LLM Cost Optimizer

AvanzadadevelopmentContexto mínimo: 32K

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

Casos de uso

  • 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

Prompt de ejemplo

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

Modelos recomendados

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

Herramientas compatibles

claude-codekirocursorany

Modalidades

Entrada: text, code
→
Salida: text

Skills relacionadas

prompt-engineeringmodel-router-designerrag-pipeline-builder

Autor

OpenModels Community

@openmodelsrun