Quick Answer: MLOps manages the lifecycle of models you train, while LLMOps manages LLM applications, adding prompt versioning, output evaluation, guardrails and token-cost control. Google Cloud files LLMOps as a subset of MLOps, so CI/CD, versioning and monitoring still apply. What moves is the work: from training pipelines to prompts, retrieval and model choice.
The LLMOps vs MLOps question is really about what changes when you run LLMs in production, and it arrives once your first LLM feature ships. A fraud model has a training pipeline and a drift alert; a support assistant has a prompt, a retrieval index, a model you don't own and a token bill.
This guide is for engineering leads deciding what to keep from MLOps, what to add, and who owns it.
What is the difference between LLMOps and MLOps?
MLOps operates models you train on your own data. LLMOps operates applications built on a large language model you usually call rather than train, so the versioned artifacts become prompts, retrieval indexes, tool configs and eval sets, not only model weights.
A fraud-detection model is classic MLOps: you own the features, retrain on fresh transactions and watch precision, recall and drift. A customer-support chatbot is LLMOps: most releases change a prompt, a retrieval source or a tool, not the weights.
| Dimension | MLOps | LLMOps |
|---|---|---|
| Main artifact | Trained model plus feature pipeline | Prompt, retrieval index, tool config and the chosen model |
| Training | Train and retrain on your labeled data | Mostly none; optional fine-tuning of a foundation model |
| Evaluation | Accuracy, precision, recall, AUC on a holdout set | Groundedness, relevance and safety, scored by rubric, LLM judge or human review |
| Monitoring signals | Data drift, prediction drift, latency | Hallucinations, retrieval misses, refusals, latency, tokens per request |
| Cost driver | Training compute and serving infrastructure | Tokens per request times traffic, plus GPU serving if you self-host |
| Security focus | Data access and model artifact integrity | Prompt injection, data leakage through outputs, tool permissions |
| Typical tools (categories) | Feature store, experiment tracker, model registry, pipeline orchestrator | Prompt registry, eval harness, tracing, LLM gateway, vector database, guardrails |
Databricks' explainer, a vendor view, lists similar shifts: prompt templates, LLM chains that call vector databases, and human feedback.
What is LLMOps?
LLMOps, short for large language model operations, is the practice of shipping, evaluating, securing and running applications built on LLMs in production. It covers the whole system around the model: prompts, retrieval, tool calls, evaluation, guardrails, routing and spend.
Google Cloud's explainer, another vendor view, describes LLMOps as a specialized subset of MLOps that deals with the large size, complex training requirements and high computational demands of LLMs. So LLMOps extends MLOps rather than replacing it.
Ask "what is LLMOps?" in a planning meeting and you'll hear three scopes: model serving and fine-tuning, prompts and release gates, and the shared gateway with its keys and budgets. A working definition includes all three.
It isn't AIOps, which applies machine learning to IT operations data such as alerts and logs.
Which MLOps practices carry over to LLM apps?
Most of the discipline carries over: version everything, test before release, deploy through CI/CD, monitor production and keep rollbacks cheap. What changes is what you version and test.
Google's MLOps guide runs from manual process to full CI/CD pipeline automation and notes that only a small fraction of a real-world ML system is ML code.
- Versioning. Model versions become prompt, retrieval-index and model-ID versions, each tied to a release.
- CI/CD. A prompt change goes through the same pipeline as a code change, with an eval step as the test gate.
- Reproducibility. Log the prompt, model ID, parameters and retrieved context for any request you may need to replay.
- Monitoring and access control. Latency, error rates and least-privilege access still apply; LLM signals sit on top.
Continuous training is what shrinks. Unless you fine-tune, there's no retraining loop.
What does LLMOps add for prompts, evaluation and cost?
LLMOps adds four things MLOps never managed: prompts and retrieval as release artifacts, evaluation of open-ended text, guardrails against manipulated inputs, and per-request token spend with model choice as the lever.
Prompts and retrieval as versioned artifacts
A prompt is code that runs on someone else's model. Store it in git or a registry, review changes and pin each release to a model ID. Treat chunking settings, embedding models and indexed documents the same way, since a retrieval change can shift answers as much as a new model.
The development loop becomes: change a prompt, retrieval setting or tool, run the eval set, compare with the last release, then ship.
Evaluation sets and LLM judges
Accuracy on a holdout set doesn't describe generated text. Teams build an eval set of real questions with reference answers and score groundedness, relevance, format and safety, mixing rule checks, an LLM judge for scale and human review for release-deciding cases.
Guardrails and prompt injection
The OWASP Top 10 for LLM Applications ranks prompt injection as LLM01, including instructions hidden in retrieved files or web pages. Its mitigations include least-privilege tool access and human approval for high-risk actions.
Token cost and model choice
Cost scales with tokens per request times traffic, so long system prompts and oversized retrieval context land directly on the bill. Track tokens per feature and team, alert on spikes, and route simple tasks to smaller models.
Where does model routing fit in LLMOps?
Model routing sits in a gateway between your applications and every model provider. It puts the LLMOps controls in one place: which model serves which request, who holds which key, what each team may spend and what gets logged.
An LLM gateway typically handles:
- Routing and fallback. Small models for simple tasks, larger ones for reasoning, failover on provider errors.
- Keys and quotas. Scoped keys per team or service, with rate limits and budgets.
- Logging. Prompt, response, model ID and token counts for replay, evals and audits.
- Policy. PII redaction and guardrails applied once, not in every service.
For traces, OpenTelemetry maintains semantic conventions for generative AI covering spans, metrics and events, which keeps LLM telemetry in your existing observability stack.
Who should own LLMOps in an engineering org?
A platform team should own the shared LLMOps layer, and each product team should own its prompts, eval sets and release decisions. That keeps budgets consistent without a central bottleneck on prompt changes.
A simple RACI-style split:
- Platform team (accountable for uptime and spend visibility): gateway, keys, quotas, tracing, eval tooling and the approved-model list.
- Product team (accountable for answer quality): prompts, retrieval sources, eval sets and release go/no-go.
- Security (consulted): guardrail policy, data classification and tool permissions.
- Finance or FinOps (informed): team budgets and alert thresholds.
AI-first companies weighing DevOps consulting services should check that the partner covers LLMOps, not just classic MLOps: ask how they version prompts, build eval sets and cap token spend.
What mistakes should you avoid when operating LLM features?
The costly mistakes come from MLOps habits that never carried over: unversioned prompts, releases without an eval set, spend without alerts and one model hard-coded everywhere.
- Treating prompts as config. An unreviewed prompt edit is an unlogged production deploy.
- Shipping without an eval set. Without fixed test questions you can't tell whether a change helped or hurt.
- No cost alerts. A retrieval change that triples context length triples the bill; alert per feature.
- One model hard-coded. Call a gateway, so a deprecation or outage doesn't mean code changes everywhere.
- Watching only the model. Most failures come from retrieval, tools or prompts, so trace the whole request.
How Origins AI runs LLMOps for product teams
Origins AI (originshq.com) is a US-based AI-augmented engineering company that builds custom AI workflows and agents for product teams and deploys self-hosted enterprise AI. Its DevOps consulting services cover CI/CD and automation, containerization and orchestration, monitoring and DevSecOps, and its service pages name MLOps pipelines and Kubernetes among its tools. Its data services page lists both predictive models and LLM prompt engineering.
For the routing layer, the Origins AI Coding Tool includes a self-hosted LLM gateway. According to its product page, the gateway exposes an OpenAI-compatible API, routes requests by task type, cost or team policy, logs every request and token count, and enforces per-team quotas. Secrets and PII are filtered before content reaches a model.
The gateway runs on-premise, in your own AWS, Azure or GCP account, air-gapped with local models, or in hybrid mode (local gateway, hosted model), where the code context submitted to the model leaves your network.
Where a team fine-tunes, the Origins AI Domain-Specific LLMs page ends its four-stage process with integration and iteration: deploy into workflows, then refine based on user feedback. Origins AI's Iterative AI Delivery model describes the same release loop, launching to pilot users, setting success metrics and gathering feedback. Engagement models are dedicated teams, project-based work, time-and-materials or build-operate-transfer.
Talk to an engineer
Planning to move an LLM feature from prototype to a monitored, budgeted service? Book a call with an Origins AI engineer to walk through your prompts, eval set and routing setup.
Written by Apoorva Kumar, Co-Founder & CEO, Origins AI.