cost-latency-optimizer

Reduce LLM API costs and latency by implementing caching to reuse repeated outputs, routing simple prompts to cheaper models, and batching requests to improve throughput. This approach identifies latency hotspots and provides actionable cost breakdowns for chatbots and data pipelines.

The best way to optimize LLM prompts for lower token usage is applying prompt optimization techniques that reduce token counts without sacrificing quality. This is combined with model selection logic to route simpler, optimized prompts to cheaper models like GPT-3.5-turbo.

Yes, you can use caching to reuse repeated LLM outputs and embeddings in data processing pipelines. Implementing a caching layer prevents redundant API calls, significantly reducing both operational costs and response latency for frequent, repeated prompts.

Identify latency hotspots in your LLM application by performing latency tracking and hotspot analysis. This pinpoints expensive operations in your workflow, enabling targeted improvements like batching, parallelization, and streaming to improve Time To First Byte (TTFB).

Model selection logic works by routing simple prompts to cheaper LLMs like GPT-3.5-turbo while reserving expensive models for complex queries. This strategy is highly effective for cost tracking and optimization across multilingual assistants and document QA workflows.