MLOps vs. LLMOps Architecture
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| TRADITIONAL MLOPS |
| Data Prep -> Train XGBoost/CNN -> Validate F1/RMSE -> Deploy Model Weights |
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VS.
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| LLMOPS |
| Foundation Models (Pre-trained) -> Prompt + RAG + Guardrails -> Evals -> API |
| Continuous Feedback -> Semantic Caching -> Traceability & Token Cost Control |
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How LLMOps Diverges from Traditional MLOps
While traditional Machine Learning Operations (MLOps) centered around feature stores, model training runs, and monitoring scalar metrics like accuracy or ROC-AUC, LLMOps introduces fundamentally distinct engineering concerns:
| Dimension | Traditional MLOps | LLMOps |
|---|---|---|
| Model Creation | Training models from scratch on tabular/image datasets. | Adopting massive Foundation Models via API or open weights. |
| Primary Artifact | Serialized model file (model.pkl, model.onnx). |
Prompt templates, RAG vector indexes, and system guardrails. |
| Evaluation | Deterministic metrics (Precision, Recall, MSE). | Non-deterministic (LLM-as-a-Judge, semantic similarity, ROUGE/BLEU). |
| Cost Driver | Training compute (cloud GPU instances). | Ongoing inference token consumption and API billing. |
| Drift Detection | Covariate shift, statistical distribution changes. | Semantic drift, prompt injection exploits, hallucination rates. |
The Core Pillars of Modern LLMOps
- Prompt Management & Version Control: Tracking prompt versions alongside code in Git, preventing silent regression.
- Evaluation-Driven Development (EDD): Running automated test suites of 100+ prompt-response pairs before merging pull requests.
- Observability & Tracing: Capturing every intermediate LLM call, vector search latency, and token cost with tools like LangSmith or OpenTelemetry.
- Guardrails & Security: Scanning inputs and outputs for PII leaks, hate speech, and jailbreak attempts.
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