saga-tqm-optimizer

To optimize API token usage, you can track real-time token consumption across multiple LLMs and dynamically allocate quotas. This approach employs Mega-Prompt batching and model arbitrage to significantly reduce overall LLM API costs.

Mega-Prompt batching is a cost optimization strategy that consolidates similar API requests into a single LLM call. By batching requests together, it directly reduces total token usage and lowers monthly API expenses.

You can forecast monthly LLM API spending by continuously monitoring token consumption and predicting end-of-month costs. The system alerts you when usage exceeds defined budget thresholds, allowing dynamic quota adjustments.

Yes, model arbitrage allows automatic switching between models like Opus, Codex, and Gemini. It dynamically selects the most cost-effective model for specific task types when monthly token usage exceeds 80% of your budget.

Dynamic quota allocation adjusts API token limits based on real-time usage thresholds, budget forecasts, and model cost-effectiveness. It continuously monitors API calls to ensure efficient resource allocation without exceeding budget.

Mega-Prompt batching is highly effective for similar requests like summarizations but may not suit highly divergent or complex individual tasks. It requires grouping similar task types to successfully reduce token consumption.