LLMOps (Large Language Model Operations) is the set of practices, tools, and infrastructure patterns for developing, deploying, monitoring, and maintaining applications built on large language models. It extends MLOps concepts to address the unique operational challenges of LLM-based systems, including prompt management, context window optimization, cost control, and evaluation of non-deterministic outputs.

How LLMOps Differs from MLOps

Traditional MLOps focuses on training pipelines, feature stores, model versioning, and performance metrics like accuracy and F1 score. LLMOps introduces different concerns. Most organizations use pre-trained models rather than training from scratch, so the focus shifts to prompt engineering, fine-tuning, and retrieval augmentation. Evaluation is harder because outputs are free-form text rather than numerical predictions. Costs scale with token consumption rather than compute time alone. And the attack surface includes prompt injection and jailbreaking, which do not exist in traditional ML.

Key Components

Prompt management - Version control for prompts, A/B testing of prompt variants, and template management systems. Evaluation and testing - Automated evaluation pipelines using LLM-as-judge, human evaluation workflows, and regression testing against golden datasets. Cost monitoring - Token usage tracking, model routing to optimize cost-performance tradeoffs, and caching strategies to reduce redundant API calls. Observability - Logging of inputs, outputs, latency, and token usage for every request, with tracing through multi-step chains and agent workflows. Guardrails - Input and output validation, content filtering, and policy enforcement applied consistently across all LLM interactions.

Tooling Landscape

The LLMOps ecosystem includes platforms like LangSmith, Weights & Biases Weave, Arize Phoenix, and Humanloop for evaluation and monitoring. AI gateways like LiteLLM and Portkey handle routing and cost management. Vector databases support RAG workflows. And orchestration frameworks like LangChain and LlamaIndex provide abstractions for building LLM applications.

Organizational Considerations

LLMOps requires collaboration between ML engineers, platform engineers, and application developers. Organizations typically establish a shared LLM platform that provides governed access to models, standardized evaluation frameworks, and cost allocation mechanisms, allowing product teams to build applications without each team solving operational challenges independently.

Sources

  • Sculley, D., et al. (2015). Hidden technical debt in machine learning systems. NeurIPS 2015. (Foundational ML engineering paper; the operational challenges LLMOps inherits from MLOps.)
  • Liang, P., et al. (2022). Holistic evaluation of language models (HELM). arXiv:2211.09110. (HELM; systematic LLM evaluation framework covering accuracy, robustness, fairness, and efficiency — what LLMOps evaluation pipelines measure.)
  • Shankar, S., et al. (2022). Operationalizing machine learning: An interview study. arXiv:2209.09125. (Empirical study of ML production challenges; many findings directly apply to LLM operational practices.)