Business / Cost

企业大模型项目的成本结构Cost Structure of Enterprise LLM Projects

大模型项目的成本不只有 token。真正需要预算的是模型调用、系统集成、数据治理、评测、运维和持续优化。LLM project cost is not just tokens. Budget for model calls, integration, data governance, evaluation, operations and ongoing optimization.

CostPlanningPilot

模型调用成本

根据 Z.AI 价格页，GLM-5.3 当前价格为输入 $1.4 / 1M tokens、缓存输入 $0.26 / 1M tokens、输出 $4.4 / 1M tokens。企业评估时应该按场景估算平均输入、平均输出、日调用量和缓存命中率。

$1.4输入 / 1M tokens
$0.26缓存输入 / 1M tokens
$4.4输出 / 1M tokens

集成成本

如果只是网页聊天，集成成本很低；如果要接入 CRM、订单、客服和权限系统，成本主要来自接口梳理、权限设计、日志审计和业务流程改造。

数据治理成本

知识库问答或 Agent 项目需要处理文档格式、版本、权限、脱敏和更新频率。数据越干净，模型输出越稳定。

持续优化成本

  • 维护 Prompt 模板和工具调用规则。
  • 建立人工反馈和评测样本集。
  • 监控 token 消耗、响应延迟和错误率。
  • 根据业务变化更新知识库和工作流。
预算建议：先按一个高频场景做月度成本估算，而不是按整个公司一次性估算。

Model usage cost

According to Z.AI pricing, GLM-5.3 is currently listed at $1.4 / 1M input tokens, $0.26 / 1M cached input tokens and $4.4 / 1M output tokens. Estimate average input, output, daily calls and cache hit rate by scenario.

$1.4input / 1M tokens
$0.26cached input / 1M tokens
$4.4output / 1M tokens

Integration cost

A simple website chat has low integration cost. Connecting CRM, orders, support and permission systems requires interface mapping, permission design, audit logs and process changes.

Data governance cost

Knowledge Q&A and agent projects require document formatting, versioning, permissions, redaction and update cadence. Cleaner data produces more stable model outputs.

Ongoing optimization cost

  • Maintain prompt templates and tool-call rules.
  • Build feedback loops and evaluation samples.
  • Monitor token usage, latency and error rate.
  • Update knowledge bases and workflows as the business changes.
Budgeting tip: estimate monthly cost for one high-frequency workflow before estimating the whole company.

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资料来源：Source: Z.AI Pricing.