LLMOps (Large Language Model Operations) is the discipline of building, deploying, and maintaining LLM-powered applications in production. It extends the principles of MLOps — which focused on traditional machine learning models — to address the unique challenges of working with large language models, where behavior is primarily controlled through prompts rather than training data.
The core pillars of LLMOps include prompt management, evaluation, monitoring, cost optimization, and reliability engineering. Prompt management provides version control, collaboration, and deployment workflows for the prompts that drive model behavior. Evaluation ensures output quality through automated testing, model-based scoring, and human review. Monitoring tracks production performance, detecting regressions, anomalies, and cost spikes in real time.
LLMOps differs from traditional MLOps in several important ways. In MLOps, the primary artifact is a trained model; in LLMOps, the primary artifact is often a prompt combined with a foundation model accessed via API. Model updates happen upstream at the provider level, outside the team's control, making prompt robustness across model versions a key concern. The non-deterministic nature of LLM outputs means that testing requires statistical approaches rather than deterministic assertions.
Cost management is a distinctive LLMOps challenge. Token-based pricing means that prompt length, output length, and request volume all directly impact costs. Teams need visibility into per-prompt and per-feature token consumption, and tools to optimize prompts for cost efficiency without sacrificing quality.
A mature LLMOps practice typically includes a prompt management platform for authoring and deployment, an evaluation framework for quality assurance, observability tooling for production monitoring, incident response procedures for prompt-related outages, and cost dashboards for budget management.
The LLMOps ecosystem is still maturing rapidly. Teams building LLM applications today often assemble their toolchain from multiple point solutions, though integrated platforms are emerging that cover the full lifecycle from prompt authoring to production monitoring.