Fully Managed AI Infrastructure Company: How to Evaluate

Evaluating a fully managed AI infrastructure company means verifying that operations, SLA, support, compliance, scalability, and total service scope are genuinely fully managed, not partially managed with a fully managed label, because the gap between the two determines whether the customer's team focuses on AI or on infrastructure. Evidence, not labels, is the test.

Companies market fully managed AI infrastructure broadly, but the label means different things. Some companies operate the entire stack under an SLA; others provide hardware with a help desk and call it managed. Evaluating which is which requires probing the six areas that define genuine full management, because the gap surfaces only when operations are needed and the customer discovers they are the ones providing them.

The Six Evaluation Areas for a Fully Managed AI Company

1. Operations Depth

Verify the company operates the full stack: GPU-specific monitoring, patching, capacity planning, and incident response, all under the provider's team. A company that provides hardware but expects the customer to operate it is not fully managed. Ask who wakes up at 3am when a training job fails.

2. SLA Definition and Accountability

Verify the SLA specifies availability, response times, remediation, and service credits. A fully managed company stands behind its operations with a measurable commitment. Vague assurances of high uptime are not an SLA and do not make a company accountable.

3. GPU-Aware Support

Verify the support team understands GPU workloads, not just general cloud issues. A fully managed AI company staffs GPU-aware engineers who can diagnose training failures and performance issues, because that is the support AI teams actually need.

4. Compliance Scope

Verify compliance certifications cover the GPU services you will use, not just the company generally. A fully managed company operates compliance controls as part of the service, so the customer inherits a compliant posture without building one.

5. Scalability Evidence

Verify the company can scale operations with your AI program. A fully managed service must sustain its operations, SLA, and support as the environment grows. Ask for evidence of multi-team, multi-year deployments under full management.

6. Total Service Scope

Verify all six components of fully managed infrastructure are included: compute, storage, networking, platform, operations, and compliance. A company that includes some but not others is partially managed, regardless of the label. Total scope is what makes the service fully managed.

Evaluation Matrix

How to Evaluate a Fully Managed AI Company

How OneSource Cloud Fits as a Fully Managed AI Company

OneSource Cloud's managed AI infrastructure operates the full stack under an SLA on private AI infrastructure, with GPU-aware support, compliance controls scoped to the services, and scalability through the OnePlus Platform. The model is designed for enterprises that need a company to fully manage AI infrastructure, not partially manage it with a label.

FAQ

How do I evaluate a fully managed AI infrastructure company?

Verify six areas: operations depth, SLA definition, GPU-aware support, compliance scope, scalability evidence, and total service scope. A fully managed company delivers all six; a partially managed one delivers some and leaves the rest to the customer. Evidence, not labels, is the test.

What distinguishes a fully managed from partially managed AI company?

Operations. A fully managed company operates the entire stack, including monitoring, patching, and incident response, under an SLA. A partially managed company provides hardware but expects the customer to operate it. The gap surfaces when operations are needed and the customer discovers they are providing them.

What should the SLA of a fully managed AI company include?

Specific availability, response and resolution times, and service credits when targets are missed. The SLA is the contract that makes the company accountable for full management. Vague assurances without specifics do not constitute an SLA.

How important is GPU-aware support in a fully managed company?

Essential. Generalist support cannot diagnose GPU-specific issues like training failures or memory pressure. A fully managed AI company staffs GPU-aware engineers who can, because that is the support AI teams need and the support that defines full management.

What is a red flag in a fully managed AI company?

Operations left to the customer, a vague SLA, generalist support, compliance scope excluding GPU services, no scaling evidence, and partial service scope. Each signals a company marketing full management while delivering partial management.

Summary

Evaluating a fully managed AI infrastructure company means verifying operations depth, SLA, GPU-aware support, compliance scope, scalability, and total service scope. The label fully managed is applied broadly, so the six-area evaluation, backed by evidence, is what distinguishes a company that genuinely manages the full stack from one that provides hardware with a help desk. For enterprises that need their team focused on AI rather than infrastructure, choosing a company that delivers on all six areas is what makes fully managed a reality rather than a marketing claim.

Next step: Explore OneSource Cloud's managed AI infrastructure to evaluate it as a fully managed company →