What needs to run?
Model size, expected usage and the number of people or applications accessing the service.
What we manage
The foundation
behind your AI.
Compute, model deployment and ongoing operations.
Designed to work together, around your requirements.
One connected serviceCompute → Models → Your work
GPU infrastructure
Start with the workload.
Build the compute around it.
Different models place different demands on infrastructure. We work from your intended use to scope the capacity, configuration and operating environment.
What does useful feel like?
Response expectations, workload patterns and the evaluation criteria that matter to your team.
Where does it fit?
Hosting location, access, data handling and the requirements of your existing systems.
Model deployment
The right model.
For the work in front of you.
Model choice is a deployment decision. Capability, licensing, compute needs and your evaluation criteria all belong in the conversation.
Capability, considered in context
Put your knowledge to work.
Language models can support document understanding, knowledge retrieval and internal assistants. Start with the content, languages and response quality your application needs.
Questions to bring
Content and context length · Languages · Output quality
Model selection and availability are confirmed during scoping.
SelectionConfigurationEvaluationDeployment
Ongoing operations
A deployment is a beginning.
Keep the work moving.
The model is only one part of the environment. An agreed operating scope makes ownership, support and change clear for both teams.
See how responsibilities fit togetherEnvironment management
Define how the infrastructure and deployed models are maintained.
Model changes
Plan evaluation and rollout when a model or workload needs to change.
Technical support
Agree support coverage, escalation and each team's responsibilities.