Workload and capacity
Assess models, volumes, expected performance and required hardware resources.
For technical teams
AI models and infrastructure, assessed for your project.
For technical teams and software vendors assessing dedicated environments for running models. Capacity, APIs and operations are defined only after technical review.
Private AI Infrastructure
Infrastructure projects differ from the UTOMAI platform
This category concerns infrastructure for technical teams and applications. It is distinct from Private AI On-prem, which provides the UTOMAI business platform on a dedicated appliance, with workspace, knowledge and configurable agents.
Questions to resolve
There is no assumed standard configuration: scope depends on workload and agreed operating responsibilities.
Model serving
API endpoints, routing, versions and integrations are topics to specify and validate for the project.
Operations and control
Logging, monitoring, updates, administrative access and service levels must be agreed explicitly.
Start with feasibility
Bring a technical requirement
Share the model, expected workload, latency and control requirements: the first step is checking whether a viable configuration exists.
Dedicated GPUs, inference APIs, model routing, monitoring and SLAs are not guaranteed components of the UTOMAI platform or Private AI On-prem package. They can be considered only when feasibility, responsibilities, availability and terms are confirmed in the proposal.
An initial conversation clarifies requirements, dependencies and checks. A proposal is made only for confirmed components and responsibilities.
Deployment
The solution and where it runs are separate choices.
Dedicated Cloud and Private AI On-prem describe deployment for the business platform. The right configuration depends on data, access, infrastructure and operating requirements.
Let’s assess a useful starting point.
I would like to discuss a private AI infrastructure project, including workloads, models, APIs, capacity and operations.