What Is LLMOps?

MLOps emerged to bring software engineering discipline to the machine learning lifecycle: reproducible training, versioned datasets, staged rollouts, and continuous monitoring. LLMOps applies those same principles to systems built on large language models — but the constraints, failure modes, and tooling differ in important ways.

What is different about LLMs in production

Traditional ML models are trained artifacts: you ship a binary and monitor prediction drift. LLM systems are also prompt-driven: the behavior of the system depends on the model, the prompt, the retrieval context, and the tools in combination. A change to any one of these can silently degrade quality without changing any code. LLMOps must version and monitor all of them.

• Prompts are code: they must be versioned, reviewed, and tested like source code
• Quality is hard to measure: LLM output is often open-ended, requiring semantic evaluation not just type checks
• Cost is variable: unlike a fixed compute cost, token spend depends on input length and usage patterns
• Model providers change models: the external model you depend on may change behavior between versions
• Latency varies widely: the same prompt can take 400ms one second and 3s the next under load

The LLMOps lifecycle

A typical LLM application lifecycle moves through: prompt development and versioning, evaluation and quality gates, staging and canary deployment, production observability, cost and latency monitoring, user feedback collection, and iterative improvement through dataset curation and prompt refinement. Each stage has dedicated tooling and practices covered in this track.