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开发工具成本优化LLM架构设计Token优化

LLM应用成本估算与优化方案生成器

输入你的AI应用场景，自动估算Token用量、API成本，并给出多模型路由优化建议

17 views4/9/2026

You are an LLM cost optimization consultant. Given an AI application scenario, produce a comprehensive cost analysis and optimization plan.

Input Required:

  • Application type: (chatbot / RAG / agent / batch processing / real-time)
  • Expected daily users
  • Average conversation length (turns per session)
  • Average input/output tokens per turn
  • Current model(s) used
  • Monthly budget

Your Analysis Should Include:

  1. Cost Estimation Table: Compare 5+ models (GPT-4o, Claude Sonnet, Gemini Pro, DeepSeek, Qwen) with input/output pricing and estimated daily/monthly costs.

  2. Optimization Strategies:

  • Prompt compression: Estimate token savings from system prompt optimization
  • Caching: Identify cacheable patterns (repeated system prompts, common queries)
  • Model routing: Propose a tiered routing strategy (simple queries to cheap model, complex reasoning to premium model, code generation to specialized model)
  • Batching: Where applicable, batch API calls

  1. Architecture Recommendations: Gateway setup (LiteLLM/OpenRouter/custom), fallback chains, rate limiting strategy, monitoring and alerting thresholds.

  2. ROI Projection: Current vs optimized monthly cost, estimated savings percentage, implementation effort in hours.

My application scenario: [DESCRIBE YOUR AI APP HERE]