MLOps vs LLMOps: What Changes When the Model Is an LLM?

MLOps is the set of practices for getting machine learning models you train (fraud scores, demand forecasts, recommendations) into production and keeping them working: versioned data and code, tracked experiments, a model registry, deployment, and monitoring for drift. LLMOps applies the same ideas to applications built on large language models, where you usually do not train the model yourself, but you do manage prompts, retrieval, tools, evaluations, safety and token costs.

So LLMOps is not a separate discipline so much as MLOps adapted to a different kind of system. The goals are the same: reliable, measurable, repeatable releases.

What stays the same

• Version everything: code, configuration, data and the model you depend on.
• Test before release on a fixed evaluation set, and compare with the current version.
• Deploy gradually (shadow traffic, a small percentage first) and be able to roll back.
• Monitor in production and alert on problems.
• Keep a feedback loop from production back into the next version.

See what MLOps is for the classic picture.

What changes

	MLOps	LLMOps
The model	You train it on your data	Usually a provider’s model or an open model, used as is or lightly fine-tuned
What you change most often	Features, training data, hyperparameters	Prompts, retrieval settings, tools, the model version
Versioned artefacts	Datasets, features, model files	Prompts and templates, retrieval indexes, tool definitions, model names and versions
Evaluation	Clear metrics on labelled data: accuracy, AUC, RMSE	Open-ended answers: test sets with reference answers, rule checks, model-graded scoring, human review
Main production risks	Data drift, concept drift, stale features	Hallucinations, prompt injection, unsafe or off-topic output, provider changes, cost spikes
Monitoring	Input distributions, prediction distributions, delayed ground truth	Traces of each call, token usage and cost, latency, refusal and error rates, user feedback, quality samples
Cost driver	Training and serving compute	Tokens per request, number of model calls per task
Release unit	A new model version	Often just a new prompt or a new model name in configuration

Things that are new in LLMOps

1. Prompts as code. Prompts are versioned, reviewed and tested like code, because a one-word change can change behaviour across thousands of requests.
2. Evaluation sets for text. You keep a set of realistic inputs with expected answers or rules (must cite a source, must refuse personal data requests), and score every change against it before release. For RAG systems, retrieval quality is scored too; see evaluating RAG.
3. Tracing. One user request may involve retrieval, several model calls and tool calls. Each step is logged with its inputs, outputs, tokens and timing, so failures can be found.
4. Guardrails and security. Input and output checks, tool permissions, and defences against prompt injection; see evaluating and securing agents.
5. Cost control. Budgets per request and per user, caching, and routing easy requests to smaller models.
6. Provider changes. A hosted model can be updated or retired, so pin model versions where possible and re-run evaluations when they change.

Where they meet

Many teams run both. A bank may have classic models for credit risk and an LLM assistant for staff. They often share the same platform: a registry, CI/CD, monitoring dashboards and an evaluation habit. LLM systems also feed classic MLOps: LLMs can label training data, and classic models can route or score LLM requests.

Common mistakes

1. Shipping prompt changes without evaluation because “it is only text”.
2. Monitoring only uptime and latency, not the quality of answers.
3. Not tracking cost per request, then discovering a bill that has doubled.
4. Assuming the provider’s model never changes. Behaviour can shift between versions.
5. Treating LLMOps as a separate team and toolset instead of extending existing MLOps practices.

Interview angle

For “MLOps vs LLMOps”:

• Same goals, different artefacts: trained models and features vs prompts, retrieval and model versions.
• The hardest new part: evaluating open-ended output, with test sets, rules, model grading and human review.
• New risks: hallucination, prompt injection, cost and provider changes.
• What you would monitor for an LLM application: traces, tokens, cost, latency, quality samples and feedback.

Practise with the AI engineer interview questions.

Key takeaway

MLOps and LLMOps share the same discipline: version, evaluate, release gradually and monitor. LLMOps changes what is versioned (prompts, indexes, tools, model versions), how quality is measured (evaluation sets for open-ended text) and what is monitored (traces, safety, tokens and cost). Teams that already practise MLOps have most of the foundations.