LLMOps vs MLOps: What Changes for LLM Applications?

MLOps matured around models that organizations train themselves. Large language model applications changed the shape of the problem: the model is usually an external dependency, the artifacts you control are prompts, retrieval indexes, tool integrations, and evaluation sets, and the dominant risks are cost, safety, and provider change. LLMOps is the adaptation of operational discipline to that shape. This comparison covers what changes and what carries over, drawing on FISTA Solutions' AI enablement practice. The DevOps foundation is in devops vs mlops and the practice overview in what is mlops.

What is MLOps?

MLOps covers the lifecycle of trained models: versioning data and features, tracking experiments, training and evaluating candidates, registering approved versions, deploying them for inference, monitoring drift and quality decay, and retraining. Its central artifact is a model produced from your data, and its central risks are data drift, reproducibility, and silent quality decay.

What is LLMOps?

LLMOps covers the lifecycle of applications built on large language models: managing prompts and context strategies, retrieval indexes and their freshness, tool and agent definitions, model and provider selection and versions, evaluation sets and judges, gateways for routing and cost control, tracing and observability, safety controls, and response to provider changes. Its central artifacts are prompts, configurations, and evaluation sets, and its central risks are cost, latency, safety, hallucination, and provider updates. Foundations of the underlying systems are in what is rag and what is agentic ai.

How do they compare?

Dimension	MLOps	LLMOps
Model origin	Trained internally	Usually external provider; sometimes fine-tuned
Primary artifacts	Datasets, features, model weights	Prompts, retrieval indexes, tool definitions, configs
Versioned unit	Model version	Application version: prompt + retrieval + tools + model version
Evaluation	Metrics on held-out data	Task success, groundedness, safety via golden sets, judges, humans
Deployment	Serve model artifact	Deploy application config; route through gateway
Monitoring	Drift, quality decay	Plus cost, tokens, latency, safety events, tool errors
Change triggers	Data drift, retraining	Prompt edits, index refresh, provider updates, cost pressure
Key infrastructure	Feature store, registry, serving	Gateway, prompt management, vector store, tracing
Dominant risks	Reproducibility, drift	Cost, safety, hallucination, provider change

What changes in versioning and registries?

The unit of release becomes the application configuration: the prompt versions, retrieval configuration and index version, tool definitions, model and provider version, and parameters, bundled with the evaluation results that approved them. A registry of these versions enables tracing any production output to its exact configuration and rolling back cleanly. Fine-tuned models add traditional model registry needs on top. Prompt infrastructure is in how to build a prompt management system.

What changes in evaluation?

Golden datasets of representative inputs with expected outcomes, scored by rubric-based LLM judges calibrated against human ratings and by human review for high-stakes cases, measuring task success, groundedness, safety, and format compliance. Evaluation runs in CI on every prompt, retrieval, or model change and against production samples continuously. Harness design is in how to build an agent evaluation harness and judge design in what is llm-as-a-judge.

What changes in deployment?

Applications route model calls through a gateway that handles provider selection, fallbacks, rate limits, caching, and cost attribution, so model changes do not require application changes. Releases deploy configuration versions with canary and shadow patterns comparing quality and cost. Gateway design is in how to build an llm gateway and routing in what is an llm router.

What changes in monitoring?

Tracing captures every step of a request: retrieval results, prompt rendered, model response, tool calls, and outcome. Dashboards track quality signals, cost per request and per feature, token usage, latency percentiles, safety events such as injection attempts, and tool error rates. Provider status and model deprecation notices are monitored as operational inputs. Monitoring design is in how to build a real-time ai monitoring system and cost visibility in how to build an ai cost dashboard.

Why is provider change a first-class risk?

External models are updated and retired on the provider's schedule. Behavior, cost, and latency can change with no change on your side. LLMOps treats this as expected: pinning versions where possible, running evaluation on new versions before migrating, keeping fallbacks configured, and tracking deprecation timelines. Multi-provider design is in what is a fallback model.

How do the disciplines share a platform?

Common CI, observability, registries, and evaluation runners serve both, with LLM-specific additions: gateway, prompt management, vector store, tracing, and cost dashboards. Organizations running both custom models and LLM applications should build one platform with two release paths rather than two platforms. Tooling is compared in mlflow vs weights and biases.

What does the practice look like in practice?

A company operating a support agent and a document pipeline versions application configurations in a registry, runs golden-set evaluations in CI on every change, routes all model calls through a gateway with fallbacks, traces every request, tracks cost per feature, and evaluates each provider model update before migrating. A machine learning team at the same company uses the same CI, observability, and registry for its forecasting models with a training-oriented release path. Roles are in hire llmops engineers and hire mlops engineers.

How FISTA Solutions operates LLM applications

FISTA Solutions builds LLMOps on clients' existing platforms: configuration registries, golden-set evaluation in CI, gateways with fallbacks and cost attribution, tracing and safety monitoring, and managed response to provider change, sharing infrastructure with MLOps where both exist. The AI enablement practice delivers the platform, AI agents are operated under it, and forward deployed engineers embed with client platform teams. The record behind the approach is 150+ projects with 99.9% uptime.

To set up operations for LLM applications, message FISTA on WhatsApp, or read ai evaluation vs ai monitoring for how the two feedback loops fit together.