What is LLMOps / MLOps?
LLMOps is the set of practices to deploy, monitor and maintain language models in production reliably.
What it covers
LLMOps adapts the disciplines of MLOps —the operational lifecycle of machine learning models— to language models, and adds what is specific to language: prompt and version management, quality evaluation, control of cost and latency, and monitoring of how the system responds with real data. It is the difference between launching a model and operating it with guarantees.
Why it matters
Most AI systems fail not when they are built, but when they have to be sustained: data changes, models are updated and quality degrades silently. LLMOps provides the observability and traceability needed to detect and correct that drift, and to keep AI in production reliable.
How we approach it at Codara
We build each Agentic OS on Codara's own platform with LLMOps practices integrated from the start —monitoring, evaluation and traceability— so your team can operate the system with guarantees when we hand it over.
Preguntas frecuentes
How does it differ from MLOps?
MLOps covers the lifecycle of machine learning models in general; LLMOps adapts those practices to language models, adding prompt management, quality evaluation and control of cost and latency.
Why is it key for production?
Because a model that works in a demo can degrade with real data; LLMOps provides deployment, monitoring, evaluation and traceability so it stays reliable over time.