部署

LLMOps

定义

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.

相关术语

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.

了解术语只是第一步，将其落地应用才是第二步。

预约一次探索通话，探讨这些 AI 概念如何转化到您所在的具体行业与业务挑战中。

LLMOps

定义

相关术语

MLOps

Prompt Cache

Token Budget

相关服务

了解术语只是第一步，将其落地应用才是第二步。

LLMOps

定义

相关术语

MLOps

Prompt Cache

Token Budget

相关服务

了解术语只是第一步，将其落地应用才是第二步。