AI infrastructure economics
Local GPU and cloud AI coding routes have different cost, capability, latency, privacy and operational profiles. The correct comparison is workload-specific: model quality, context fit, hardware utilisation, queue delay and human review all affect cost per verified delivery. Hybrid routing can use each where it is appropriate.
A local model can have low marginal inference cost while the hardware remains underutilised or the output needs more remediation. A hosted model can have higher token cost while completing certain work with fewer iterations.
Cost per verified delivery combines execution cost with review, failure and delay.
Private execution can support data and infrastructure policies, but it does not prove output quality. Both local and cloud workers need bounded repository access and evidence gates.
Credential, network and retention boundaries must be evaluated for each route.
Nexus is designed for model-independent routing under one project governance layer. Suitable work can use approved self-hosted infrastructure while other tasks use approved cloud models.
Actual deployment availability and model routes should be confirmed for the required environment.
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