cost-aware-llm-pipeline

Reduce LLM API costs by routing batch requests to the most cost-effective models based on task complexity. The pipeline enforces live budget constraints and applies prompt caching to avoid resending large system prompts, minimizing overall spend without sacrificing output quality.

Model routing evaluates task complexity to direct simple prompts to cheaper models and complex prompts to advanced ones. This automatic routing preserves output quality while minimizing cost across batches or live requests.

You can enforce budget limits on live LLM API requests using immutable cost tracking. The pipeline records every call, computes total spend against a configurable budget, and applies guardrails to prevent overspending.

Handle transient failures securely and cost-effectively using narrow retry logic. The pipeline retries only specific transient failures, preventing excessive repeated calls and preserving reliability without exceeding your budget.

Prompt caching works across applications that call Claude, OpenAI, or similar providers. It avoids resending large system prompts for repeated requests, reducing token usage and optimizing API spend.

Avoid automated model routing when your application requires a specific model for every task regardless of complexity or cost. The pipeline is designed for workflows where tasks have varying complexity and can tolerate being routed to different models to minimize cost.