# LLMOps

> Operational practices and tooling for deploying, monitoring, and maintaining LLM applications in production, encompassing prompt management, model versioning, evaluation, and observability.

- **URL:** https://www.langchain.com/langsmith
- **Category:** Concepts & Definitions
- **Tags:** operations, mlops, production
- **Updated:** 2026-03-18 13:02
- **Canonical page:** https://vectordb.ever.works/pt/items/llmops

## Details

## What is LLMOps?

LLMOps (Large Language Model Operations) refers to the practices, tools, and workflows for deploying, monitoring, and maintaining LLM-powered applications in production environments. It extends MLOps principles to the unique challenges of language models.

## Core Components

**Prompt Management**:
- Version control for prompts
- A/B testing different prompt variations
- Prompt templates and composition
- Multi-language prompt support

**Model Management**:
- Model versioning and rollback
- Multi-model routing and fallbacks
- Cost optimization across providers
- Performance monitoring

**Evaluation & Testing**:
- Automated evaluation pipelines
- Regression testing for prompts
- Human-in-the-loop review
- Quality metrics tracking

**Observability**:
- Request tracing and logging
- Latency and token usage monitoring
- Error tracking and alerting
- Cost attribution

## Key Challenges

- **Non-determinism**: LLM outputs vary even with same inputs
- **Evaluation Difficulty**: Subjective quality assessment
- **Cost Management**: Token usage can be expensive
- **Prompt Engineering**: Iterative optimization needed
- **Safety & Compliance**: Content moderation and data privacy

## LLMOps Platforms

- LangSmith (LangChain)
- Phoenix (Arize AI)
- TruLens
- Promptfoo
- LiteLLM Proxy
- Helicone
- Weights & Biases for LLMs

## Best Practices

1. Implement comprehensive logging and tracing
2. Set up automated evaluation pipelines
3. Monitor costs and set budgets
4. Version prompts alongside code
5. Establish human review workflows
6. Build fallback strategies
7. Implement caching where appropriate
8. Track user feedback systematically

## Differences from MLOps

- More focus on prompt engineering vs model training
- Different evaluation metrics (coherence, relevance vs accuracy)
- API-based deployment vs serving infrastructure
- Token-based pricing considerations
- Greater emphasis on output validation

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