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Quick Definition
LLMOps is the set of practices for operating large language models in production, covering prompt management, fine-tuning, evaluation, deployment, monitoring, and cost control. It extends MLOps to the specific challenges of LLMs, including token usage and GPU-intensive inference.
LLMOps is the set of practices for operating large language model applications: managing prompts and model versions, evaluating output quality, monitoring latency and failures, and controlling the very particular economics of token-based pricing. It extends MLOps to the realities of generative AI.
The cost dimension is unusually sharp. LLM usage is billed per token, tiny units of text, at rates that vary enormously by model. Costs scale with prompt length, response length, and call volume, so a verbose prompt template or an unnecessary retry multiplies across millions of requests. Routing simple queries to small models and reserving frontier models for hard cases is often the single biggest saving available.
Example. A support assistant sends every query to a premium model. Analysis shows 70 percent are simple questions a model one tenth the price answers equally well. Adding a router cuts inference spend by more than half with no quality drop users notice.
Token budgets, per-feature cost tracking, and caching are core practice. Start with the token budgeting guide, FinOps for AI, and the AI Cost Optimization Guide.
MLOps centers on training and deploying your own models. LLMOps centers on prompts, evaluation, and token economics, often around third-party models.
Model choice first, then prompt and response length, then call volume. Routing and caching attack all three.
Track tokens and cost per feature and per customer, alert on anomalies, and review model routing decisions against quality metrics.