LLMOps: definition and framing
Definition
LLMOps refers to the practices and tools used to organise the ongoing operation of language models. This includes monitoring answer quality, cost control, versioning of prompts and models, logging, and handling errors. LLMOps carries ideas from classic software operation over to the particularities of AI applications, whose outputs are not firmly predictable.
Why the Term Matters
An AI pilot can be built in weeks; reliable operation is the real work. Without LLMOps, no one notices when quality slips, costs rise, or a prompt change causes harm. For neutral consulting the term is a reality check. It shows that a rollout is not the end but the start of responsibility. Planning LLMOps early prevents a good solution from quietly decaying in operation.
When It Is Relevant
- An AI application moves from pilot to regular operation.
- Answer quality and cost must be monitored continuously.
- Changes to prompts or models should stay traceable.
- Errors and outages should be caught early rather than reported by users.
When Not
When a project is still a one-off experiment with no claim to lasting operation, a full LLMOps setup is overkill. And when usage is very small and rare, a simple check often suffices. Then the question of whether the use case should enter regular operation at all comes before building the operating discipline.
Industries That Use It
- Software houses: continuously monitor an AI feature embedded in a product so quality and cost stay stable.
- Mail order: absorb seasonal load peaks in customer service under control.
- Energy and utilities: run regulated processes with complete logging.
- Staffing firms: keep changes to selection logic traceable to evidence fair procedures.
Typical Use Cases
- Answer quality is spot-checked so a slip stands out.
- Cost per period is tracked and reconciled with a budget.
- Prompts and model versions are versioned to make changes traceable.
- Errors and outages are logged and analysed.
- A change is tested on a subset before it reaches everyone.
Decision Guide
| Signal | Plan for LLMOps | Alternative |
|---|---|---|
| Pilot moves to regular operation | set up operation and monitoring | without monitoring quality decays quietly |
| One-off experiment | rather not | a simple check suffices |
| Cost must stay controlled | measure consumption continuously | an estimate without measurement is risky |
| Regulated process | logging is mandatory | operation without evidence is delicate |
Example
A software house embeds an AI feature into its product. We set up spot-check quality control, cost tracking and prompt versioning so changes stay traceable. A quality drop then stands out before customers report it. This describes the method, not an asserted client metric.
Service Connection
We plan the move from pilot to operation within AI implementation; whether the use case is ready is shown by the AI readiness check.
Sources
This framing describes common operating practice. Scope and tools depend on the use case and regulatory requirements and should be determined case by case.