LLMOps

LLMOps is an adaptation of MLOps principles for the specific challenges of building and operating applications powered by large language models. While traditional MLOps focuses on training, deploying, and monitoring custom models, LLMOps addresses the unique operational concerns of working with LLMs. Key LLMOps concerns include prompt management (versioning, testing, and deploying system prompts and prompt templates), evaluation (measuring output quality for tasks that lack simple metrics), cost management (tracking and optimising token usage across models), latency optimisation (managing response times for user-facing applications), and safety monitoring (detecting harmful outputs, prompt injection attempts, and data leakage). LLMOps tools and platforms have emerged to address these needs, including LangSmith, Helicone, Braintrust, Promptfoo, and others. These complement traditional infrastructure tools with LLM-specific capabilities like trace visualisation, prompt playground environments, and automated evaluation frameworks.

As organisations move from LLM experiments to production applications, operational challenges multiply. Without LLMOps practices, teams struggle with inconsistent prompt quality, uncontrolled costs, undetected quality regressions, and difficult debugging when things go wrong. LLMOps provides structure and visibility. Prompt versioning ensures that changes to system prompts are tracked and can be rolled back. Evaluation pipelines catch quality issues before they reach users. Cost monitoring prevents unexpected spending spikes. Trace logging enables debugging of complex multi-step AI workflows. The maturity of an organisation's LLMOps practices directly correlates with the reliability and cost-effectiveness of their AI applications. Investing in LLMOps early — even before scaling — prevents technical debt that becomes increasingly expensive to address later.