llm-cost-optimizer

Reduce LLM API spend by auditing token usage, implementing model routing for cheaper tiers on simple tasks, applying prompt caching, and setting max_tokens controls to cap over-generation. These optimizations lower costs while preserving response quality.

Auditing token usage requires per-request token and cost logging to generate feature-level spend breakdowns. This identifies the top cost drivers in AI products, enabling targeted cost optimization through prompt compression and model tier selection.

Set up model routing by selecting cheaper models for simpler tasks, and implement semantic caching to reuse responses for similar prompts. Combine these with batching and output length controls to significantly reduce LLM API costs.

Yes, per-request token and cost logging is required to perform an accurate cost audit. It provides the granular data needed to generate feature-level spend breakdowns and identify uncapped generation costs or model overspending.

You can add budget guardrails and max_tokens controls before launching new AI features to ensure cost-safe architecture. This involves setting output limits, applying caching strategies, and implementing model tier selection to prevent uncapped generation costs.

Rising LLM API spend often results from model overspending, uncapped generation costs, or uncontrolled token usage. Implementing cost observability and semantic caching eliminates unnecessary token usage and stabilizes API expenditures.