LLMOps is not the same as MLOps. I learned this the hard way.

Traditional MLOps: you train a model, version it, deploy it, monitor its performance. The model is a static artifact that you replace with a better version periodically.

LLMOps: the "model" is an API you don't control. Your system behavior depends on prompts that are sensitive to tiny changes. The provider can update the model under you. Your costs are proportional to usage, not infrastructure. Evaluation is subjective and hard to automate.

New challenges specific to LLMs: prompt management and versioning, cost monitoring and optimization per query, quality evaluation at scale, handling provider outages and model updates, managing context window limits, caching and rate limiting.

The tools are still maturing. LangSmith, Helicone, and Portkey handle some of these. But many teams end up building custom solutions for their specific needs.

The biggest operational surprise for teams deploying LLMs: cost volatility. A prompt change can 3x your token usage. A viral feature can 10x your API costs overnight. Without proactive monitoring and limits, budgets explode.

If you're deploying LLM applications, build operational infrastructure from day one: cost dashboards, quality monitoring, and rate limiting at minimum. Don't wait until the first shocking invoice.

LLMOps is its own discipline. Treat it that way.