cost-latency-optimizer

What problem does it solve?

Reduce the cost and latency of large language model workloads by intelligently caching results, selecting cheaper models when appropriate, batching requests, and optimizing prompts to minimize token usage.

Core Features & Use Cases

• Cost breakdown analysis and visibility into LLM expenditures, enabling data-driven optimization.
• Caching strategy that stores repeated prompts and responses to reduce token usage and response times.
• Model selection logic to swap cheaper models for simple queries while reserving capable models for complex tasks.
• Batched execution and parallelization to improve throughput and lower overall latency.
• Prompt optimization techniques to shrink token count without sacrificing result quality.
• Latency hotspot analysis and streaming options to shorten time-to-first-byte and end-to-end latency.
• Real-world use cases include enterprise chat assistants, code generation, and document processing pipelines.

Quick Start

Configure and run the optimizer on your LLM workflow to reduce costs and improve latency.