LLMOps vs MLOps
MLOps is the practice of deploying, monitoring, and maintaining machine-learning models in production. LLMOps is MLOps adapted for large language models, adding prompt and version management, evaluation of open-ended text, retrieval, guardrails, and token-level cost control.
The two disciplines share a foundation and diverge on four assumptions. A traditional model produces a bounded output you can score with accuracy or RMSE against a labelled set; a language model produces open-ended text where correctness is a judgement call, so evaluation moves to faithfulness, citation accuracy, and guardrail pass rates. A traditional model is the artefact you version; with a language model the behaviour-defining artefacts are the prompt, the retrieval corpus, and the tool grants, which means all three belong in version control with review. A traditional model costs compute at training time; a language model costs tokens on every request, so spend is a per-request metric rather than a capacity plan. And a traditional model fails by drifting; a language model fails by producing something fluent and wrong. Most enterprises adopting AI need both practices, sharing CI/CD, monitoring, and governance while LLMOps layers on the language-specific primitives.
What is the core difference between LLMOps and MLOps?
| Dimension | MLOps | LLMOps |
|---|---|---|
| Primary artefact | Trained model weights | Foundation model plus prompts, retrieval corpora, tool grants |
| Output shape | Bounded (class, score, forecast) | Open-ended text and tool calls |
| Evaluation | Accuracy, precision, recall, RMSE | Faithfulness, citation accuracy, LLM-as-judge, guardrail pass rate |
| Versioned inputs | Features, datasets, hyperparameters | Prompt registry with content hashes, corpus and index versions, model identifiers |
| Cost driver | Training and inference compute | Tokens per request, plus retrieval and reranking |
| Dominant failure | Data drift, degraded accuracy | Hallucination, prompt regression, injection, cost blow-out |
| Rollback unit | Previous model version | Previous prompt pin, index version, or model identifier |
| New primitives | Feature store | Prompt registry, eval harness, guardrails, retrieval spine, model gateway |
The row that surprises teams most is the rollback unit. In MLOps, rolling back means promoting the previous model version, and the artefact registry already tracks it. In LLMOps a regression can originate in a prompt edit, a reindexed corpus, a reranker change, or a provider updating the model behind a stable name, so rollback only works if each of those is separately versioned and recorded per request. Teams that pin the prompt by content hash, stamp the index version and the model identifier onto every trace, and keep a blue-green path for index promotion can answer "what changed" in minutes. Teams that do not end up bisecting production.
What does LLMOps add that MLOps does not have?
Prompt and version management. System prompts are behaviour-defining configuration. Treat them as code: versioned, reviewed, and pinned by content hash so a change shows up as a diff and a rollback is a pin change rather than a redeploy.
Evaluation for open-ended output. A golden question set per corpus, adversarial sets, retrieval metrics such as recall at k and nDCG against labelled relevance, and generation metrics for faithfulness and citation accuracy. The eval suite runs as a required check on any change to prompts, retrieval, or model selection, with a documented pass threshold. A regression suite that only runs manually is not a gate.
Retrieval as a shared spine. Grounding is hybrid in production: dense vectors alongside lexical search, fused, reranked with a cross-encoder, then filtered by access control before anything reaches the model. Which corpus an application binds to is configuration, not a bespoke pipeline per team. The trade-offs are set out in RAG vs fine-tuning.
Guardrails. Injection and jailbreak screens on input; on output, groundedness checks, citation validation against the retrieved identifiers, and a confidence floor that routes weak answers to a refusal or a human queue instead of guessing.
Cost and observability. A model gateway is the single chokepoint for provider traffic, which is what makes routing, fallback chains, caching, per-tenant token budgets, and audit possible in one place rather than in every service.
What changes again when the model becomes an agent?
An agent selects and executes actions at runtime, so the operational surface grows beyond prompts and retrieval to include tool grants, autonomy tiers, and an identity per agent. A model version change is no longer only an accuracy question: a provider-side update resets behaviour assumptions for every tool the agent can call, which is why model identifiers belong in the audit record per request and why tier assignments should be re-evaluated after a swap. Adding a tool to an agent is a permission change and deserves the same approval path as granting a human new production access. Once tool calls are in play, the governed AI agent controls become part of the operational stack rather than a separate compliance exercise, and the classification model is worked through in the autonomy tiers guide.
Do you need MLOps, LLMOps, or both?
If you run predictive models such as fraud scoring, demand forecasting, or recommendations, you need MLOps. If you are building assistants, retrieval systems, or agents on language models, you need LLMOps. Most enterprises need both, and the sensible pattern is one platform: shared CI/CD, shared secrets and identity, shared observability, with the language-specific primitives layered on top rather than run as a parallel stack by a separate team.
Two decisions do most of the work when standing this up. First, put every model call behind a gateway from day one, because retrofitting cost attribution and audit later is painful. Second, build the evaluation harness before the second use case, because without it every prompt change is a bet. The change-control expectations that both frameworks impose are mapped in ISO 42001 and NIST AI RMF for AI agents, and the identity layer underneath is covered in workload identity for AI agents.
How Citadel helps
Citadel builds production LLMOps: the model gateway, prompt registry, evaluation harness, retrieval spine, guardrails, and cost observability that let a language-model system change safely. It is paired with governed AI agents so the systems running on those models carry identity, tiered autonomy, and an audit trail, and with the security posture described on AI agent security and zero trust for AI.
Where to start
The Agent Assurance Assessment is a fixed-scope, fixed-price three-week engagement: an inventory of what is already running, an evidence pack mapped to NIST AI RMF and ISO/IEC 42001, and one governed agent live on a real workflow. For the control set in written form, the Enterprise AI Agent Blueprint is a free governance guide. You can also book a discovery call or read the enterprise consulting overview.