LLMOps vs MLOps
MLOps is the practice of deploying, monitoring, and maintaining traditional machine-learning models in production. LLMOps is MLOps adapted for large language models — adding prompt and version management, evaluation of open-ended text outputs, retrieval-augmented generation (RAG), guardrails against unsafe or hallucinated output, and token-level cost control. They share a foundation, but LLMs break several MLOps assumptions, which is why LLMOps exists as a distinct discipline.
The core difference
Traditional ML models produce a bounded, measurable output — a class, a score, a number — so you can evaluate them with accuracy, precision, or RMSE. LLMs produce open-ended text, cost money per token, depend heavily on prompts and retrieved context, and can produce fluent but wrong answers. That changes what "operations" means.
| Dimension | MLOps | LLMOps |
|---|---|---|
| Model | Trained on your data | Foundation model + prompts/RAG/fine-tuning |
| Output | Bounded (class/score) | Open-ended text |
| Evaluation | Accuracy, precision, RMSE | LLM-as-judge, factuality, guardrail pass rate |
| Key artifacts | Features, model weights | Prompts, prompt versions, retrieval corpora |
| Failure mode | Drift, degraded accuracy | Hallucination, prompt regression, cost blow-out |
| Cost driver | Compute (train/infer) | Tokens (per request) |
| New primitives | Feature store | Prompt registry, eval harness, guardrails, RAG |
What LLMOps adds
- Prompt & version management — prompts are code; changes need versioning, review, and regression tests.
- Evaluation for open-ended output — you can't use accuracy alone; you need factuality checks, LLM-as-judge, and guardrail pass rates.
- Retrieval (RAG) — grounding models in your corpora, evaluated for accuracy and tenancy-isolated (see RAG vs fine-tuning).
- Guardrails — hallucination mitigation, safety filters, and human-gating for high-stakes actions.
- Cost & observability — token accounting, latency, and per-request cost tracking (a real budget line, not an afterthought).
Do you need MLOps, LLMOps, or both?
If you run predictive models (fraud scoring, forecasting, recommendations), you need MLOps. If you're building on LLMs (assistants, RAG, agents), you need LLMOps. Most enterprises adopting AI in 2026 need both, sharing CI/CD, monitoring, and governance foundations while LLMOps layers on the LLM-specific primitives.
How Citadel helps
Citadel builds production LLMOps — CI/CD, evaluation, observability, and cost control for models in production — and pairs it with governed AI agents so the systems that run on those models are auditable and safe.