LLM Cost Optimizer

İleridevelopmentEn az 32K bağlam

Analyzes LLM usage and reduces inference cost without sacrificing quality. Covers prompt compression, context trimming, caching (prompt and semantic), model routing by task difficulty, batching, structured output to cut retries, and token accounting. Produces a concrete plan with estimated savings and quality guardrails.

llmcost-optimizationpromptcachingroutingtokens

Kullanım alanları

  • Cutting token spend on a high-volume LLM feature
  • Introducing prompt and semantic caching
  • Routing easy requests to cheaper models
  • Reducing retries with structured outputs
  • Building a token cost dashboard and budget alerts

Örnek prompt

Here is our current LLM pipeline and monthly token usage: [paste details].
Produce a cost-optimization plan that:
1. Identifies the biggest cost drivers
2. Recommends prompt/context reductions with examples
3. Proposes a caching strategy (prompt + semantic)
4. Designs a model-routing policy by task difficulty
5. Estimates savings and defines quality guardrails

Önerilen modeller

claude-opus-4-7gpt-5-5-progemini-3-1-prodeepseek-v4

Uyumlu araçlar

claude-codekirocursorany

Modaliteler

Giriş: text, code
→
Çıkış: text

İlgili Skills

prompt-engineeringmodel-router-designerrag-pipeline-builder

Yazar

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