable operation of LLMs, training and inference in applications and business processes.
- GPU cloud infrastructure with NVIDIA GPUs – RTX A4000 to A100, dedicated, billed by the hour, no minimum term.
- Operated in German data centres – data sovereignty, low latency, direct contacts, BSI C5 Type 1 attestation.
- Managed servers for AI infrastructure – monitoring, performance optimisation and scaling adjustments as part of the managed offering.
GPU cloud infrastructure behind productive AI
Many AI projects fail not because of the model but because of the infrastructure: as soon as models are meant to run in production, CPU systems and single GPUs reach their limits – the result is long training times, blocked resources and projects that grow more slowly than planned. This is exactly where centron's AI infrastructure comes in: a platform for training, inference and data processing.
GPU compute power
Parallel compute power is decisive for training and LLM inference with modern models: NVIDIA GPUs for large neural networks, optimised for machine and deep learning workloads – considerably faster than CPU systems.
Scalable compute resources
AI workloads evolve dynamically: scalable instances for training and inference, flexible capacity expansion, adaptation to growing model sizes and datasets.
High-performance storage
NVMe-based storage for high I/O performance – fast access to training data and models, processing of large datasets without bottlenecks.
Network and orchestration
High-performance network architecture, cluster-capable infrastructure for GPU workloads, container and Kubernetes integration for running complex AI environments at scale.
Typical applications of production AI
Production AI is used not only to develop models but to run them permanently in applications and business processes. As soon as models are meant to run in production, CPU systems and single GPUs reach their limits – the result is long training times, blocked resources and projects that grow more slowly than planned.
Generative AI for text, code, images and designs
Generative AI creates new content – text, code, images or technical designs – and code generation speeds up development. These workloads demand high computing power: cloud GPUs for training and LLM inference.
AI-supported automation of business processes
Many software companies integrate AI functions directly into their products – for intelligent search, automated document processing or recommendation systems, for instance; scaled in Kubernetes with AutoScaler.
Knowledge management with AI
For in-house access to your own knowledge, knowledge management with AI is the right use case – documents, wikis and tickets searchable via RAG, without data leaving the company.
Chatbots, assistance systems and AI analytics platforms
Chatbots, assistance systems and AI-supported analytics platforms are among the most common production applications – with training data in the S3 data lake and models on NVMe storage without I/O bottlenecks.
GPU cloud for productive AI
Other providers offer GPUs exclusively as dedicated servers – that suits classic workloads but quickly reaches its limits with dynamic AI projects. AI workloads need resources that scale with training load or inference demand. For that, centron provides a genuine GPU cloud infrastructure.
- Performance & scalability – cloud GPUs for parallel computation; train large networks faster and run models reliably in production.
- Security & compliance – ISO-certified data centres and a cloud platform with BSI C5 Type 1 attestation, transparent costs without your own hardware.
- AI infrastructure from Germany – operated in our own data centre, in Switzerland only on request: data sovereignty, low latency, direct contacts.
- Managed Server for AI – monitoring, performance tuning and scaling adjustments as part of the managed offering.
The right centron products
Customers typically implement this use case using these building blocks – which can be combined and expanded at any time.
- RTX A4000 from €92.59 per month
- Dedicated, not shared
- No minimum term
- AutoScaler included
- Traffic at a fixed price
- CI/CD-ready
- S3-compatible API
- Free traffic
- Unlimited scalability
How much does generative AI cost at centron?
Run generative AI with confidence: GPU infrastructure for your own models and applications – GDPR-compliant, with data remaining in Germany. The cornerstone of this is Cloud GPU from €92.59 per month – billed by the hour, with no minimum contract term. This is supplemented, as required, by Kubernetes and S3 Object Storage. All data remains in Germany: our own data centres in Hallstadt near Bamberg, certified to ISO 27001 on the basis of IT-Grundschutz and BSI C5:2020 Type 1. New accounts receive a €200 starting credit.
| Building block | Price |
|---|---|
| Cloud GPU | from €92.59 per month |
| Kubernetes | from €29.99 per month |
| S3 Object Storage | from €5.00 per month |
Everything about infrastructure for dynamic AI projects
What are typical applications of production AI?
Why do production AI applications need GPUs as a basis?
Where is centron’s AI infrastructure operated?
Can the AI infrastructure also be operated as a managed service?
Can I rent servers from centron – and which option is right?
How do I choose the right GPU type (RTX A4000, RTX 6000, A100, RTX 6000 Ada) for my ML project?
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