Managed AI Inference

AI inference is the use of a trained model to generate predictions, classifications or content from new inputs. In managed AI inference, a service provider operates much of the serving infrastructure behind an endpoint; in self-hosted deployment, the user operates the model-serving stack on chosen infrastructure.

Division of operational responsibility

Managed inference follows a shared-responsibility model, whose exact boundary depends on the service contract. Self-hosting places most infrastructure and model-serving responsibilities on the deploying organisation.

• A managed provider generally handles compute provisioning, serving software, infrastructure maintenance, scaling and service availability.
• The user still controls model choice, configuration, access permissions, input data, application integration and evaluation of outputs.
• With self-hosting, the user manages hardware or cloud instances, containers, drivers, inference runtimes, scaling, patching, observability and incident response.

Operational trade-offs

The central difference is not model intelligence but who operates and controls the production system.

• Managed inference can reduce deployment time and specialist operational work through standard endpoints, elastic scaling and metered resource use.
• Self-hosting offers greater control over hardware, runtime optimisation, network placement, update schedules and data paths, but requires stronger engineering capacity.
• Managed services may constrain customisation, portability or supported models, while self-hosting permits deeper optimisation at greater operational complexity.
• Both approaches require monitoring of latency, throughput, failures, model quality, security and cost after deployment.

Choosing between them

The appropriate model depends on workload characteristics rather than benchmark rankings alone.

• Managed inference suits variable demand, rapid experimentation and teams that prefer to transfer infrastructure operations to a provider.
• Self-hosting may suit predictable high utilisation, specialised hardware, strict data-location requirements or applications needing extensive runtime control.
• The comparison should use total cost and risk, including engineering effort, idle capacity, availability requirements, privacy, portability and vendor lock-in.