Déploiement

LLMOps

Définition

An extension of MLOps focused on the operational challenges specific to large language models: prompt management, context handling, latency optimisation, cost control, hallucination monitoring, and compliance logging. LLMOps tooling is rapidly maturing in 2025–26.

Termes Connexes

MLOps

The practice of combining Machine Learning, DevOps, and data engineering to streamline the deployment, monitoring, and maintenance of ML models in production. MLOps ensures reliable, scalable, and reproducible ML systems across their entire lifecycle.

Prompt Cache

A technique that caches the key-value (KV) attention states of a repeated prompt prefix, so subsequent requests reuse the pre-computed computation rather than re-running it. Prompt caching reduces latency and cost significantly for applications with long system prompts.

Token Budget

The total number of tokens allocated for a model request, encompassing both input (prompt + context) and output. Managing token budgets is central to controlling inference cost in production LLM applications, especially when processing long documents or maintaining conversational history.

Services Connexes

Pilot To Production

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