AI infrastructure architecture
We design the cloud foundation your AI needs: compute, storage, data flow, networking and the environments to run training and inference.
AI Infrastructure Services
AI Infrastructure Services
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
Build the cloud foundation your AI actually needs. Noseberry designs and runs AI infrastructure, GPU and inference capacity, scalable and secure environments for training and serving, and the cost controls to keep it affordable, on AWS, Azure and Google Cloud. The foundation AI depends on, built for AI's real demands, not improvised on top of standard hosting.
Book a free AI infrastructure reviewDefinition
What are AI infrastructure services?
AI infrastructure services design, provision and operate the cloud foundation that AI runs on: compute (including GPUs), storage, data flow, networking, security and the environments for training and inference. AI has demands ordinary applications do not, GPU capacity, high data throughput, scaling under load, and cost that grows with usage, so the foundation has to be built for that. It is primarily a cloud capability, part of our cloud and DevOps practice, applied for AI and linked from AI because it is the foundation every AI system depends on.
Key takeaways
- AI infrastructure is the cloud, compute, data and networking foundation that AI systems run on.
- AI has demands ordinary apps do not: GPU and inference capacity, large data throughput, and cost that scales with usage.
- The work spans architecture, provisioning, scaling, security and cost control across AWS, Azure and Google Cloud.
- This is primarily a Cloud capability, applied for AI, and it sits under our cloud and DevOps practice.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
Scope
What AI infrastructure services include
The full foundation, from architecture and capacity to security and cost control.
GPU and inference capacity
We provision and right-size GPU and inference resources so models train and serve reliably without paying for idle capacity.
Scalable, resilient environments
Auto-scaling, high availability and failover, so AI holds up under real load and does not fall over at peak.
Security and isolation
Encryption, identity and access, network isolation and secrets management around models and data.
Cost visibility and control
Monitoring and controls on compute, inference and storage spend, so AI costs stay predictable as usage grows.
IaC and DevSecOps
Infrastructure as code and secure delivery pipelines, so the environment is repeatable, auditable and safe to change.
Who this is for
Built for teams scaling AI in the cloud
Engineering and platform leaders whose AI workloads need reliable, secure, cost-controlled cloud foundations that standard hosting cannot provide.
Signs you need it
- You are scaling AI and your current infrastructure cannot keep up.
- GPU or inference costs are unpredictable or spiralling.
- You need reliable, secure environments for training and serving models.
- AI workloads are straining infrastructure built for ordinary applications.
- You want cloud foundations for AI done properly, not improvised.
How we work
Assess, architect, run
1
Assess
We review your AI workloads, current infrastructure and cost profile.
2
Architect
We design the compute, data and networking foundation your AI needs.
3
Provision
We build it as infrastructure as code, with security and scaling built in.
4
Optimise
We right-size capacity and tune cost, so spend tracks real usage.
5
Operate
We set up monitoring and controls to keep it reliable and affordable.
Why Noseberry
Why choose Noseberry for AI infrastructure
AI-specific cloud
We build for AI's real demands: GPUs, inference, data throughput and usage-based cost, not generic hosting.
Secure and resilient
DevSecOps, isolation and high availability, so AI infrastructure is safe and stays up.
Cost under control
Right-sizing and monitoring so AI compute does not become an unpredictable bill.
Proven at scale
2M+ lives touched, 15+ Fortune 500 clients, 250+ solutions across 20+ countries.
Frequently Asked Questions
They are the design, provisioning and operation of the cloud foundation AI runs on: compute (including GPUs), storage, data flow, networking, security and the environments for training and inference, built and tuned for AI's specific demands.
AI workloads need GPU and inference capacity, high data throughput, and scaling that ordinary applications do not, and their cost scales with usage. Infrastructure built for standard apps often cannot keep up or becomes expensive.
AWS, Azure and Google Cloud, chosen to fit your workloads, existing tools and cost profile.
We right-size compute and inference, use auto-scaling so you are not paying for idle capacity, and put monitoring and controls on spend, so cost tracks real usage rather than spiralling.
It is primarily a cloud capability, part of our cloud and DevOps practice, applied for AI. It is linked from AI because AI infrastructure is the foundation AI systems depend on.
AI infrastructure provides the environment; LLMOps is the practice of deploying, monitoring and iterating models on top of it. They work together, and we offer both.
Give AI the foundation it needs
Book a free review and we will architect AI infrastructure that scales and stays affordable.
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