On-premises LLM vs private cloud: a deployment decision

Choosing between an on-premises LLM and private cloud starts with conditions the system must satisfy. If a documented requirement prohibits external connectivity for a workflow, a remote API cannot serve that workflow. If the organisation cannot maintain infrastructure, installing a server in its office does not solve the operational gap. Separate preferences from requirements that make an option unacceptable.

Specify what must remain inside each boundary: documents, prompts, outputs, logs and credentials. Identify who can administer the environment and from which locations. A restriction on content may still permit aggregated metrics, but the exception must be explicit. A diagram containing a single box labelled private AI conceals the most important decisions.

The private AI business guide provides the broader decision framework. This comparison focuses on execution location and operating responsibility. For the choice between operating a model and consuming a managed endpoint, also read the self-hosted LLM versus API comparison.

These differences are design questions, not automatic guarantees. A proposal must answer them with specific architecture and ownership. Keep the task, volume and required service level constant when comparing bids; otherwise a cheaper proposal may simply include less capacity or recovery support.

An on-premises deployment can fit existing internal networks and systems, but it creates ongoing work. Capacity, patches, certificates, backups, monitoring and recovery all need named owners. If one person is the only individual who understands the server, operational risk remains even when no business data leaves the building. Include documentation and handover in the deployment scope.

Before purchasing hardware, test a representative model with the intended context length and concurrency. Fitting the model weights into memory does not establish sufficient capacity for simultaneous requests. The serving engine and generation cache also need memory. The inference capacity planning guide explains how to translate user counts into a measurable workload.

Offline operation needs a specific rehearsal. Block the relevant outbound connections and test sign-in, model loading, inference, logging and recovery. Document how updates enter the environment and how they are checked before installation. Treat disconnected operation as a property demonstrated by the complete workflow, rather than a claim inferred from the location of the model.

A hosted proposal should distinguish dedicated capacity, logical isolation and shared components. Ask which resources are reserved, which limits apply and what happens when demand increases. A unique endpoint says little about system isolation or the privileges retained by an infrastructure operator. Request an explanation that reaches beyond the application interface.

Review the administrative and support path: authentication, authorisation, session logging, customer approval and revocation. Establish whether prompts or outputs are inspected during incidents. Diagnostic work may require evidence, but sensitive content should not be sent automatically when metrics or synthetic examples are sufficient. Specify who authorises exceptional access and how it is recorded.

Recovery needs more detail than the word redundancy. Define acceptable interruption, tolerable data loss and the location where service will resume. An alternate environment outside an approved boundary may restore availability while violating a data requirement. Include failover in the data residency and access review, not only in the availability plan.

A hybrid design can route different tasks to different environments, such as internal processing of restricted documents and external processing of public information. To make that arrangement reviewable, data classification and routing policy need explicit rules. Do not leave permission to transmit data to a model's improvised judgement. Define what happens when an input cannot be classified confidently.

Automatic fallback deserves particular scrutiny. Sending requests to another provider when the private environment fails changes the data flow. That alternative must be approved in advance and retain the requirements of the task. Where no acceptable alternative exists, queuing or a manual process may be the correct response. Continuity does not justify an unapproved transfer.

Hybrid deployments also expand the test matrix. Environments can have different context limits, model versions and output behaviour. Estimate whether the benefit justifies maintaining those differences. A simpler architecture with an explicit degraded mode may be easier to operate than a combination whose failure behaviour has never been rehearsed.

Use common inputs and record quality, time to first response, completion time and errors under load. Test the loss of the dependency most likely to constrain service: connectivity, serving engine, storage or identity. One option may be faster but harder to recover. Both findings belong in the decision record, alongside the cost and ownership implications.

The vLLM metrics documentation identifies useful engine measurements. These do not replace an end-to-end business test. Include application queues, user experience and review effort, and record versions and conditions so the comparison can be reproduced. Avoid comparing a warm, lightly loaded environment with a cold, saturated one.

The final recommendation should state the selected option, rejected alternatives, reasons, assumptions and accepted risks. Assign an owner to every dependency and estimate recurring operating cost. Primedefence private inference services can start with the workload assessment before a deployment modality is committed.