Choose the right environment for your AI workload
ETT helps you assess and implement AI infrastructure against performance, cost, data location and operating requirements. Define what the workload needs before committing to capacity.
What you would be buying
A decision on where an AI workload should run, and the environment to run it in, sized against requirements you can actually state.
What you receive
- Workload requirements: model profile, expected demand, latency and data-location needs
- Hosting options with a cost model for each
- Architecture for the chosen option
- Provisioning plan
- Monitoring and support responsibilities, split between ETT, the provider and your team
What you provide
- Model and workload profile
- Expected demand, including peaks
- Latency requirements
- Deployment constraints: data residency, network, procurement rules
- Security requirements the environment has to satisfy
How success is measured
- Workload performance against the agreed specification
- Utilisation against provisioned capacity
- Reliability over the measurement period
- Cost against the modeled forecast
Infrastructure is supplied by named providers; ETT designs, provisions and supports it. Available regions, current capacity, commercial unit and any minimum commitment are confirmed per engagement, because they change.
What can hold it up
- A workload profile specific enough to size against; “we might need GPUs” is not one
- Provider capacity in the region you need, at the time you need it
- Your procurement and security review timelines
After it goes live
Support boundaries are set per engagement: which party monitors what, what response is expected, and what falls to the underlying provider.
Deliverables and measures describe the standard shape of this engagement. Exact scope, duration and commercial terms are agreed and confirmed in writing before work starts.
How we do the work
Hosted GPU infrastructure
We support secure, high-performance environments for AI workloads including model training, inference and LLM experimentation. Where approved and appropriate, this may include access to GPU capacity such as NVIDIA H100, A100 or L40s through ETT's infrastructure and partner ecosystem.
AI-native infrastructure consulting
We help organizations understand what infrastructure their AI workloads need, from compute requirements and data flows to security, networking and deployment architecture.
Networking and security design
We support the design of secure connectivity, access controls and networking layers across cloud, hybrid or dedicated environments.
MLOps and ModelOps platforms
We help teams create the operational backbone for production AI, including model deployment, monitoring, governance, pipeline management and lifecycle control.
AI tooling integration
We support integration with the wider AI ecosystem, including LLM workflows, RAG pipelines, vector databases, inference endpoints and existing enterprise platforms.
Data and workload management
We help organizations think through how data, compute and workloads should be managed so AI systems can run efficiently, securely and in line with business requirements.
When dedicated AI infrastructure makes sense
Building in-house AI applications
For CTOs and AI teams developing internal LLM applications, AI tools or model-driven products.
Why this matters
AI workloads place different demands on infrastructure.
Teams building GenAI applications, LLM workflows, model training pipelines or inference services often need access to high-performance compute, predictable capacity, secure networking and better control over cost, data and deployment environments.
Shared cloud GPU instances can be useful, but they can also introduce challenges. Provisioning may be slow, performance can be inconsistent, costs can be difficult to forecast, and some workloads need stronger control over residency, compliance or dedicated capacity.
ETT helps organizations design AI infrastructure that fits the workload, not the other way around. From dedicated GPU hosting to MLOps environments and secure integration with AI tooling, we help teams create the technical foundation needed to build, run and scale AI with more confidence.
Why AI workloads need specialized infrastructure
AI infrastructure is not just about where something is hosted. The environment needs to support how models are trained, deployed, monitored and improved. That can involve GPU capacity, storage, networking, security controls, data access, orchestration, monitoring and integration with the wider AI tooling ecosystem.
For some organizations, shared cloud services are enough. For others, the workload demands more control. Dedicated AI infrastructure can help when teams need predictable compute, clearer data residency, reduced noisy-neighbour risk, stronger security design or closer alignment between infrastructure and the AI product roadmap.
The right infrastructure decision depends on the model, the data, the risk profile, the workload and the way the organization plans to operate AI over time.
Shared capacity, unclear costs, slow provisioning, generic configuration, limited control.
Dedicated GPU capacity, clearer environment design, secure networking, AI-specific architecture, workload-aligned.
Technology that fits the workload
ETT works with specialist technology and infrastructure partners to help organizations access the right AI environment for the right use case.
Where approved, this may include dedicated GPU capacity, hosted AI environments, secure networking, MLOps tooling and infrastructure options through partners such as CUDO Compute.
The focus is always on the workload: what the model needs, how the data moves, what security controls are required, and how the environment will support live AI operation.
These are technologies ETT implements. The relationship behind each one — formal partnership, authorised resale, or a platform we deploy — is stated on its technology ecosystem entry.
How we design infrastructure around AI workloads
Orient
We assess the AI workload, model goals, data, performance and security requirements, and current infrastructure constraints.
Prove
We validate the architecture on a representative workload, confirming performance, cost and reliability before committing to scale.
Govern
We define the security, access, monitoring and operational controls the infrastructure must enforce for governed AI workloads.
Scale
We implement the infrastructure environment — compute, networking, storage, MLOps and integration — connecting the tooling and controls AI needs to run.
Compound
We review performance, capacity, cost and workload changes so the infrastructure continues to support AI use efficiently over time.
Often delivered alongside
Timing, cost, access and ownership
How long does this take?
Design is quick. Provisioning depends on provider capacity in the region you need and on your own procurement and security review, both of which are worth starting early.
What drives the cost?
The workload profile, expected demand including peaks, latency requirements and data-location constraints. Dedicated capacity costs more than contended capacity and earns that only for sustained workloads — we will tell you which yours is rather than assuming dedicated is always better.
What access do you need?
A workload profile specific enough to size against: model, expected demand, latency requirements and any deployment constraints.
Who supplies the infrastructure?
Named third-party providers supply the compute. ETT designs the environment, provisions it and supports it within the boundaries agreed per engagement. Available regions, current capacity, the commercial unit and any minimum commitment are confirmed at the time, because they change and a figure published on a web page would be out of date.
What happens next?
A conversation about the specific process or decision you have in mind. If there is a workable opportunity we will describe a scoped first engagement; if there is not, we will say so and explain why. Requesting a session does not commit you to anything.
About this service
What is AI Infrastructure as a Service?
It provides the compute, hosting, networking and operational environment needed to build, train, deploy or run AI workloads.
What is dedicated GPU hosting?
Dedicated GPU hosting gives organizations access to GPU capacity for AI workloads such as model training, fine-tuning, inference or LLM experimentation, without relying solely on shared cloud instances.
When does a business need dedicated AI infrastructure?
When workloads require predictable compute, stronger control, specific security requirements, data residency, MLOps capability or reduced reliance on generic shared cloud environments.
What is MLOps?
The set of practices and tools used to deploy, monitor, manage and improve machine learning models in production environments.
Can AI infrastructure support RAG and vector databases?
Yes. It can support RAG pipelines, vector databases, inference endpoints, model hosting and the wider tooling needed for AI applications.
Does your AI workload have the infrastructure it needs?
Request an AI session to explore the compute, data, security and MLOps foundations needed to build, train or run AI systems with more control.
Around four minutes. Indicative guidance based on your answers.