Private LLM operations: monitoring, change and recovery

The business owner decides what output is acceptable and when use should stop. Platform operators maintain capacity and availability. Security owns access review and incident responsibilities, while an evaluation owner checks quality changes. One person may cover several functions, but each responsibility needs a name. Specify who receives alerts, coverage hours and escalation arrangements.

Document dependencies: identity, storage, document retrieval, networking and the serving engine. A healthy endpoint can still rely on an outdated index or incorrectly synchronised permissions. The architecture example provides a way to trace those relationships. Distinguish customer-operated components from responsibilities a supplier would assume within the agreed service scope.

An SLO describes a measurable service objective; an SLA formalises contractual commitments and their conditions. Do not use the terms interchangeably or advertise an SLA that has not been agreed. Define the measurement window, request population and exclusions. Measuring latency only for admitted requests should not conceal widespread rejection caused by saturation.

Objectives should represent the process. A batch job may need completion before a deadline; an interactive interface needs initial wait and total completion measures. Include quality and review where these determine task completion. Set thresholds using the pilot and capacity tests, rather than copying numbers from another environment with different inputs or operating conditions.

Available metrics depend on the serving engine and version. Consult NVIDIA NIM observability or vLLM metrics as appropriate. Verify metric names and units against the deployed version before creating dashboards and alerts.

Capacity investigation often needs request identifiers, versions, timings, token counts and error categories rather than full content. Logging complete prompts and outputs can increase data exposure. Define what content is retained, its purpose, retention period and permitted readers. Include monitoring exports and support access rather than limiting the policy to the main application log.

Where quality samples are necessary, use an authorised process with restricted access and minimisation. Removing names alone does not establish that a document is no longer sensitive. Ensure an alert does not copy confidential content into a broader communication channel. The data inventory should cover logs, traces, samples and backups as well as model inputs.

Version the model, instructions, engine, quantisation, retrieval and validators. Before production changes, run relevant regression cases and compare quality, performance and controls. A prompt-template change can alter results even when the weights remain identical. Record the reason, evidence, operational approval and reversal procedure so the on-call operator can identify what actually changed.

A gradual release can limit failure impact where traffic can be directed to identifiable versions. Define stop criteria before starting. Rollback must restore compatible configuration and handle in-flight work, rather than merely downloading old weights. If the index or output schema changed, verify that the previous version can still operate correctly with its dependencies and consumers.

The runbook should support detection, containment, diagnosis, restoration and verification. For suspected disclosure, restrict affected access and preserve evidence under controlled access. For saturation, bound retries and admission. Do not resolve an outage by automatically sending data to another supplier unless that route has been approved and tested against the same requirements.

Test restoration of required configuration and dependencies, rather than simply checking that a backup exists. Record recovery time and lost or pending work, then compare these with agreed objectives. Verify that restoration does not reintroduce revoked permissions or data scheduled for deletion. Link these exercises to the controls in the security plan so recovery does not silently weaken them.

Choose a review cadence proportionate to process risk and change. Examine incidents, volume, error segments and cost per accepted task. Model behaviour may remain unchanged while the documents supplied to it change; an authorised review process should therefore observe actual task outcomes. Keep an action record and check whether interventions reduced the problem that prompted them.

Include retirement triggers: lost support, a licence incompatible with a new use, disproportionate cost or insufficient quality. The model selection guide provides a decision record for replacements. Private inference services should specify who performs these duties. Hosting a model and maintaining a business process both require explicit responsibilities, resources and a way to verify that the work is being done.