LLMOps is the discipline for turning language-model applications into services you can actually control. An SME doesn't need a massive platform: it needs to know which models it uses, evaluate real tasks, version prompts and knowledge, deploy through gates, monitor quality and cost, manage incidents, and be able to roll back to a safe version.
Minimum inventory
Every application should record:
- owner;
- purpose;
- model and provider;
- version and region;
- prompt/policy;
- data and RAG;
- tools;
- permissions;
- metrics;
- cost;
- status and date.
AI features embedded inside SaaS tools count too: without this register, it's impossible to know how many language-model applications are actually running across the company.
Environments
Separate development, evaluation and production. Tests don't use real data unless necessary. Credentials and limits differ between environments.
The move to production requires approval and evidence. The prompt is never edited directly in production without a version, because that control prevents improvised changes that nobody can later explain or revert.
Version the whole system
The response depends on:
- the model;
- the parameters;
- the prompt;
- the tools;
- the policy;
- the index and documents;
- the code;
- the output format.
An "agent version" must fix all of these dependencies in order to reproduce a failure; without that fixing, two seemingly identical runs can produce different results.
Evaluation
The test set contains ordinary cases, edge cases, unanswerable questions, incomplete data, attacks and tool failures.
Metrics:
| Layer | Metric |
|---|---|
| Result | accuracy, completeness and format |
| Faithfulness | grounding in sources |
| Safety | leakage, injection and prohibited actions |
| Operations | latency, availability and cost |
| Human | corrections, escalations and adoption |
| Impact | complaints and harm |
Critical classes have zero tolerance: a failure in one of them blocks the move to production even if every other metric improves.
Regression testing
Regression runs are triggered whenever the model, prompt, provider, source, tool or policy changes. The new and previous versions are compared against the same test set.
A version is never promoted just because it performs better on average if it makes critical cases worse.
Gradual rollout
- Shadow mode, no actions.
- Internal users.
- Limited population.
- Low-impact automation.
- Conditional scale-up.
Each phase has exit criteria and a rollback plan, because moving forward without meeting them shifts the risk straight onto real users.
Observability
The trace includes:
- request and identity;
- version;
- sources;
- tools;
- validations;
- response;
- approval;
- tokens, cost and latency;
- feedback.
Personal data and secrets are minimised. Logs are not an indiscriminate copy of conversations: the goal is to be able to reconstruct what happened, not to accumulate unnecessary information.
Alerts
- rising error rate;
- increased abstention;
- anomalous cost;
- p95 latency;
- rejected tool calls;
- distribution shifts;
- outdated documents;
- access denied;
- loops.
Every alert has an owner and a runbook, because an alert nobody acts on is the same as having no alert at all.
Security and supply chain
OWASP highlights risks in models, data, dependencies and platforms. What gets reviewed:
- model provenance;
- hashes and signing;
- packages;
- permissions;
- prompt injection;
- untrusted output;
- secrets;
- providers;
- SBOM where applicable.
A model's output is never executed without validation: trusting the model is no substitute for verifying what it produces.
Cost management
Budgets are set per application, per user and per task. Cost is measured per valid outcome.
Techniques:
- routing by difficulty;
- smaller models;
- minimal context;
- caching with expiry;
- step limits;
- selective retrieval;
- batch processing.
Cost is never cut by sacrificing evidence or security, because that saving usually costs far more the moment an incident happens.
Incidents
The procedure must allow you to:
- stop;
- revoke credentials;
- identify the version;
- reconstruct actions;
- roll back;
- assess affected people and data;
- fix the test set;
- communicate.
Lessons learned feed back into evaluation, so the same failure doesn't slip through unnoticed in the next version.
Providers
Monitor changes to terms, models, prices, regions, subprocessors and limits. Any change announced by a provider triggers a regression run and, where relevant, a privacy review.
Lightweight governance
| Role | Responsibility |
|---|---|
| Product owner | value and scope |
| Technical team | architecture and operations |
| Security | threats and response |
| Legal/DPO | obligations and data |
| Business expert | test set and acceptance |
| Management | risk and resources |
60-day plan
Days 1–15
Inventory, owners and risks.
Days 16–30
Test set, metrics and versions.
Days 31–45
Traces, alerts, security and costs.
Days 46–60
Pilot, rollback and incidents.
Common mistakes
- Versioning only the model.
- Testing with the prompt's own examples.
- Measuring subjective preference.
- Switching providers without a regression run.
- Logging secrets.
- Not capping cost.
- Having no rollback plan.
- Not testing against attacks.
- Ignoring the RAG layer.
- Having no owner.
Checklist
- Inventory.
- Full versioning.
- Independent evaluation.
- Critical gates.
- Environments.
- Traces and alerts.
- Security and supply chain.
- Budgets.
- Incidents and rollback.
- Provider review.
Frequently asked questions
Is LLMOps only for large companies?
No. The controls can be implemented with simple tools as long as they are reproducible.
Do you have to keep every prompt?
Not indiscriminately. You should keep the evidence you actually need, with minimisation and security in mind.
What triggers a regression run?
Any change to the model, prompt, tool, data, index, policy or provider.
Sources consulted
Summum IA can implement evaluation, observability, security and cost control over your language-model applications, in coordination with your AI agents.