Guide
LLMOps vs MLOps: what actually changes.
MLOps is the practice of running machine learning models in production. LLMOps applies the same idea to large language models, and several of the hard parts move somewhere new.
What MLOps covers
MLOps manages the lifecycle of machine learning models: preparing data, training, versioning models and datasets, deploying them, and watching for drift once they meet real traffic. The model is something your team trains, so most of the effort goes into data pipelines and training infrastructure.
What LLMOps covers
With large language models, most teams do not train the model. They use a pre-trained one, through an API or self-hosted, and shape its behaviour with prompts, retrieved context and sometimes fine-tuning. The operational work shifts from training to everything around the model: prompts, retrieval, evaluation, guardrails and cost.
The main differences
- What you version: MLOps versions models and datasets. LLMOps also versions prompts, retrieval indexes, tool definitions and the model release you depend on.
- Evaluation: classic models are scored against a labelled test set. LLM answers are free text, so you need graded examples, regression suites and human review for the cases that matter.
- Cost: in MLOps the large bill is training. In LLMOps it is inference: tokens per request with an API, GPU hours when self-hosting.
- Failure modes: beyond drift, LLMs can state wrong things confidently, follow instructions hidden in the content they read, and leak data that was placed in their context.
- Deployment: instead of one model server, there is usually a gateway that routes between several models and providers, with fallbacks and rate limits.
What stays the same
The foundations do not change. You still need CI/CD, infrastructure as code, observability, access control and a rollback path. Teams with solid DevOps and platform practices adopt LLMOps much faster, because the AI workload is one more workload on a platform they already trust.
What to put in place first
- A single gateway for all model calls, with authentication and per-team budgets
- Logging of requests and answers, with personal data handled deliberately
- A small evaluation set that runs on every prompt or model change
- Dashboards for latency, error rate, quality scores and spend
- A way to roll back a prompt or model version in minutes
Integritasec does this work hands-on. See AI infrastructure, MLOps and LLMOps, or talk it through directly.