LLM Cost Optimizer

advanceddevelopmentMin 32K context

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

Use Cases

  • 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

Example 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

Recommended Models

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

Compatible Tools

claude-codekirocursorany

Modalities

Input: text, code
→
Output: text

Related Skills

prompt-engineeringmodel-router-designerrag-pipeline-builder

Author

OpenModels Community

@openmodelsrun